JP2005192158A - Image processing method, image processing apparatus, and image recording apparatus - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an image processing method for applying gray balance adjustment under a more optimized condition by each photographing scene, and to provide an image processing apparatus and an image recording apparatus employing the method. <P>SOLUTION: The image recording apparatus 1 disclosed herein acquires the hue value and the saturation value of input image data, generates a two-dimensional histogram denoting the accumulated frequency distribution of pixels within a coordinate plane wherein an X-axis is the hue value (H) and a Y-axis is the saturation value (S), divides the two-dimensional histogram into prescribed hue regions, calculates a share (occupancy rate) of pixels of each region in the entire input image data by each divided hue region, calculates a low saturation threshold value by each hue region on the basis of the calculated occupancy rate, and extracts low saturation pixels used for calculating the gray balance adjustment condition on the basis of the low saturation threshold value. Then the image recording apparatus 1 calculates the gray balance adjustment condition by using the extracted low saturation threshold value and applies gray balance adjustment to the input image data under the calculated condition. <P>COPYRIGHT: (C)2005,JPO&NCIPI

Description

  The present invention relates to an image processing method, an image processing apparatus, and an image recording apparatus that perform image processing on captured image data and output image data optimized for viewing on an output medium.

  Today, scanning images of color photographic film and digital image data taken with an imaging device are distributed via storage devices such as CD-Rs, floppy disks, and memory cards, and the Internet, and CRT (Cathode Ray Tube ), Displayed on a display device such as a liquid crystal display or plasma display or a small liquid crystal display monitor of a mobile phone, or printed as a hard copy image using an output device such as a digital printer, an ink jet printer, or a thermal printer.・ Printing methods are diversified.

In response to these various display / printing methods, efforts have been made to increase the versatility of digital image data. As part of this effort, there is an attempt to standardize the color space represented by the digital RGB signal into a color space that does not depend on the characteristics of the imaging device. Currently, "sRGB" is adopted as the standardized color space for many digital image data. (See "Multimedia Systems and Equipment-Colour Measurment and Management-Part2-1: Color Management-Default RGB Color Space-sRGB" IEC "61966-2-1.) The sRGB color space is that of a standard CRT display monitor. It is set corresponding to the color reproduction area.

  In general, a scanner or a digital camera uses an image sensor (photoelectric conversion function) that combines a CCD (charge coupled device), a charge transfer mechanism, and a checkered color filter to provide color sensitivity. CCD type image sensor, hereinafter simply referred to as “CCD”). Digital image data output by a scanner or digital camera is converted into an electrical original signal converted via a CCD by correcting the photoelectric conversion function of the image sensor (for example, tone correction, spectral sensitivity crosstalk correction, darkness correction). File conversion to a specified data format that has been standardized so that it can be read and displayed by image editing software with current noise suppression, sharpening, white balance adjustment, saturation adjustment, etc. It has undergone compression processing and the like.

  As such a data format, for example, “Baseline Tiff Rev.6.0RGB Full Color Image” adopted as an uncompressed file of an Exif (Exchangeable Image File Format) file and a compressed data file format compliant with the JPEG format are known. It has been. The Exif file conforms to the sRGB, and correction of the photoelectric conversion function of the image sensor is set so as to obtain the most suitable image quality on a display monitor conforming to sRGB.

  For example, in any digital camera, tag information indicating that display is performed in a standard color space of a display monitor compliant with the sRGB signal (hereinafter also referred to as “monitor profile”), the number of pixels, the pixel arrangement, and The digital image data can be displayed on a display monitor as long as it adopts a function for writing additional information indicating model-dependent information such as the number of bits per pixel as metadata in the file header of the digital image data and such a data format. The image editing software (for example, Adobe Photoshop) that displays the tag information can analyze the tag information, prompt the monitor profile to be changed to sRGB, or perform the change process automatically. Therefore, it is possible to reduce the difference between different displays and to view digital image data captured by a digital camera in a suitable state on the display.

  Further, as additional information written in the file header of digital image data, in addition to the above-mentioned model-dependent information, for example, information directly related to the camera type (model) such as camera name and code number, exposure time, shutter Speed, aperture value (F number), ISO sensitivity, brightness value, subject distance range, light source, presence or absence of strobe light, subject area, white balance, zoom magnification, subject composition, shooting scene type, amount of reflected light from strobe light source, Tags (codes) indicating shooting condition settings such as shooting saturation and information on the type of subject are used, and image editing software and output devices read these additional information to improve the quality of hard copy images. A function to be suitable is provided.

  Also for color photographic films, products (APS films) provided with a magnetic recording layer have been developed to provide additional information as described above. However, contrary to the expectation of those skilled in the art, the spread in the market is slow, and the conventional products still occupy the majority. Therefore, image processing using additional information for the scanner read image cannot be expected for the time being. In addition, since the characteristics of color photographic films differ depending on the type, the initial digital minilabs prepared the optimum conditions for the respective characteristics in advance, but in recent years they have been abolished in most models for efficiency. Therefore, there is an increasing demand for a very advanced image processing technique that corrects the difference for each product type and automatically performs image quality improvement comparable to processing using additional information using only film density information.

  In particular, gray (white) balance adjustment for correcting a change in color temperature of the photographing light source is one of the items that are desirably corrected at the time of photographing or information for correction is obtained. In digital cameras, these corrections can be made at the time of shooting, but in principle it is impossible with color photographic film, and additional information cannot be expected as described above. It's a huge obstacle, and you're still forced to use a number of heuristic algorithms. Hereinafter, processing contents and problems of gray balance adjustment will be described.

For example, Patent Document 1 describes a method of extracting low-saturation pixels and linearly approximating the equivalent neutral density on the BGR density coordinates. In order to suppress color density deviation called color feria, a method is described in which an RC hue distribution is extracted for the BG correlation and a BY hue distribution pixel is extracted for the RG correlation as the low-saturation pixels. Further, the highlight point and the shadow point are calculated, and points below a certain saturation are extracted as low saturation pixels from the straight line connecting the two. In this case, the rule of thumb is that the highlight and shadow points are low in saturation, but the scenes with large areas occupied by the lawn and the sky, and the high brightness of the skin in the flash close-up scene, the darkness There are many examples that do not fit the rule of thumb, such as floating taillights and aquarium tanks. As a result, in these shooting scenes, a color density deviation called color feria occurs. As a countermeasure, there has been proposed a method of using information held by a plurality of photographing frames instead of calculating gray balance adjustment conditions for each frame. For example, in Patent Document 2, when low saturation pixels are extracted from all shooting frames, and the number of low saturation pixels extracted from each frame is small, gray balance adjustment conditions from all shooting frames and conditions from one frame are A method of weighting is proposed. In addition, Patent Document 3 proposes a method for performing a discrimination process between a normal scene and an abnormal scene. However, any of these methods mainly deals with addition or selection of information amount.
JP-A-9-191474 Japanese Patent Laid-Open No. 11-317880 JP 2001-257896 A

  As described above, the problem with the gray balance adjustment method is that the shooting scene has a special color arrangement tendency, such as extremely few low-saturation pixels extracted from one frame, or the highlight side or shadow side is not low-saturation. It can be said that this is a proposal for a coping method. Therefore, if the coloration tendency of the shooting scene is discriminated and the optimum gray balance adjustment condition is calculated for each shooting scene based on the discrimination result, there seems to be enough room for further accuracy improvement. . Many proposals have also been made regarding white balance adjustment methods for image data taken by a digital camera. However, all of them have proposed a threshold setting for extracting low-saturation pixels, or the application rate of gray balance adjustment itself, and the shooting scene. The relationship with the coloration tendency is not specified. Also, there is no known method for analyzing the coloration tendency of the photographic scene using a two-dimensional or higher histogram.

  An object of the present invention is to provide a novel image processing method capable of performing gray balance adjustment under conditions optimized for each shooting scene, and an image processing apparatus and an image recording apparatus using the same, in view of the above-described phenomenon. It is to be.

In order to solve the above-mentioned problem, the invention described in claim 1
In an image processing method for inputting captured image data and outputting image data optimized for viewing on an output medium,
Obtaining a hue value and a saturation value for each pixel of the captured image data;
Dividing the captured image data into at least two hue regions;
Calculating an occupancy ratio indicating a ratio of pixels for each of the divided hue regions to the entire screen of the captured image data;
Calculating a low saturation threshold value for each hue area according to the calculated occupancy ratio for each hue area;
Extracting low saturation pixels used for gray balance adjustment based on the calculated low saturation threshold;
Calculating a gray balance adjustment condition using the extracted low saturation pixel;
Applying gray balance adjustment to the captured image data based on the calculated gray balance adjustment condition;
It is characterized by including.

The invention described in claim 2
In an image processing method for inputting captured image data and outputting image data optimized for viewing on an output medium,
Obtaining a hue value and a saturation value for each pixel of the captured image data;
Dividing the captured image data into at least two hue regions;
Calculating an occupancy ratio indicating a ratio of pixels for each of the divided hue regions to the entire screen of the captured image data;
Calculating a gray balance adjustment application rate according to the calculated occupancy for each hue region;
Applying gray balance adjustment based on the calculated application rate;
It is characterized by including.

The invention according to claim 3
In an image processing method for inputting captured image data and outputting image data optimized for viewing on an output medium,
Obtaining a hue value and a saturation value for each pixel of the captured image data;
Dividing the captured image data into at least two hue regions;
Calculating an occupancy ratio indicating a ratio of pixels for each of the divided hue regions to the entire screen of the captured image data;
Calculating a low saturation threshold value for each hue area according to the calculated occupancy ratio for each hue area;
Extracting low saturation pixels used for gray balance adjustment based on the calculated low saturation threshold;
Calculating a gray balance adjustment condition using the extracted low saturation pixel;
Calculating a gray balance adjustment application rate according to the calculated occupancy for each hue region;
Applying gray balance adjustment to the captured image data based on the calculated gray balance adjustment condition and application rate;
It is characterized by including.

The invention according to claim 4 is the invention according to any one of claims 1 to 3,
Creating a two-dimensional histogram of the acquired hue and saturation values;
The step of dividing the captured image data into at least two hue regions is characterized in that the captured image data is divided into at least two hue regions based on the created two-dimensional histogram.

The invention described in claim 5
In an image processing method for inputting captured image data and outputting image data optimized for viewing on an output medium,
Obtaining a hue value, a saturation value, and a brightness value for each pixel of the captured image data;
Dividing the captured image data into at least two hue regions;
Calculating an occupancy ratio indicating a ratio of pixels for each of the divided hue regions to the entire screen of the captured image data;
Calculating a low saturation threshold value for each hue area according to the calculated occupancy ratio for each hue area;
Extracting low saturation pixels used for gray balance adjustment based on the calculated low saturation threshold;
Calculating a gray balance adjustment condition using the extracted low saturation pixel;
Dividing the captured image data into at least two brightness regions;
Calculating an occupancy ratio indicating a ratio of pixels of the divided brightness areas to the entire screen of the captured image data;
Estimating a captured scene of the captured image data according to the calculated occupancy for each brightness region;
Applying gray balance adjustment to the captured image data based on the calculated gray balance adjustment condition and the estimation result of the shooting scene;
It is characterized by including.

The invention described in claim 6
In an image processing method for inputting captured image data and outputting image data optimized for viewing on an output medium,
Obtaining a hue value, a saturation value, and a brightness value for each pixel of the captured image data;
Dividing the captured image data into at least two hue regions;
Calculating an occupancy ratio indicating a ratio of pixels for each of the divided hue regions to the entire screen of the captured image data;
Calculating a gray balance adjustment application rate according to the calculated occupancy for each hue region;
Dividing the captured image data into at least two brightness regions;
Calculating an occupancy ratio indicating a ratio of pixels of the divided brightness areas to the entire screen of the captured image data;
Estimating a captured scene of the captured image data according to the calculated occupancy for each brightness region;
Applying gray balance adjustment to the captured image data based on the calculated application rate and the estimation result of the shooting scene;
It is characterized by including.

The invention described in claim 7
In an image processing method for inputting captured image data and outputting image data optimized for viewing on an output medium,
Obtaining a hue value, a saturation value, and a brightness value for each pixel of the captured image data;
Dividing the captured image data into at least two hue regions;
Calculating an occupancy ratio indicating a ratio of pixels for each of the divided hue regions to the entire screen of the captured image data;
Calculating a low saturation threshold value for each hue area according to the calculated occupancy ratio for each hue area;
Extracting low saturation pixels used for gray balance adjustment based on the calculated low saturation threshold;
Calculating a gray balance adjustment condition using the extracted low saturation pixel;
Calculating a gray balance adjustment application rate according to the calculated occupancy for each hue region;
Dividing the captured image data into at least two brightness regions;
Calculating an occupancy ratio indicating a ratio of pixels of the divided brightness areas to the entire screen of the captured image data;
Estimating a captured scene of the captured image data according to the calculated occupancy for each brightness region;
Applying gray balance adjustment to the captured image data based on the calculated application rate and the estimation result of the shooting scene;
Applying gray balance adjustment to the captured image data based on the calculated gray balance adjustment condition, application rate, and estimation result of the shooting scene;
It is characterized by including.

The invention according to claim 8 is the invention according to any one of claims 5 to 7,
Creating a three-dimensional histogram of the acquired hue value, saturation value and brightness value;
The step of dividing the captured image data into at least two hue regions divides the captured image data into at least two hue regions based on the created three-dimensional histogram,
The step of dividing the captured image data into at least two brightness regions is characterized in that the captured image data is divided into at least two brightness regions based on the created three-dimensional histogram.

The invention according to claim 9 is the invention according to any one of claims 5 to 8,
Dividing each of the divided at least two hue regions into at least two brightness regions;
Calculating an occupancy ratio indicating a ratio of pixels of a predetermined hue and brightness area in the divided area to the entire screen of the captured image data;
Estimating a shooting scene of the captured image data according to the calculated occupancy for each brightness area and the occupancy of the predetermined hue and brightness area;
It is characterized by including.

The invention according to claim 10 is the invention according to any one of claims 1 to 9,
In the step of dividing the picked-up image data into at least two hue regions, the picked-up image data is divided with the hue values of the HSV color system 70, 185, 225 as boundaries, and the skin hue region, the green hue region, the sky region It is characterized by being divided into four hue areas, a hue area and a red hue area.

The invention according to claim 11 is the invention according to any one of claims 5 to 10,
In the step of dividing the picked-up image data into at least two lightness areas, the picked-up image data is divided with HSV color system lightness values of 85 and 170 as boundaries, and a shadow area, an intermediate area, and a highlight area. It is characterized by being divided into two brightness areas.

The invention described in claim 12 is the invention described in claims 5-11,
A lightness deviation amount of a skin hue area among the divided hue areas is calculated and used for estimation of the shooting scene.

The invention according to claim 13 is the invention according to any one of claims 1, 3, 4, 5, and 7,
Calculating a low saturation pixel extraction rate for each hue region according to the calculated occupancy rate for each hue region;
Extracting low saturation pixels used for gray balance adjustment based on the calculated low saturation pixel extraction rate; and
It is characterized by including.

The invention according to claim 14 is the invention according to any one of claims 2, 3, 6, and 7,
The step of calculating the application rate of gray balance adjustment according to the calculated occupancy rate for each hue region is obtained by calculating the application rate for each hue region from each occupancy rate using a different application rate calculation formula for each hue region. The maximum value of the application rate calculated for each hue region is used as the application rate of the gray balance adjustment.

The invention according to claim 15 is the invention according to claim 14,
When the captured image data is divided into a skin hue area, a sky hue area, a green hue area, and a red hue area, the application ratio calculation formula is an application ratio of the skin hue area and the sky hue area with respect to the same occupation ratio. Is set to be smaller than the application rate of other hue regions.

The invention according to claim 16 is the invention according to any one of claims 1 to 15,
The captured image data is scene reference image data.

The invention according to claim 17 is the invention according to any one of claims 1 to 16,
The image data optimized for viewing on the output medium is viewing image reference data.

The invention described in claim 18
In an image processing apparatus for inputting captured image data and outputting image data optimized for viewing on an output medium,
Data acquisition means for obtaining a hue value and a saturation value for each pixel of the captured image data;
Hue area dividing means for dividing the captured image data into at least two hue areas;
A hue area occupancy ratio calculating unit that calculates an occupancy ratio indicating a ratio of pixels for each of the divided hue areas to the entire screen of the captured image data;
Low saturation threshold value calculating means for calculating a low saturation threshold value for each hue region according to the calculated occupancy ratio for each hue region;
Low saturation pixel extraction means for extracting low saturation pixels used for gray balance adjustment based on the calculated low saturation threshold;
A gray balance adjustment condition calculating means for calculating a gray balance adjustment condition using the extracted low saturation pixel;
Based on the calculated gray balance adjustment conditions, gray balance adjustment means for performing gray balance adjustment on the captured image data;
It is characterized by having.

The invention according to claim 19 is
In an image processing apparatus for inputting captured image data and outputting image data optimized for viewing on an output medium,
Data acquisition means for obtaining a hue value and a saturation value for each pixel of the captured image data;
Hue area dividing means for dividing the captured image data into at least two hue areas;
A hue area occupancy ratio calculating unit that calculates an occupancy ratio indicating a ratio of pixels for each of the divided hue areas to the entire screen of the captured image data;
An application rate calculating means for calculating an application rate of gray balance adjustment according to the calculated occupation ratio for each hue region;
Gray balance adjustment means for performing gray balance adjustment based on the calculated application rate;
It is characterized by having.

The invention according to claim 20 provides
In an image processing apparatus for inputting captured image data and outputting image data optimized for viewing on an output medium,
Data acquisition means for obtaining a hue value and a saturation value for each pixel of the captured image data;
Hue area dividing means for dividing the captured image data into at least two hue areas;
A hue area occupancy ratio calculating unit that calculates an occupancy ratio indicating a ratio of pixels for each of the divided hue areas to the entire screen of the captured image data;
Low saturation threshold value calculating means for calculating a low saturation threshold value for each hue region according to the calculated occupancy ratio for each hue region;
Low saturation pixel extraction means for extracting low saturation pixels used for gray balance adjustment based on the calculated low saturation threshold;
A gray balance adjustment condition calculating means for calculating a gray balance adjustment condition using the extracted low saturation pixel;
An application rate calculating means for calculating an application rate of gray balance adjustment according to the calculated occupation ratio for each hue region;
Gray balance adjustment means for performing gray balance adjustment on the captured image data based on the calculated gray balance adjustment condition and application rate;
It is characterized by having.

The invention according to claim 21 is the invention according to any one of claims 18 to 20,
Two-dimensional histogram creation means for creating a two-dimensional histogram of the acquired hue value and saturation value;
The hue area dividing unit divides the captured image data into at least two hue areas based on the created two-dimensional histogram.

The invention described in claim 22
In an image processing apparatus for inputting captured image data and outputting image data optimized for viewing on an output medium,
Data acquisition means for obtaining a hue value, a saturation value and a brightness value for each pixel of the captured image data;
Hue area dividing means for dividing the captured image data into at least two hue areas;
A hue area occupancy ratio calculating unit that calculates an occupancy ratio indicating a ratio of pixels for each of the divided hue areas to the entire screen of the captured image data;
Low saturation threshold value calculating means for calculating a low saturation threshold value for each hue region according to the calculated occupancy ratio for each hue region;
Low saturation pixel extraction means for extracting low saturation pixels used for gray balance adjustment based on the calculated low saturation threshold;
A gray balance adjustment condition calculating means for calculating a gray balance adjustment condition using the extracted low saturation pixel;
Brightness area dividing means for dividing the captured image data into at least two brightness areas;
A brightness area occupancy ratio calculating means for calculating an occupancy ratio indicating a ratio of pixels of the divided brightness area to the entire screen of the captured image data;
In accordance with the calculated occupancy for each brightness area, shooting scene estimation means for estimating a shooting scene of the captured image data;
Gray balance adjustment means for performing gray balance adjustment on the captured image data based on the calculated gray balance adjustment condition and the estimation result of the shooting scene;
It is characterized by having.

The invention according to claim 23 provides
In an image processing apparatus for inputting captured image data and outputting image data optimized for viewing on an output medium,
Data acquisition means for obtaining a hue value, a saturation value and a brightness value for each pixel of the captured image data;
Hue area dividing means for dividing the captured image data into at least two hue areas;
A hue area occupancy ratio calculating unit that calculates an occupancy ratio indicating a ratio of pixels for each of the divided hue areas to the entire screen of the captured image data;
An application rate calculating means for calculating an application rate of gray balance adjustment according to the calculated occupation ratio for each hue region;
Brightness area dividing means for dividing the captured image data into at least two brightness areas;
A brightness area occupancy ratio calculating means for calculating an occupancy ratio indicating a ratio of pixels of the divided brightness area to the entire screen of the captured image data;
In accordance with the calculated occupancy for each brightness area, shooting scene estimation means for estimating a shooting scene of the captured image data;
Gray balance adjustment means for performing gray balance adjustment on the captured image data based on the calculated application rate and the estimation result of the shooting scene;
It is characterized by having.

The invention according to claim 24 provides
In an image processing apparatus for inputting captured image data and outputting image data optimized for viewing on an output medium,
Data acquisition means for obtaining a hue value, a saturation value and a brightness value for each pixel of the captured image data;
Hue area dividing means for dividing the captured image data into at least two hue areas;
A hue area occupancy ratio calculating unit that calculates an occupancy ratio indicating a ratio of pixels for each of the divided hue areas to the entire screen of the captured image data;
Low saturation threshold value calculating means for calculating a low saturation threshold value for each hue region according to the calculated occupancy ratio for each hue region;
Low saturation pixel extraction means for extracting low saturation pixels used for gray balance adjustment based on the calculated low saturation threshold;
A gray balance adjustment condition calculating means for calculating a gray balance adjustment condition using the extracted low saturation pixel;
An application rate calculating means for calculating an application rate of gray balance adjustment according to the calculated occupation ratio for each hue region;
Brightness area dividing means for dividing the captured image data into at least two brightness areas;
A brightness area occupancy ratio calculating means for calculating an occupancy ratio indicating a ratio of pixels of the divided brightness area to the entire screen of the captured image data;
In accordance with the calculated occupancy for each brightness area, shooting scene estimation means for estimating a shooting scene of the captured image data;
Gray balance adjustment means for performing gray balance adjustment on the captured image data based on the calculated gray balance adjustment condition, application rate, and estimation result of the shooting scene;
It is characterized by including.

The invention according to claim 25 is the invention according to any one of claims 22 to 24,
A three-dimensional histogram creating means for creating a three-dimensional histogram of the acquired hue value, saturation value and lightness value;
The hue area dividing unit divides the captured image data into at least two hue areas based on the created three-dimensional histogram,
The brightness area dividing means divides the captured image data into at least two brightness areas based on the created three-dimensional histogram.

The invention according to claim 26 is the invention according to any one of claims 22 to 25,
Hue lightness area dividing means for dividing each of the divided at least two hue areas into at least two lightness areas;
Hue lightness area occupancy calculating means for calculating an occupancy ratio indicating a ratio of pixels of a predetermined hue and lightness area among the divided areas to the entire screen of the captured image data,
The shooting scene estimation means estimates a shooting scene of the captured image data according to the calculated occupancy for each brightness area and the occupancy of the predetermined hue and brightness area.

The invention according to claim 27 is the invention according to any one of claims 18 to 26,
The hue area dividing means divides the captured image data with HSV color system hue values of 70, 185, and 225 as boundaries, and includes a skin hue area, a green hue area, a sky hue area, and a red hue area. It is characterized by being divided into four hue regions.

The invention according to claim 28 is the invention according to any one of claims 22 to 27,
The brightness area dividing unit divides the captured image data into three brightness areas, a shadow area, an intermediate area, and a highlight area, by dividing the HSV color system brightness values with values of 85 and 170 as boundaries. It is characterized by.

The invention according to claim 29 is the invention according to any one of claims 22 to 28,
The photographing scene estimation means calculates a lightness deviation amount of a skin hue area among the divided hue areas, and uses the lightness deviation amount for estimation of the photographing scene.

The invention according to claim 30 is the invention according to any one of claims 18, 20, 21, 22, 24,
Low saturation pixel extraction rate calculating means for calculating an extraction rate of low saturation pixels for each hue region according to the calculated occupancy rate for each hue region;
The low saturation pixel extraction unit extracts low saturation pixels used for gray balance adjustment based on the calculated low saturation threshold or the calculated extraction ratio of the low saturation pixels. .

The invention described in claim 31 is the invention described in any one of claims 19, 20, 23, 24.
The application rate calculating means calculates an application rate for each hue region from each occupancy rate using a different calculation formula for each hue region, and calculates a maximum value of the application rate calculated for each hue region. It is characterized by the application rate of balance adjustment.

The invention according to claim 32 is the invention according to claim 31,
When the captured image data is divided into a skin hue area, a sky hue area, a green hue area, and a red hue area, the application ratio calculation formula is an application ratio of the skin hue area and the sky hue area with respect to the same occupation ratio. Is set to be smaller than the application rate of other hue regions.

The invention according to claim 33 is the invention according to any one of claims 18 to 32,
The captured image data is scene reference image data.

The invention according to claim 34 is the invention according to any one of claims 18 to 33,
The image data optimized for viewing on the output medium is viewing image reference data.

The invention according to claim 35 provides
In an image recording apparatus that inputs captured image data, generates image data optimized for viewing on an output medium, and forms the generated image data on the output medium.
Data acquisition means for obtaining a hue value and a saturation value for each pixel of the captured image data;
Hue area dividing means for dividing the captured image data into at least two hue areas;
A hue area occupancy ratio calculating unit that calculates an occupancy ratio indicating a ratio of pixels for each of the divided hue areas to the entire screen of the captured image data;
Low saturation threshold value calculating means for calculating a low saturation threshold value for each hue region according to the calculated occupancy ratio for each hue region;
Low saturation pixel extraction means for extracting low saturation pixels used for gray balance adjustment based on the calculated low saturation threshold;
A gray balance adjustment condition calculating means for calculating a gray balance adjustment condition using the extracted low saturation pixel;
Based on the calculated gray balance adjustment conditions, gray balance adjustment means for performing gray balance adjustment on the captured image data;
It is characterized by having.

The invention according to claim 36 provides
In an image recording apparatus that inputs captured image data, generates image data optimized for viewing on an output medium, and forms the generated image data on the output medium.
Data acquisition means for obtaining a hue value and a saturation value for each pixel of the captured image data;
Hue area dividing means for dividing the captured image data into at least two hue areas;
A hue area occupancy ratio calculating unit that calculates an occupancy ratio indicating a ratio of pixels for each of the divided hue areas to the entire screen of the captured image data;
An application rate calculating means for calculating an application rate of gray balance adjustment according to the calculated occupation ratio for each hue region;
Gray balance adjustment means for performing gray balance adjustment based on the calculated application rate;
It is characterized by having.

The invention according to claim 37 provides:

In an image recording apparatus that inputs captured image data, generates image data optimized for viewing on an output medium, and forms the generated image data on the output medium.
Data acquisition means for obtaining a hue value and a saturation value for each pixel of the captured image data;
Hue area dividing means for dividing the captured image data into at least two hue areas;
A hue area occupancy ratio calculating unit that calculates an occupancy ratio indicating a ratio of pixels for each of the divided hue areas to the entire screen of the captured image data;
Low saturation threshold value calculating means for calculating a low saturation threshold value for each hue region according to the calculated occupancy ratio for each hue region;
Low saturation pixel extraction means for extracting low saturation pixels used for gray balance adjustment based on the calculated low saturation threshold;
A gray balance adjustment condition calculating means for calculating a gray balance adjustment condition using the extracted low saturation pixel;
An application rate calculating means for calculating an application rate of gray balance adjustment according to the calculated occupation ratio for each hue region;
Gray balance adjustment means for performing gray balance adjustment on the captured image data based on the calculated gray balance adjustment condition and application rate;
It is characterized by having.

The invention according to claim 38 is the invention according to any one of claims 35 to 37,
Two-dimensional histogram creation means for creating a two-dimensional histogram of the acquired hue value and saturation value;
The hue area dividing unit divides the captured image data into at least two hue areas based on the created two-dimensional histogram.

The invention according to claim 39 provides
In an image recording apparatus that inputs captured image data, generates image data optimized for viewing on an output medium, and forms the generated image data on the output medium.
Data acquisition means for obtaining a hue value, a saturation value and a brightness value for each pixel of the captured image data;
Hue area dividing means for dividing the captured image data into at least two hue areas;
A hue area occupancy ratio calculating unit that calculates an occupancy ratio indicating a ratio of pixels for each of the divided hue areas to the entire screen of the captured image data;
Low saturation threshold value calculating means for calculating a low saturation threshold value for each hue region according to the calculated occupancy ratio for each hue region;
Low saturation pixel extraction means for extracting low saturation pixels used for gray balance adjustment based on the calculated low saturation threshold;
A gray balance adjustment condition calculating means for calculating a gray balance adjustment condition using the extracted low saturation pixel;
Brightness area dividing means for dividing the captured image data into at least two brightness areas;
A brightness area occupancy ratio calculating means for calculating an occupancy ratio indicating a ratio of pixels of the divided brightness area to the entire screen of the captured image data;
In accordance with the calculated occupancy for each brightness area, shooting scene estimation means for estimating a shooting scene of the captured image data;
Gray balance adjustment means for performing gray balance adjustment on the captured image data based on the calculated gray balance adjustment condition and the estimation result of the shooting scene;
It is characterized by having.

The invention according to claim 40 provides
In an image recording apparatus that inputs captured image data, generates image data optimized for viewing on an output medium, and forms the generated image data on the output medium.
Data acquisition means for obtaining a hue value, a saturation value and a brightness value for each pixel of the captured image data;
Hue area dividing means for dividing the captured image data into at least two hue areas;
A hue area occupancy ratio calculating unit that calculates an occupancy ratio indicating a ratio of pixels for each of the divided hue areas to the entire screen of the captured image data;
An application rate calculating means for calculating an application rate of gray balance adjustment according to the calculated occupation ratio for each hue region;
Brightness area dividing means for dividing the captured image data into at least two brightness areas;
A brightness area occupancy ratio calculating means for calculating an occupancy ratio indicating a ratio of pixels of the divided brightness area to the entire screen of the captured image data;
In accordance with the calculated occupancy for each brightness area, shooting scene estimation means for estimating a shooting scene of the captured image data;
Gray balance adjustment means for performing gray balance adjustment on the captured image data based on the calculated application rate and the estimation result of the shooting scene;
It is characterized by having.

The invention of claim 41 provides
In an image recording apparatus that inputs captured image data, generates image data optimized for viewing on an output medium, and forms the generated image data on the output medium.
Data acquisition means for obtaining a hue value, a saturation value and a brightness value for each pixel of the captured image data;
Hue area dividing means for dividing the captured image data into at least two hue areas;
A hue area occupancy ratio calculating unit that calculates an occupancy ratio indicating a ratio of pixels for each of the divided hue areas to the entire screen of the captured image data;
Low saturation threshold value calculating means for calculating a low saturation threshold value for each hue region according to the calculated occupancy ratio for each hue region;
Low saturation pixel extraction means for extracting low saturation pixels used for gray balance adjustment based on the calculated low saturation threshold;
A gray balance adjustment condition calculating means for calculating a gray balance adjustment condition using the extracted low saturation pixel;
An application rate calculating means for calculating an application rate of gray balance adjustment according to the calculated occupation ratio for each hue region;
Brightness area dividing means for dividing the captured image data into at least two brightness areas;
A brightness area occupancy ratio calculating means for calculating an occupancy ratio indicating a ratio of pixels of the divided brightness area to the entire screen of the captured image data;
In accordance with the calculated occupancy for each brightness area, shooting scene estimation means for estimating a shooting scene of the captured image data;
Gray balance adjustment means for performing gray balance adjustment on the captured image data based on the calculated gray balance adjustment condition, application rate, and estimation result of the shooting scene;
It is characterized by including.

The invention according to claim 42 is the invention according to any one of claims 39 to 41,
A three-dimensional histogram creating means for creating a three-dimensional histogram of the acquired hue value, saturation value and lightness value;
The hue area dividing unit divides the captured image data into at least two hue areas based on the created three-dimensional histogram,
The brightness area dividing means divides the captured image data into at least two brightness areas based on the created three-dimensional histogram.

The invention according to claim 43 is the invention according to any one of claims 39 to 42,
Hue lightness area dividing means for dividing each of the divided at least two hue areas into at least two lightness areas;
Hue lightness area occupancy calculating means for calculating an occupancy ratio indicating a ratio of pixels of a predetermined hue and lightness area among the divided areas to the entire screen of the captured image data,
The shooting scene estimation means estimates a shooting scene of the captured image data according to the calculated occupancy for each brightness area and the occupancy of the predetermined hue and brightness area.

The invention according to claim 44 is the invention according to any one of claims 35 to 43,
The hue area dividing means divides the captured image data with HSV color system hue values of 70, 185, and 225 as boundaries, and includes a skin hue area, a green hue area, a sky hue area, and a red hue area. It is characterized by being divided into four hue regions.

The invention according to claim 45 is the invention according to any one of claims 39 to 44,
The brightness area dividing unit divides the captured image data into three brightness areas, a shadow area, an intermediate area, and a highlight area, by dividing the HSV color system brightness values with values of 85 and 170 as boundaries. It is characterized by.

The invention according to claim 46 is the invention according to any one of claims 39 to 45,
The photographing scene estimation means calculates a lightness deviation amount of a skin hue area among the divided hue areas, and uses the lightness deviation amount for estimation of the photographing scene.

The invention according to claim 47 is the invention according to any one of claims 35, 37, 38, 39, 41,
Low saturation pixel extraction rate calculating means for calculating an extraction rate of low saturation pixels for each hue region according to the calculated occupancy rate for each hue region;
The low saturation pixel extraction unit extracts low saturation pixels used for gray balance adjustment based on the calculated low saturation threshold or the calculated extraction ratio of the low saturation pixels. .

The invention according to claim 48 is the invention according to any one of claims 36, 37, 40, 41,
The application rate calculating means calculates an application rate for each hue region from each occupancy rate using a different calculation formula for each hue region, and calculates a maximum value of the application rate calculated for each hue region. It is characterized by the application rate of balance adjustment.

The invention according to claim 49 is the invention according to claim 48,
When the captured image data is divided into a skin hue area, a sky hue area, a green hue area, and a red hue area, the application ratio calculation formula is an application ratio of the skin hue area and the sky hue area with respect to the same occupation ratio. Is set to be smaller than the application rate of other hue regions.

The invention according to claim 50 is the invention according to any one of claims 35 to 49,
The captured image data is scene reference image data.

The invention according to claim 51 is the invention according to any one of claims 35 to 50,
The image data optimized for viewing on the output medium is viewing image reference data.

  Here, “captured image data” described in this specification is digital image data in which subject information is held as an electrical signal value. Whatever process is used to obtain digital image data, such as digital image data recorded as color image information on a color photographic film and generated by scanning a scanner or digital image data generated by photographing with a digital camera good.

  However, when digital image data is generated from a color negative film by reading a scanner, the unexposed area (minimum density area) of the color negative film is calibrated and inverted so that the RGB values of the digital image data are all zero. It is desirable to reproduce a state almost proportional to the luminance change of the subject by performing conversion processing from a scale directly proportional to the amount of transmitted light to a logarithmic (density) scale and gamma correction processing of a color negative film. . Similarly, it is desirable that the digital image data captured by the digital camera is in a state that is substantially proportional to the luminance change of the subject. Further, the digital image data is preferably “scene reference image data”.

  "Scene reference image data" refers to standard colors such as RIMM RGB (Reference Input Medium Metric RGB) and ERIMM RGB (Extended Reference Input Medium Metric RGB), which are signal strengths of each color channel based on at least the spectral sensitivity of the image sensor itself. It means image data that has been mapped to a space and in which image processing for modifying the data contents is omitted in order to improve the effect at the time of image viewing such as gradation conversion, sharpness enhancement, and saturation enhancement. The scene reference image data is the photoelectric conversion characteristics of the imaging device (opto-electronic conversion function defined by ISO1452, such as Corona “Fine Imaging and Digital Photography” (published by the Japan Photographic Society Publishing Committee ◆ page 479). It is preferable that the correction is performed. The information amount (for example, the number of gradations) of the standardized scene reference image data conforms to the performance of the A / D converter, and the information amount (for example, the number of gradations) required for the “viewing image reference data” described later. It is preferable that it is equal to or higher. For example, when the number of gradations of the viewing image reference data is 8 bits per channel, the number of gradations of the scene reference image data is preferably 12 bits or more, more preferably 14 bits or more, and even more preferably 16 bits or more.

  “Optimized for viewing on output media” means to obtain optimal images on output devices such as display devices such as CRT, liquid crystal display, plasma display, silver halide photographic paper, inkjet paper, thermal printer paper, etc. For example, when it is assumed that the image is displayed on a CRT display monitor compliant with the sRGB standard, the process is performed so that optimum color reproduction is obtained within the color gamut of the sRGB standard. If output to silver salt photographic paper is assumed, processing is performed so that optimum color reproduction is obtained within the color gamut of silver salt photographic paper. In addition to color gamut compression, gradation compression from 16 bits to 8 bits, reduction of the number of output pixels, and processing for handling output characteristics (LUT) of the output device are also included. Furthermore, it goes without saying that tone compression processing such as noise suppression, sharpening, gray balance adjustment, saturation adjustment, or dodging processing is performed.

  "Image data optimized for viewing on output media" means digital image data used to generate images on output media such as CRT, liquid crystal display, plasma display, silver halide photographic paper, inkjet paper, thermal printer paper, etc. In other words, processing is performed so as to obtain an optimal image on a display device such as a CRT, a liquid crystal display, a plasma display, and an output medium such as a silver salt photographic paper, an inkjet paper, and a thermal printer paper. When the above-described “captured image data” is “scene reference image data”, “image data optimized for viewing on an output medium” is referred to as “viewing image reference data”.

The boundary values for dividing the hue area in claims 10, 11, 27, 28, 44, and 45 of the present invention are the detection of human skin color, plant green, and sky color for about 1000 frames of film scan image. It was calculated from the result of examining the hue range where the rate was the highest. Regarding the boundary value for dividing the lightness region, a scene such as backlight and strobe proximity is defined in advance, and the same investigation is performed to determine a numerical value. In the practice of the present invention, it is desirable to change the numerical limits for the film scan image and the digital camera image.

  According to the invention described in claims 1, 18 and 35, since the low saturation threshold setting for extracting low saturation pixels for gray balance adjustment condition calculation is changed based on the coloration tendency in the captured image data, For example, in a scene where the rule of thumb that the highlight side or shadow side in the image has low saturation does not match, such as a shooting scene where the proportion of lawn, sky, etc. is large, the low used to calculate gray balance adjustment conditions from a specific hue It is possible to prevent an uneven hue due to the extraction of many saturation pixels, and as a result, it is possible to suppress color failure.

  According to the invention described in claims 2, 19, and 36, since the application rate of gray balance adjustment is changed based on the color arrangement tendency in the captured image data, for example, a shooting scene using an artificial light source such as tungsten or a self-light-emitting light source In this case, excessive color deviation correction is prevented, and as a result, color feria can be suppressed.

  According to the invention described in claims 3, 20 and 37, since the low saturation threshold value setting for extracting low saturation pixels for gray balance adjustment condition calculation is changed, for example, the proportion occupied by lawn, sky, etc. In scenes where the empirical rule that the highlight side or shadow side in the image is low saturation does not match, such as in large shooting scenes, because many low saturation pixels used for gray balance adjustment condition calculation are extracted from a specific hue Hue bias is prevented. Further, since the application rate of gray balance adjustment is changed based on the color arrangement tendency in the captured image data, excessive color deviation correction in a shooting scene using an artificial light source such as tungsten or a self-light-emitting light source is prevented. As a result, color feria can be suppressed.

  According to invention of Claim 4, 21, 38, the two-dimensional histogram of the hue value and saturation value of picked-up image data is created, Based on the created two-dimensional histogram, at least two pieces of picked-up image data are obtained. Since the image is divided into hue regions, the processing can be made more efficient.

  According to the invention described in claims 5, 22 and 39, since the low saturation threshold value setting for extracting low saturation pixels for gray balance adjustment condition calculation is changed based on the coloration tendency in the captured image data, For example, in a scene where the rule of thumb that the highlight side or shadow side in the image has low saturation does not match, such as a shooting scene where the proportion of lawn, sky, etc. is large, the low used to calculate gray balance adjustment conditions from a specific hue It is possible to prevent an uneven hue due to the extraction of many saturation pixels, and as a result, it is possible to suppress color failure. Also, by determining the lightness distribution tendency in the captured image data and estimating the shooting scene such as backlighting or close-up flash photography, it is possible to prevent erroneous setting of the highlight point and to further improve the accuracy of color feria suppression. It becomes.

  According to the invention described in claims 6, 23 and 40, since the application rate of the gray balance adjustment is changed based on the color arrangement tendency in the captured image data, for example, a photographing scene using an artificial light source such as tungsten or a self-light-emitting light source. Excessive color deviation correction at is prevented. As a result, color feria can be suppressed. Further, it is possible to adjust the application rate of the gray balance adjustment according to the photographic scene by determining the lightness distribution tendency in the photographic image data and estimating the photographic scene such as backlight or strobe proximity photography.

  According to the invention described in claims 7, 24 and 41, since the low saturation threshold value setting for extracting low saturation pixels for gray balance adjustment condition calculation is changed, for example, the proportion of lawn, sky, etc. In scenes where the empirical rule that the highlight side or shadow side in the image is low saturation does not match, such as in large shooting scenes, because many low saturation pixels used for gray balance adjustment condition calculation are extracted from a specific hue Hue bias is prevented. Further, since the application rate of gray balance adjustment is changed based on the color arrangement tendency in the captured image data, excessive color deviation correction in a shooting scene using an artificial light source such as tungsten or a self-light-emitting light source is prevented. In addition, by determining the lightness distribution tendency in the captured image data and estimating the shooting scene such as backlighting or close-up flash photography, incorrect setting of the highlight point is prevented and gray balance adjustment is applied according to the shooting scene The rate can be adjusted. As a result, it is possible to improve the accuracy of color feria suppression.

  The invention according to claim 8, 25, 42 creates a three-dimensional histogram of the saturation value and the lightness value of the captured image data, and based on the created three-dimensional histogram, the captured image data is stored in at least two hue regions. Since the captured image data is divided into at least two brightness areas based on the created three-dimensional histogram, the processing can be made more efficient.

  According to the invention described in claims 9, 26, and 43, since the photographic scene is estimated according to the occupancy rate for each lightness area and the occupancy ratio of the predetermined hue and lightness area, it is possible to improve the estimation accuracy. It becomes possible.

  According to the tenth, twenty-seventh and twenty-fourth aspects of the present invention, it is possible to perform gray balance adjustment in accordance with the flesh color, green, sky blue, and red coloration tendency.

  According to the invention described in claims 11, 28, and 45, it is possible to estimate a photographic scene according to the brightness distribution tendency of shadow, middle, and highlight.

  According to the invention described in claims 12, 29, and 46, the lightness deviation amount of the skin hue region can be used for estimation of the shooting scene.

  According to the invention described in claims 13, 30 and 47, the low saturation pixel extraction rate is calculated for each hue region in accordance with the occupancy ratio for each hue region, and the calculated low saturation pixel extraction rate is calculated. Based on this, low saturation pixels used for gray balance adjustment are extracted. Therefore, it is possible to prevent a deviation between hues in low saturation pixel extraction.

  According to the invention described in claims 14, 31, and 48, it is possible to calculate the application rate according to the characteristics for each hue region.

  According to the invention described in claims 15, 32, and 49, when the occupation ratio of the skin hue area and the sky hue area that are likely to be erroneously set to the highlight point is high, the application ratio of the gray balance adjustment can be lowered. Therefore, excessive color deviation correction can be prevented.

  According to the invention described in claims 16, 33, and 50, it is possible to form an optimized image on the output medium without causing information loss of the captured image information.

  According to the invention described in claims 17, 34, and 51, it is possible to provide optimum viewing image reference data in which no color failure occurs.

[First Embodiment]
Hereinafter, a first embodiment of the present invention will be described in detail with reference to the drawings.
First, the configuration will be described.

  FIG. 1 is a perspective view showing an external configuration of an image recording apparatus 1 according to an embodiment of the present invention. As shown in FIG. 1, the image recording apparatus 1 is provided with a magazine loading unit 3 for loading a photosensitive material on one side of a housing 2. Inside the housing 2 are provided an exposure processing unit 4 for exposing the photosensitive material, and a print creating unit 5 for developing and drying the exposed photosensitive material to create a print. On the other side surface of the housing 2, a tray 6 for discharging the print created by the print creation unit 5 is provided.

  In addition, a CRT (Cathode Ray Tube) 8 serving as a display device, a film scanner unit 9 serving as a device for reading a transparent document, a reflective document input device 10, and an operation unit 11 are provided on the upper portion of the housing 2. The CRT 8 constitutes display means for displaying an image of image information to be printed on the screen. Further, the housing 2 includes an image reading unit 14 that can read image information recorded on various digital recording media, and an image writing unit 15 that can write (output) image signals to various digital recording media. Yes. In addition, a control unit 7 that centrally controls these units is provided inside the housing 2.

  The image reading unit 14 includes a PC card adapter 14a and a floppy (registered trademark) disk adapter 14b, and a PC card 13a and a floppy (registered trademark) disk 13b can be inserted therein. The PC card 13a has, for example, a memory in which a plurality of frame image data captured by a digital camera is recorded. For example, a plurality of frame image data captured by a digital camera is recorded on the floppy (registered trademark) disk 13b. Examples of the recording medium on which frame image data is recorded other than the PC card 13a and the floppy (registered trademark) disk 13b include a multimedia card (registered trademark), a memory stick (registered trademark), MD data, and a CD-ROM. .

  The image writing unit 15 is provided with a floppy (registered trademark) disk adapter 15a, an MO adapter 15b, and an optical disk adapter 15c, into which an FD 16a, an MO 16b, and an optical disk 16c can be respectively inserted. Examples of the optical disc 16c include a CD-R and a DVD-R.

  In FIG. 1, the operation unit 11, the CRT 8, the film scanner unit 9, the reflection original input device 10, and the image reading unit 14 are integrally provided in the housing 2. One or more may be provided separately.

  Further, in the image recording apparatus 1 shown in FIG. 1, there is exemplified an apparatus that creates a print by exposing to a photosensitive material and developing it, but the print creation system is not limited to this, for example, an inkjet system, an electronic system, etc. A method such as a photographic method, a thermal method, or a sublimation method may be used.

<Functional Configuration of Image Recording Apparatus 1>
FIG. 2 is a block diagram showing a functional configuration of the image recording apparatus 1. Hereinafter, the functional configuration of the image output apparatus 1 will be described with reference to FIG.

  The control unit 7 is configured by a microcomputer, and cooperates with various control programs stored in a storage unit (not shown) such as a ROM (Read Only Memory) and a CPU (Central Processing Unit) (not shown). The operation of each part constituting the image recording apparatus 1 is controlled.

  The control unit 7 includes an image processing unit 70 according to the image processing apparatus of the present invention, and is read from the film scanner unit 9 or the reflective original input device 10 based on an input signal (command information) from the operation unit 11. Image processing is performed on the image signal, the image signal read from the image reading unit 14, and the image signal input from the external device via the communication unit 32 to form exposure image information, and the exposure processing unit 4 Output. Further, the image processing unit 70 performs a conversion process corresponding to the output form on the image signal subjected to the image processing, and outputs the image signal. Output destinations of the image processing unit 70 include the CRT 8, the image writing unit 15, the communication means (output) 33, and the like.

  The exposure processing unit 4 exposes an image to the photosensitive material and outputs the photosensitive material to the print creating unit 5. The print creating unit 5 develops and exposes the exposed photosensitive material to create prints P1, P2, and P3. The print P1 is a service size, high-definition size, panoramic size print, the print P2 is an A4 size print, and the print P3 is a business card size print.

  The film scanner unit 9 reads a frame image recorded on a transparent original such as a developed negative film N or a reversal film imaged by an analog camera, and acquires a digital image signal of the frame image. The reflective original input device 10 reads an image on a print P (photo print, document, various printed materials) by a flat bed scanner, and acquires a digital image signal.

  The image reading unit 14 reads frame image information recorded on the PC card 13 a or the floppy (registered trademark) disk 13 b and transfers the frame image information to the control unit 7. The image reading unit 14 includes, as the image transfer means 30, a PC card adapter 14a, a floppy (registered trademark) disk adapter 14b, and the like. The image reading unit 14 reads frame image information recorded on the PC card 13a inserted into the PC card adapter 14a or the floppy (registered trademark) disk 13b inserted into the floppy (registered trademark) disk adapter 14b. Transfer to the control unit 7. For example, a PC card reader or a PC card slot is used as the PC card adapter 14a.

  The communication means (input) 32 receives an image signal representing a captured image and a print command signal from another computer in the facility where the image recording apparatus 1 is installed, or a distant computer via the Internet or the like.

  The image writing unit 15 includes, as the image conveying unit 31, a floppy (registered trademark) disk adapter 15a, an MO adapter 15b, and an optical disk adapter 15c. In accordance with a write signal input from the control unit 7, the image writing unit 15 includes a floppy (registered trademark) disk 16a inserted into the floppy (registered trademark) disk adapter 15a, an MO 16b inserted into the MO adapter 15b, The image signal generated by the image processing method according to the present invention is written to the optical disk 16c inserted into the optical disk adapter 15c.

  The data storage unit 71 stores and sequentially stores image information and order information corresponding to the image information (information on how many prints are to be created from images of which frames, print size information, and the like).

  The template storage means 72 stores at least one template data for setting a synthesis area and a background image, an illustration image, and the like, which are sample image data corresponding to the sample identification information D1, D2, and D3. A predetermined template is selected from a plurality of templates that are set by the operation of the operator and stored in advance in the template storage means 72, and the frame image information is synthesized by the selected template and designated sample identification information D1, D2, D3 The sample image data selected on the basis of the image data and the image data and / or character data based on the order are combined to create a print based on the specified sample. The synthesis using this template is performed by a well-known chroma key method.

  Note that sample identification information D1, D2, and D3 for specifying a print sample is configured to be input from the operation unit 211. These sample identification information is recorded on a print sample or an order sheet. Therefore, it can be read by reading means such as OCR. Or it can also input by an operator's keyboard operation.

  In this way, sample image data is recorded corresponding to the sample identification information D1 for designating the print sample, the sample identification information D1 for designating the print sample is input, and based on the input sample identification information D1. Select the sample image data, synthesize the selected sample image data with the image data and / or text data based on the order, and create a print based on the specified sample. Can actually place a print order and meet the diverse requirements of a wide range of users.

  Also, the first sample identification information D2 designating the first sample and the image data of the first sample are stored, and the second sample identification information D3 designating the second sample and the second sample The image data is stored, the sample image data selected based on the designated first and second sample identification information D2 and D3, and the image data and / or character data based on the order are synthesized, and according to the designation In order to create a print based on a sample, it is possible to synthesize a wider variety of images, and it is possible to create a print that meets a wider variety of user requirements.

  The operation unit 11 includes information input means 12. The information input unit 12 is configured by a touch panel, for example, and outputs a pressing signal of the information input unit 12 to the control unit 7 as an input signal. Note that the operation unit 11 may be configured to include a keyboard, a mouse, and the like. The CRT 8 displays image information and the like according to a display control signal input from the control unit 7.

  The communication means (output) 33 sends an image signal representing a photographed image after image processing of the present invention and order information attached thereto to other computers in the facility where the image recording apparatus 1 is installed, the Internet Etc. to a distant computer via

  As shown in FIG. 2, the image recording apparatus 1 displays an image input unit that captures image information obtained by dividing and metering images of various digital media and an image original, an image processing unit, and a processed image. Print output, image output means for writing to an image recording medium, and means for transmitting image data and accompanying order information to another computer in the facility or a distant computer via the Internet via a communication line, Prepare.

<Internal Configuration of Image Processing Unit 70>
FIG. 3 is a block diagram illustrating a functional configuration of the image processing unit 70. Hereinafter, the image processing unit 70 will be described in detail with reference to FIG.

  As shown in FIG. 3, the image processing unit 70 includes an image adjustment processing unit 701, a film scan data processing unit 702, a reflection original scan data processing unit 703, an image data format decoding processing unit 704, a template processing unit 705, and CRT specific processing. 706, printer specific processing unit A707, printer specific processing unit B708, and image data creation processing unit 709.

  The film scan data processing unit 702 performs a calibration operation specific to the film scanner unit 9, negative / positive reversal (in the case of a negative document), dust scratch removal, contrast adjustment, granular noise removal, and sharpness for the image data input from the film scanner unit 9. Processing such as conversion enhancement is performed, and processed image data is output to the image adjustment processing unit 701. In addition, the film size, the negative / positive type, information relating to the main subject optically or magnetically recorded on the film, information relating to the photographing conditions (for example, information content described in APS), and the like are also output to the image adjustment processing unit 701. .

  The reflection original scan data processing unit 703 performs a calibration operation unique to the reflection original input device 10, negative / positive reversal (in the case of a negative original), dust flaw removal, contrast adjustment, and noise removal on the image data input from the reflection original input device 10. Then, processing such as sharpening enhancement is performed, and processed image data is output to the image adjustment processing unit 701.

  The image data format decoding processing unit 704 restores the compression code, if necessary, according to the data format of the image data input from the image transfer means 30 and / or the communication means (input) 32, and the color data. Are converted into a data format suitable for computation in the image processing unit 70 and output to the image adjustment processing unit 701. Further, when the size of the output image is specified from any of the operation unit 11, the communication unit (input) 32, and the image transfer unit 30, the image data format decoding processing unit 704 detects the specified information, The image is output to the image adjustment processing unit 701. Information about the size of the output image designated by the image transfer means 30 is embedded in the header information and tag information of the image data acquired by the image transfer means 30.

  The image adjustment processing unit 701 receives from the film scanner unit 9, the reflection original input device 10, the image transfer unit 30, the communication unit (input) 32, and the template processing unit 705 based on the command from the operation unit 11 or the control unit 7. The image data is subjected to optimization processing including gray balance adjustment processing A, which will be described later, to generate digital image data for output optimized for viewing on the output medium. The CRT specific processing unit 706, printer The data is output to the unique processing unit A 707, the printer unique processing unit B 708, the image data creation processing unit 709, and the data storage unit 71.

  In the optimization process, for example, when it is assumed that the image is displayed on a CRT display monitor compliant with the sRGB standard, the optimal color reproduction is performed within the sRGB standard color gamut. If output to silver salt photographic paper is assumed, processing is performed so that optimum color reproduction is obtained within the color gamut of silver salt photographic paper. In addition to the compression of the color gamut, gradation compression from 16 bits to 8 bits, reduction of the number of output pixels, and processing for handling output characteristics (LUT) of the output device are also included. Furthermore, it goes without saying that tone compression processing such as noise suppression, sharpening, saturation adjustment, or dodging processing is performed.

  The template processing unit 705 reads out predetermined image data (template) from the template storage unit 72 based on a command from the image adjustment processing unit 701, and performs template processing for combining the image data to be processed with the template. The image data after the template processing is output to the image adjustment processing unit 701.

  The CRT specific processing unit 706 performs a process such as changing the number of pixels and color matching on the image data input from the image adjustment processing unit 701 as necessary, and combines the information with information that needs to be displayed, such as control information. Image data is output to the CRT 8.

  The printer-specific processing unit A707 performs printer-specific calibration processing, color matching, pixel number change processing, and the like as necessary, and outputs processed image data to the exposure processing unit 4.

  When an external printer 51 such as a large-format ink jet printer can be connected to the image recording apparatus 1 of the present invention, a printer specific processing unit B708 is provided for each printer apparatus to be connected. The printer-specific processing unit B708 performs printer-specific calibration processing, color matching, pixel number change, and the like, and outputs processed image data to the external printer 51.

  The image data format creation processing unit 709 converts the image data input from the image adjustment processing unit 701 into various general-purpose image formats typified by JPEG, TIFF, Exif, and the like as necessary. The completed image data is output to the image transport unit 31 and the communication means (output) 33.

  3, the film scan data processing unit 702, the reflection original scan data processing unit 703, the image data format decoding processing unit 704, the image adjustment processing unit 701, the CRT specific processing unit 706, the printer specific processing unit A707, the printer The divisions of the unique processing unit B708 and the image data creation processing unit 709 are provided to assist understanding of the functions of the image processing unit 70, and are not necessarily realized as physically independent devices. Alternatively, it may be realized as a type of software processing performed by a single CPU.

Next, the operation of the present invention will be described.
FIG. 4 is a flowchart showing the gray balance adjustment process A executed by the image adjustment processing unit 701. This processing is realized by software processing in cooperation with the gray balance adjustment processing A program stored in a storage unit (not shown) such as a ROM and the CPU. The film scan data processing unit 702, reflection The process starts when image data (image signal) is input from the document scan data processing unit 703 or the image data format decoding processing unit 704. By executing this gray balance adjustment processing A, the data acquisition means, hue area division means, hue area occupancy ratio calculation means, low saturation threshold value calculation means, low saturation, and the like according to claims 18, 20, 35 and 37 of the present invention. The threshold value extraction means, the gray balance adjustment condition calculation means, the gray balance adjustment means, and the two-dimensional histogram creation means according to claims 21 and 38 are realized.
Hereinafter, the gray balance adjustment processing A will be described with reference to FIG.

  When image data is input from the film scan data processing unit 702, the reflection original scan data processing unit 703, or the image data format decoding processing unit 704, the input image data is converted into L * a * b * or HSV from the RGB color system. Are converted into the color system, the hue value and the saturation value for each pixel of the input image data are acquired and stored in a RAM (not shown) (step S1).

Hereinafter, specific examples of calculation formulas for acquiring the hue value, the saturation value, and the brightness value from the RGB values of each pixel of the input image data will be shown.
First, an example of obtaining a hue value, a saturation value, and a lightness value by converting from RGB to the HSV color system will be described in detail with reference to [Expression 1]. This conversion program is hereinafter referred to as an HSV conversion program. The HSV color system is devised based on the color system proposed by Munsell, which expresses colors with three elements of hue, saturation, and value (brightness).

The values of digital image data that is input image data are defined as InR, InG, and InB. The calculated hue value is defined as OutH, the scale is defined as 0 to 360, the saturation value is defined as OutS, the lightness value is defined as OutV, and the unit is defined as 0 to 255.

  Any color system such as L * a * b *, L * u * v *, Hunter L * a * b *, YCC, YUV, YIQ may be used in addition to HSV. It is desirable to use an HSV that can directly obtain a hue value and a saturation value.

As a reference example using a color system other than HSV, an example using L * a * b * will be described below.
The L * a * b * color system (CIE1976) is one of the uniform color spaces established by the CIE (International Commission on Illumination) in 1976, and is defined by IEC61966-2-1 [Equation 2 The L * a * b * value is obtained from the RGB value by applying the following [Equation 3] defined by JISZ8729. From the obtained a * b *, the hue value (H ′) and the saturation value (S ′) are obtained by the following [Equation 4]. However, the hue (H ′) and saturation (S ′) obtained here are different from the hue value (H) and saturation value (S) of the HSV color system described above.

The above [Equation 2] is converted from _8- bit input image data (R _{sRGB (8)} , G _{sRGB (8)} , B _{sRGB (8)} ) to tristimulus values (X, Y, Z) of color matching functions. Is shown. Here, the color matching function is a function indicating the spectral sensitivity distribution of the human eye. Here, _sRGB of input image data (R _{sRGB (8)} , G _{sRGB (8)} , B _{sRGB (8)} ) in [Equation 2] indicates that the RGB value of the input image data conforms to the sRGB standard, (8) indicates 8-bit (0 to 255) image data.

Further, the above [Expression 3] indicates that the tristimulus values (X, Y, Z) are converted into L * a * b *. Xn [Expression 3], Yn, Zn are X each standard white plate, Y, indicates Z, D ₆₅ represents a stimulus value when the color temperature is illuminated with light of 6500K to standard white plate. In [Formula 3], Xn = 0.95, Yn = 1.00, and Zn = 1.09.

  When the hue value and saturation value of each pixel of the input image data are acquired, the cumulative frequency distribution of the pixels in the coordinate plane with the hue value (H) as the X axis and the saturation value (S) as the Y axis is obtained. A two-dimensional histogram is generated (step S2).

  FIGS. 5A and 5B show examples of a two-dimensional histogram. The two-dimensional histogram shown in FIG. 5A represents a grid point having a cumulative frequency distribution value of pixels in a coordinate plane with a hue value (H) on the X axis and a saturation value (S) on the Y axis. It is a thing. The grid points at the edge of the coordinate plane hold the cumulative frequency of the number of pixels distributed in the range where the hue value (H) is 18 and the saturation value (S) is about 13. The remaining grid points hold the cumulative frequency of the number of pixels distributed in the range where the hue value (H) is 36 and the saturation value (S) is about 25. A region A represents a green hue region having a hue value (H) of 70 to 184. The boundary line B represents a low saturation threshold. A region having a saturation (S) smaller than the boundary line B of the region A is a range in which pixels are extracted as low saturation pixels.

  Next, the input image data is divided into predetermined hue regions based on the created two-dimensional histogram (step S3). Specifically, the created two-dimensional histogram is divided into two or more planes with at least one predefined hue value as a boundary, whereby the input image data is divided into predetermined hue regions. . In the present invention, it is desirable to divide input image data into four planes based on at least three hue values. Further, it is desirable to define the hue value as the boundary as 70, 185, 225 as calculated values by the above-mentioned HSV conversion program. By defining in this way, as shown in FIG. 5B, a two-dimensional histogram (input image data) is converted into a skin hue area (hue values 0 to 69), a green hue area (hue values 70 to 184), and sky. It is possible to divide into four hue areas, a hue area (hue values 185 to 224) and a red hue area (hue values 225 to 360). Also in the present embodiment, the two-dimensional histogram (input image data) is divided into four hue regions with hue values of 70, 185, and 225.

  Next, for each of the hue regions divided in step S3, the pixel of each hue region is the entire screen of the input image data by dividing the sigma value of the cumulative frequency distribution in each region by the total number of pixels of the input image data. Is calculated, that is, the occupation ratio for each hue region is calculated (step S4).

For example, as input image data, the total number of pixels is 5 million pixels, and the shooting scene is a relatively large image of a woman wearing a red cardigan sitting on the lawn (referred to as image data α). When the steps S1 to S4 described above are executed, the occupation ratio becomes a value shown in [Table 1] below.

  Next, a low saturation threshold value for each hue area is calculated according to the occupation ratio for each hue area obtained in step S4 (step S5). In this step, a saturation threshold for extracting low saturation pixels used in a gray balance adjustment condition calculation process described later (hereinafter referred to as “low saturation threshold value”) according to the occupation ratio for each hue region. Is set for each hue region.

A relational expression between the occupation ratio (= RC) of each hue region and the low saturation threshold value (= LC) is illustrated in [Expression 1] below. Note that the relational expression between the occupation ratio (= RC) and the low saturation threshold (= LC) of each hue region is not limited to this.
[Formula 1]
LC (S) = 30 × ((RC (%) / 100) ^0.25 × 30)

図５（ｂ）の２次元ヒストグラムの境界線Bは、画像データαについて求められた低彩度閾値を示している。 When the low saturation threshold value for each hue region of the image data α described above is obtained in step S5, the following [Table 2] results are obtained.

The boundary line B of the two-dimensional histogram in FIG. 5B indicates the low saturation threshold obtained for the image data α.

  Next, for each hue region, pixels used for gray balance adjustment are extracted based on the low saturation threshold calculated in step S5 (step S6). That is, a low saturation pixel to be used in a gray balance adjustment condition calculation process to be described later is determined using a low saturation threshold value calculated for each hue region, and is configured only with low saturation pixels (excluding the pixel itself, Intermediate image data that is masked with a value of 0 or 255) is generated.

  By executing Steps S1 to S6 described above, it is possible to prevent a hue bias in gray balance adjustment due to extraction of many low-saturation pixels from a region having a high ratio in the image, such as lawn or sky.

  In this process, not only the “low saturation threshold value” but also a relational expression between the occupancy rate of each hue region and the extraction rate of low saturation pixels is defined in advance, and the occupancy rate for each hue region is determined. Based on the low saturation threshold and the low saturation pixel extraction rate, the "low saturation pixel extraction rate" is calculated based on the low saturation pixel extraction rate calculation means according to claims 30 and 47. Low saturation pixels may be extracted.

  Next, a gray balance adjustment condition calculation process is executed with the pixel extracted in step S6 as the target pixel (step S7).

  FIG. 6 is a flowchart showing the gray balance adjustment condition calculation processing executed by the image adjustment processing unit 701 in step S7. Hereinafter, the gray balance adjustment condition calculation process will be described with reference to the flowchart of FIG. 6 and FIGS. 7 to 9.

  First, a cumulative histogram of each channel of R, G, and B is created for the target pixel for gray balance adjustment condition calculation (step S11). That is, the cumulative histogram shown in FIG. 7 is created for each of the R, G, and B channels.

  Next, as shown in FIG. 7, in the cumulative histogram of each channel of R, G, and B, the cumulative frequency of pixels from the highlight (255 for 8 bits) side to the shadow (0) side is the total low saturation pixel number. The average value of the accumulated pixels until the predetermined percentage is reached is calculated, and the average value of the accumulated pixels is determined as the highlight point (Hp) of each channel (step S12). Further, as shown in FIG. 7, in the cumulative histogram of each of the R, G, and B channels, the cumulative frequency of pixels from the shadow (0) side to the highlight (255 for 8 bits) is the total low saturation pixel number. The average value of the accumulated pixels until reaching a predetermined percentage of is calculated, and the average value of the accumulated pixels is determined as the shadow point (Sp) of each channel (step S13).

  Next, END (equivalent neutral density) conversion (equivalent neutral density conversion) pixels are extracted based on the determined highlight point Hp and shadow point Sp of each channel (step S14). Specifically, BG and RG are calculated for all the target pixels, and a chromaticity diagram with the BG on the X axis and the RG on the Y axis is created, and a highlight point is displayed on the chromaticity diagram. After Hp and shadow point Sp are plotted (see FIG. 8, point A is Hp, point B is Sp), pixels distributed within a predetermined distance from the line connecting them (pixels in region Q in FIG. 8) Are extracted as END conversion pixels. In this step, on the premise of an empirical rule that the highlight point Hp and the shadow point Sp have low saturation, low saturation pixels distributed in the vicinity on the chromaticity diagram are extracted as END conversion pixels. However, in the present embodiment, low saturation pixels extracted based on the occupation ratio of each hue in the entire image are used as target pixels. Even when the above empirical rule does not agree, such as a high shooting scene, it is possible to set an adjustment condition capable of performing gray balance adjustment without hue deviation.

  When the END conversion pixels are extracted, as shown in FIG. 9, a correlation diagram of x-axis = G value, y-axis = R value, and a correlation diagram of x-axis = G value, y-axis = B value are created. The extracted END conversion pixel values are plotted on each correlation diagram (step S15), and a least square approximation line l is set (step S16). Then, an END conversion equation for correcting the set least square approximation straight line l so that the slope is 1 and the intercept is 0 is calculated (step S17), and the process proceeds to step S8 in FIG.

  In FIG. 4, when the END conversion formula as the gray balance adjustment condition is calculated, the calculated END conversion formula is applied to the input image data to perform gray balance adjustment (step S8). finish.

  As described above, according to the image recording apparatus 1, the hue value and the saturation value of the input image data are acquired, and the X axis is the hue value (H) and the Y axis is the saturation value (S). Create a two-dimensional histogram showing the cumulative frequency distribution of pixels in a plane, divide the two-dimensional histogram into predetermined hue areas, and the ratio of pixels in each area to the entire input image data for each divided hue area (Occupancy ratio) is calculated, a low saturation threshold value for each hue area is calculated based on the calculated occupation ratio, and low saturation pixels used for gray balance adjustment condition calculation are extracted based on the low saturation threshold value. To do. Then, a gray balance adjustment condition is calculated using the extracted low saturation pixels, and the gray balance adjustment is performed on the input image data under the calculated condition.

  Therefore, by determining the coloration tendency of the shooting scene and changing the threshold value setting for extracting low saturation pixels based on the determination result, low saturation pixels can be detected from areas with high occupancy such as lawn and sky. The unevenness of hue due to the extraction is prevented, and as a result, color feria is suppressed.

[Second Embodiment]
Next, a second embodiment of the present invention will be described.
The configuration of the image recording apparatus 1 according to the present embodiment is the same as that shown in FIG.

The operation of the second embodiment will be described below.
FIG. 10 is a flowchart showing the gray balance adjustment process B executed by the image adjustment processing unit 701. This processing is realized by software processing in cooperation with the gray balance adjustment processing B program stored in a storage unit (not shown) such as a ROM and the CPU, and includes a film scan data processing unit 702, reflection The process starts when image data (image signal) is input from the document scan data processing unit 703 or the image data format decoding processing unit 704. By executing this gray balance adjustment processing B, the data acquisition means, hue area dividing means, hue area occupancy rate calculating means, application rate calculating means, gray balance adjusting means according to claims 19, 20, 36, and 37 of the present invention And the two-dimensional histogram creation means of Claims 21 and 38 is implement | achieved.

Hereinafter, the gray balance adjustment process B will be described with reference to FIG.
When image data is input from the film scan data processing unit 702, the reflection original scan data processing unit 703, or the image data format decoding processing unit 704, the input image data is converted into L * a * b * or HSV from the RGB color system. Are converted into the color system, the hue value and the saturation value for each pixel of the input image data are calculated and stored in a RAM (not shown) (step S21). As a calculation formula for calculating the hue value and the saturation value from the RGB value of each pixel, for example, [Expression 1], [Expression 2] to [Expression 4] described in the first embodiment are used. Use.

  When the hue value and saturation value of each pixel of the image data are calculated, the cumulative frequency distribution of the pixels is shown in the coordinate plane with the hue value (H) as the X axis and the saturation value (S) as the Y axis. A two-dimensional histogram is created (step S22). The two-dimensional histogram created here is, for example, a histogram similar to that described with reference to FIGS.

  Next, the input image data is divided into predetermined hue areas based on the created two-dimensional histogram (step S23). Specifically, the created two-dimensional histogram is divided into two or more planes with at least one predefined hue value as a boundary, whereby the input image data is divided into predetermined hue regions. . In the present invention, it is desirable to divide input image data into four planes based on at least three hue values. Further, it is desirable to define the hue value as the boundary as 70, 185, 225 as calculated values by the above-mentioned HSV conversion program. By defining in this way, as shown in FIG. 5B, a two-dimensional histogram (input image data) is converted into a skin hue area (hue values 0 to 69), a green hue area (hue values 70 to 184), and sky. It is possible to divide into four hue areas, a hue area (hue values 185 to 224) and a red hue area (hue values 225 to 360). Also in the present embodiment, the two-dimensional histogram (image data) is divided into four hue regions with hue values of 70, 185, and 225.

  Next, for each of the hue areas divided in step S23, the ratio of each hue area in the screen of the input image is obtained by dividing the sigma value of the cumulative frequency distribution in each area by the total number of pixels of the input image data. That is, the occupation ratio for each hue area is calculated (step S24).

For example, as input image data, the total number of pixels is 5 million pixels, and the shooting scene is a relatively large image of a woman wearing a red cardigan sitting on the lawn (referred to as image data α). When the steps S21 to S24 described above are executed, the occupation ratio becomes a value shown in [Table 3] below.

Next, the application rate of gray balance adjustment is calculated according to the occupancy ratio obtained for each hue area in step S24, specifically, according to the maximum occupancy ratio among the occupancy ratios determined for each hue area. (Step S25). The application rate of gray balance adjustment refers to the conversion rate of input image data according to the END conversion formula calculated in the gray balance adjustment condition calculation process described later. As a calculation method of the application rate of gray balance adjustment, for example, when the maximum occupancy rate is RCmax and the application rate is AR among the occupancy rates obtained for each hue region, it can be obtained by the following [Equation 2].
[Formula 2]
AR (%) = 100-RCmax (%)

  It is desirable that the above [Expression 2] is not uniform and a different expression is set according to the hue region. In particular, it is desirable to set the skin hue area and the sky hue area so that the application rate (AR (%)) for the same occupancy is lower than the other hue areas. For example, when the occupancy ratio of the skin hue area exceeds 70%, there is a high possibility of photographing with a warm-colored artificial light source such as tungsten in addition to face up. In addition, even when the sky hue area occupancy is high, there is a high possibility of shooting an airplane or an aquarium tank. In such a shooting scene, the rule of thumb that the highlight point is low saturation does not match, and excessive color deviation correction is performed in gray balance adjustment, resulting in color feria. It is desirable to set the application rate of the hue area low in advance.

  Next, gray balance adjustment condition calculation processing is executed with all pixels of the input image data as target pixels (step S26). The gray balance adjustment condition calculation process is performed by the same process as described with reference to FIG. When the END conversion formula is calculated by this process, the END conversion formula is applied to the input image data based on the application rate calculated in step S25, and gray balance adjustment is performed (step S27). That is, when the END conversion formula is applied to the input image data, the difference (ΔRGB) between the image data before and after the application is compressed based on the application rate (AR).

  As described above, according to the image recording apparatus 1, the hue value and the saturation value of the input image data are acquired, and the X axis is the hue value (H) and the Y axis is the saturation value (S). Create a two-dimensional histogram showing the cumulative frequency distribution of pixels in a plane, divide the two-dimensional histogram into predetermined hue areas, and the ratio of pixels in each area to the entire input image data for each divided hue area (Occupancy rate) is calculated, and an application rate of gray balance adjustment is calculated based on the calculated occupancy rate. Then, an END conversion formula used for gray balance adjustment is calculated from the input image data, the END conversion formula is applied to the input image data based on the calculated application rate, and gray balance adjustment is performed on the input image data.

  Therefore, by determining the coloration tendency of the shooting scene and changing the application rate of the gray balance adjustment based on the determination result, excessive color deviation correction of artificial light sources such as tungsten and self-luminous light sources is prevented, As a result, color feria is suppressed.

  In the first and second embodiments, the average saturation for each hue region may be obtained, and the above-described low saturation threshold and application rate may be set according to the average saturation. When the average saturation is high, the low saturation threshold and the application rate of gray balance adjustment are reduced, for example, excessive color deviation in a scene with high overall saturation such as tungsten as the light source. Correction can be prevented.

  Further, it is preferable that the extraction of the low saturation pixels used in the gray balance adjustment condition calculation in the first embodiment and the setting of the application rate of the gray balance adjustment in the second embodiment are used in combination. As a method to be used in combination, steps S1 to S7 in FIG. 4 described above are executed to calculate an END conversion formula as a gray balance adjustment condition, and when the occupation ratio is calculated, a gray balance adjustment application rate is calculated. Based on the calculated application rate, the gray balance adjustment processing is performed by applying the calculated END conversion formula to the input image data. As a result, it is possible to further improve the accuracy of color feria suppression as compared with the case where the gray balance adjustment processing A in the first embodiment and the gray balance adjustment processing B in the second embodiment are each executed independently. It becomes.

[Third Embodiment]
Next, a third embodiment of the present invention will be described.
The configuration of the image recording apparatus 1 according to the present embodiment is the same as that shown in FIG.

Hereinafter, the operation of the third embodiment will be described.
Next, the operation of the present invention will be described.
FIG. 11 is a flowchart showing the gray balance adjustment process C executed by the image adjustment processing unit 701. This processing is realized by software processing in cooperation with the CPU and the gray balance adjustment processing C program stored in a storage unit (not shown) such as a ROM, and includes a film scan data processing unit 702, reflection The process starts when image data (image signal) is input from the document scan data processing unit 703 or the image data format decoding processing unit 704. By executing this gray balance adjustment processing C, the data acquisition means, hue area division means, hue area occupancy rate calculation means, low saturation threshold value calculation means, low saturation, and the like according to claims 22, 24, 39, and 41 of the present invention. 43. A degree pixel extracting means, a gray balance adjustment condition calculating means, a brightness area dividing means, a brightness area occupancy rate calculating means, a shooting scene estimating means, a gray balance adjusting means, and a three-dimensional histogram creating means according to claim 25 and 42 are realized. Is done.

Hereinafter, the gray balance adjustment processing C will be described with reference to FIG.
When image data is input from the film scan data processing unit 702, the reflection original scan data processing unit 703, or the image data format decoding processing unit 704, the input image data is converted into L * a * b * or HSV from the RGB color system. Are converted into the color system, the hue value, saturation value, and brightness value for each pixel of the input image data are calculated and stored in a RAM (not shown) (step S31). As a calculation formula for calculating the hue value and the saturation value from the RGB value of each pixel, for example, [Expression 1], [Expression 2] to [Expression 4] described in the first embodiment are used. Use.

  When the hue value, saturation value, and brightness value of each pixel of the input image data are calculated, the x axis is the hue value (H), the y axis is the saturation value (S), and the z axis is the brightness value (V). A three-dimensional histogram showing the cumulative frequency distribution of the pixels is created in the coordinate space (step S32).

  FIGS. 12A and 12B show an example of a three-dimensional histogram. The three-dimensional histograms shown in FIGS. 12 (a) and 12 (b) have pixel values in a coordinate space with the hue value (H) on the x axis, the saturation value (S) on the y axis, and the lightness (V) on the z axis. This is a representation of grid points having a cumulative frequency distribution value. The grid points on the surface of the coordinate space hold the cumulative frequency of the number of pixels distributed in the range where the hue value (H) is 18, the saturation value (S), and the brightness (V) are both about 13. The other grid points hold the cumulative frequency of the number of pixels distributed in the range where the hue value (H) is 36, the saturation value (S), and the brightness (V) are both about 25. FIG. 12B shows lattice points distributed in the green hue region having a hue (H) of 70 to 184.

  Next, the input image data is divided into predetermined hue regions based on the created three-dimensional histogram (step S33). Specifically, the generated three-dimensional histogram is divided into two or more spaces with at least one predefined hue value as a boundary, whereby the input image data is divided into predetermined hue regions. . In the present invention, it is desirable to divide input image data into four spaces based on at least three hue values. Further, it is desirable to define the hue value as the boundary as 70, 185, 225 as calculated values by the above-mentioned HSV conversion program. By defining in this way, a three-dimensional histogram (input image data) is converted into a skin hue area (hue values 0 to 69), a green hue area (hue values 70 to 184), a sky hue area (hue values 185 to 224), It becomes possible to divide into four hue areas of the red hue area (hue values 225 to 360). Also in this embodiment, it is assumed that the three-dimensional histogram (input image data) is divided into four hue regions with hue values of 70, 185, and 225.

  Next, for each of the hue areas divided in step S33, the ratio of each hue area in the screen of the input image is obtained by dividing the sigma value of the cumulative frequency distribution in each area by the total number of pixels of the input image data. That is, the occupation ratio for each hue area is calculated (step S34).

For example, as input image data, the total number of pixels is 5 million pixels, and the shooting scene is a relatively large image of a woman wearing a red cardigan sitting on the lawn (referred to as image data α). When Steps S31 to S34 described above are executed, the occupation ratio becomes a value shown in [Table 4] below.

  Next, a low saturation threshold (extraction condition) for each hue area is calculated according to the occupation ratio of each hue area obtained in step S34 (step S35). This step is a process for setting, for each hue area, a low saturation threshold used in a gray balance adjustment condition calculation process described later according to the occupation ratio for each hue area.

  The relational expression between the occupation ratio (= RC) of each hue region and the low saturation threshold (= LC) is the same as [Expression 1] described above. Note that the relational expression between the occupation ratio (= RC) and the low saturation threshold (= LC) of each hue region is not limited to this.

When the low saturation threshold value of each hue region of the image data α described above is obtained in step S35, the following [Table 5] result is obtained.

  Next, for each hue region, pixels used for gray balance adjustment are extracted based on the low saturation threshold calculated in step S35 (step S36). That is, a low saturation pixel to be used in a gray balance adjustment condition calculation process to be described later is determined using a low saturation threshold value calculated for each hue region, and is configured only with low saturation pixels (excluding the pixel itself, Intermediate image data that is masked with a value of 0 or 255) is generated.

  By executing the above-described steps S31 to S36, hue bias in gray balance adjustment due to extraction of many low-saturation pixels from a region having a high ratio in the image, such as lawn and sky, is prevented.

  In addition to the “low saturation threshold value”, the “low saturation pixel extraction rate” may be set for each hue region.

  Next, gray balance adjustment condition calculation processing is executed using the intermediate image data extracted in step S36, and an END conversion formula is calculated (step S37). Since the gray balance adjustment condition calculation process is the same as that described in the first embodiment, description thereof is omitted (see FIG. 6).

  Next, the input image data is divided into predetermined brightness regions based on the created three-dimensional histogram (step S38). Specifically, the created three-dimensional histogram is divided into two or more spaces with at least one predefined brightness value as a boundary, whereby the input image data is divided into predetermined brightness areas. . In the present invention, it is desirable to divide the three-dimensional histogram into three spaces by at least two lightness values. Further, it is desirable to define the brightness value as the boundary as 85 and 170 calculated by the above-mentioned HSV conversion program. By defining in this way, the three-dimensional histogram (input image data) is divided into three brightness areas: shadow (brightness values 0 to 84), intermediate (brightness values 85 to 169), and highlight (brightness values 170 to 255). It becomes possible to divide into. Also in the present embodiment, it is assumed that the three-dimensional histogram (input image data) is divided into three brightness regions (shadow, middle, highlight) with brightness values of 85 and 170.

  Next, for each of the brightness areas divided in step S38, the ratio of each brightness area in the screen of the input image is obtained by dividing the sigma value of the cumulative frequency distribution in each area by the total number of pixels of the input image data. That is, the occupation ratio for each brightness area is calculated (step S39).

For example, as input image data, the total number of pixels is 5 million pixels, and the shooting scene is a relatively large image of a woman wearing a red cardigan sitting on the lawn (referred to as image data α). When step S38, 39 mentioned above is performed, the occupation rate of each brightness area will be the value shown in the following [Table 6].

When the occupation ratio for each brightness area is calculated, a shooting scene of the input image data is estimated according to the occupation ratio (step S40). For example, backlight or strobe close-up photography is estimated based on the size relationship between the shadow, intermediate, and highlight areas. The occupancy ratios of the shadow, middle, and highlight areas are Rs, Rm, and Rh, respectively, and the definition of the relationship between the magnitude relationship and the shooting scene is exemplified below as [Definition 1].
[Definition 1]
Backlight scene… Rs> Rm, Rh> Rm
Strobe close-up photography… Rh> Rs, Rh> Rm
All other scenes are normal scenes.

In step S38, each of the three-dimensional histograms divided into at least two hue regions in step S33 may be divided into predetermined lightness regions (hue lightness region division according to claims 26 and 43). means). When the hue value that becomes the boundary of division is defined as 70, 185, 225, and the lightness value that becomes the boundary of division is defined as 85, 170, calculated by the HSV conversion program described above, for each hue area using image data α When the brightness division is performed, the occupation ratio for each area shown in the following [Table 7] is obtained in step S39 (hue lightness area occupation ratio calculating means according to claims 26 and 43).

At this time, using the three-dimensional histogram divided into 12 as described above, the estimation of the photographic scene may be assisted from the occupancy ratio of the shadow, middle, and highlight areas in the skin hue area. When the occupancy rates of the shadow, middle, and highlight areas are Rs, Rm, and Rh, respectively, and the occupancy ratios of the shadow, middle, and highlight areas are SkRs, SkRm, and SkRh, respectively. The definition of the relationship with the scene is exemplified below as [Definition 2].
[Definition 2]
Backlit scene… Rs> Rm, Rh> Rm, SkRs>SkRm> SkRh
Strobe close-up photography… Rh> Rs, Rh> Rm, SkRh>SkRm> SkRs
Other than the above, it is estimated that all are normal scenes.

In addition, the brightness deviation amount of the skin color area may be calculated separately to assist the estimation of the shooting scene. Examples are given below.
Using the average brightness value (= A) of the skin color area, the maximum brightness (= B), and the minimum brightness (= C) of the entire image, the brightness deviation amount (= D) of the skin color area is obtained from [Equation 3] below. [Definition 3] below shows an example of a definition for estimating a shooting scene based on the magnitude relationship between the lightness deviation amount (= D) of the flesh color area, the occupation ratio for each hue area, and the occupation ratio of the brightness area.
[Formula 3]
D = (AB) / (CB)
[Definition 3]
D> 0.6 ... Backlit scene.
D <0.4 + Green hue area less than 15%, Sky hue area less than 30% ... Flash close-up scene.
0.4 ≤ D ≤ 0.6 + Green hue area less than 15% and sky hue area less than 30% + Rs20% or more ... Flash photography scene.
0.4 ≤ D ≤ 0.6 + 15% or more of the green hue area or 30% or more of the sky hue area + Rh> Rm ... Backlight scene.
All other scenes are normal scenes.

When the shooting scene of the input image data is estimated, the low saturation threshold value calculated in step S35 is changed in consideration of the shooting scene estimation result, and the gray balance adjustment condition is corrected based on this change. (Step S41). The following [Table 8] shows an example of changing the low saturation threshold according to the photographic scene estimation result.

  Backlit photography is a scene where the subject is photographed with the sun behind, so the sky hue region with high brightness occupies a high ratio. For this reason, the above-described low saturation threshold value is changed in order to increase the accuracy of preventing the highlight point from being erroneously set in the sky hue region. Further, in the flash close-up shooting, since the flash shooting is mostly performed close to a person, the ratio of the skin hue region with high brightness is high. For this reason, the above-described low saturation threshold is changed in order to increase the accuracy of preventing the highlight point from being erroneously set in the skin hue region.

  When the gray balance adjustment condition is corrected according to the shooting scene estimation result, that is, when an END conversion formula corresponding to the shooting scene estimation result is newly calculated, the calculated END conversion formula is applied to the input image data. As a result, the gray balance adjustment based on the photographed scene estimation result is performed (step S42), and the present process ends.

  As described above, according to the image recording apparatus 1, the hue value, the saturation value, and the brightness value of the input image data are acquired, the x-axis is the hue value (H), and the y-axis is the saturation value (S). , Create a three-dimensional histogram showing the cumulative frequency distribution of pixels in the coordinate space with the z-axis lightness value (V), and divide the input image data into predetermined hue regions using this three-dimensional histogram. For each hue area, the ratio (occupancy ratio) of the pixels in each area to the entire input image data is calculated, and the low saturation threshold value for each hue area is calculated based on the calculated occupation ratio. Based on the threshold value, low saturation pixels used for gray balance adjustment condition calculation are extracted. Then, a gray balance adjustment condition is calculated using the extracted low saturation pixel. Further, the input image data is divided into predetermined brightness areas using a three-dimensional histogram, and the ratio (occupancy ratio) of the pixels in each area to the entire input image data is calculated for each divided brightness area. The shooting scene of the input image data is estimated based on the occupancy ratio. Then, the gray balance adjustment condition is corrected based on the estimation result of the shooting scene, and the gray balance adjustment is performed on the input image data under the corrected condition.

  Therefore, by determining the coloration tendency of the shooting scene and changing the threshold value setting for extracting low saturation pixels based on the determination result, low saturation pixels can be detected from areas with high occupancy such as lawn and sky. The unevenness of hue due to the extraction is prevented, and as a result, color feria is suppressed. Also, by determining the brightness distribution tendency of the photographic scene and estimating the photographic scene such as backlight or strobe proximity, erroneous setting of the highlight point can be prevented, and the accuracy of color feria suppression can be further improved.

[Fourth Embodiment]
Next, a fourth embodiment of the present invention will be described.
The configuration of the image recording apparatus 1 according to the present embodiment is the same as that shown in FIG.

The operation of the fourth embodiment will be described below.
Next, the operation of the present invention will be described.
FIG. 13 is a flowchart showing the gray balance adjustment process D executed by the image adjustment processing unit 701. This processing is realized by software processing in cooperation with the gray balance adjustment processing D program stored in a storage unit (not shown) such as a ROM and the CPU. The film scan data processing unit 702, reflection The process starts when image data (image signal) is input from the document scan data processing unit 703 or the image data format decoding processing unit 704. By executing this gray balance adjustment processing D, the data acquisition means, hue area dividing means, hue area occupation ratio calculating means, application rate calculating means, and brightness area dividing means according to claims 23, 24, 40, and 41 of the present invention The brightness area occupancy rate calculating means, the shooting scene estimating means, the gray balance adjusting means, and the three-dimensional histogram creating means according to claims 25 and 42 are realized.

Hereinafter, the gray balance adjustment processing D will be described with reference to FIG.
When image data is input from the film scan data processing unit 702, the reflection original scan data processing unit 703, or the image data format decoding processing unit 704, the input image data is converted into L * a * b * or HSV from the RGB color system. Are converted into the color system, the hue value, saturation value, and brightness value for each pixel of the input image data are calculated and stored in a RAM (not shown) (step S51). As a calculation formula for calculating the hue value and the saturation value from the RGB value of each pixel, for example, [Expression 1], [Expression 2] to [Expression 4] described in the first embodiment are used. Use.

  When the hue value, saturation value, and brightness value of each pixel of the input image data are calculated, the x axis is the hue value (H), the y axis is the saturation value (S), and the z axis is the brightness value (V). A three-dimensional histogram showing the cumulative frequency distribution of the pixels is created in the coordinate space (step S52). The three-dimensional histogram created here is, for example, the same histogram as described in FIG.

  Next, the input image data is divided into predetermined hue regions based on the created three-dimensional histogram (step S53). Specifically, the generated three-dimensional histogram is divided into two or more spaces with at least one predefined hue value as a boundary, whereby the input image data is divided into predetermined hue regions. . In the present invention, it is desirable to divide input image data into four spaces based on at least three hue values. Further, it is desirable to define the hue value as the boundary as 70, 185, 225 as calculated values by the above-mentioned HSV conversion program. By defining in this way, a three-dimensional histogram (input image data) is converted into a skin hue area (hue values 0 to 69), a green hue area (hue values 70 to 184), a sky hue area (hue values 185 to 224), It becomes possible to divide into four hue areas of the red hue area (hue values 225 to 360). Also in this embodiment, it is assumed that the three-dimensional histogram (input image data) is divided into four hue regions with hue values of 70, 185, and 225.

  Next, for each of the hue areas divided in step S53, the ratio of each hue area in the screen of the input image is obtained by dividing the sigma value of the cumulative frequency distribution in each area by the total number of pixels of the input image data. That is, the occupation ratio of each hue area is calculated (step S54).

  Next, the application rate of gray balance adjustment for each hue area is calculated according to the occupation ratio of each hue area obtained in step S54 (step S55). The application rate of gray balance adjustment refers to the conversion rate of input image data according to the END conversion formula calculated in the gray balance adjustment condition calculation process described later. As a method of calculating the application rate of the gray balance adjustment, for example, it can be obtained by [Expression 2] described above.

  It is desirable that the above [Formula 2] is set differently depending on the hue region having the maximum occupancy instead of being uniform. In particular, in the skin hue area and the sky hue area, it is desirable to set the reduction rate of the application rate (AR (%)) high. For example, when the occupancy ratio of the skin hue area exceeds 70%, there is a high possibility of photographing with a warm-colored artificial light source such as tungsten in addition to face up. In addition, even when the sky hue area occupancy is high, there is a high possibility of shooting an airplane or an aquarium tank. In such a shooting scene, the rule of thumb that the highlight point is low saturation does not match, and excessive color deviation correction is performed in gray balance adjustment, resulting in color failure, so the application rate of It is desirable to set the lowering range high.

  Next, a gray balance adjustment condition calculation process is executed using the pixel value of the input image data (step S56). The gray balance adjustment condition calculation process is performed by the same process as described with reference to FIG.

  Next, the input image data is divided into predetermined brightness areas based on the created three-dimensional histogram (step S57). Specifically, the created three-dimensional histogram is divided into two or more spaces with at least one predefined brightness value as a boundary, whereby the input image data is divided into predetermined brightness areas. . In the present invention, it is desirable to divide the three-dimensional histogram into three spaces by at least two lightness values. In addition, it is desirable to define the brightness value as the boundary as 85 and 170 calculated by the above-mentioned HSV conversion program. By defining in this way, the three-dimensional histogram (input image data) is divided into three brightness areas: shadow (brightness values 0 to 84), intermediate (brightness values 85 to 169), and highlight (brightness values 170 to 255). It becomes possible to divide into. Also in the present embodiment, it is assumed that the three-dimensional histogram (input image data) is divided into three brightness regions (shadow, middle, highlight) with brightness values of 85 and 170.

  Next, for each of the brightness areas divided in step S57, the ratio of each brightness area in the screen of the input image is obtained by dividing the sigma value of the cumulative frequency distribution in each area by the total number of pixels of the input image data. That is, the occupation ratio of each brightness area is calculated (step S58).

For example, as input image data, the total number of pixels is 5 million pixels, and the shooting scene is a relatively large image of a woman wearing a red cardigan sitting on the lawn (referred to as image data α). When steps S57 and S58 described above are executed, the occupancy ratio of each lightness area becomes a value shown in [Table 9] below.

  When the occupancy rate of each lightness area is calculated, the shooting scene of the input image data is estimated according to the occupancy rate (step S59). As a method for estimating the shooting scene, for example, the method described in step S40 of the gray balance adjustment process C in the third embodiment described above can be used. In addition, each of the three-dimensional histograms divided into at least two spaces in step S53 is divided into predetermined brightness areas, and the shooting scene is estimated from the occupancy ratios of the shadow, middle, and highlight areas in the skin hue area. You may help. In addition, the brightness deviation amount of the skin color area may be calculated separately to assist the estimation of the shooting scene.

When the shooting scene of the input image data is estimated, the application rate for each hue area calculated in step S55 is reviewed based on the estimation result of the shooting scene, and if necessary, the application rate is newly set. The END conversion formula is applied to the input image data based on the application rate calculated for the hue area with the largest occupation ratio, and gray balance adjustment based on the estimation result of the shooting scene is performed. (Step S60). [Table 10] below shows an example of reviewing the application rate based on the estimation result of the shooting scene.

  As described above, according to the image recording apparatus 1, the hue value, the saturation value, and the brightness value of the input image data are acquired, the x-axis is the hue value (H), and the y-axis is the saturation value (S). , Create a three-dimensional histogram showing the cumulative frequency distribution of pixels in the coordinate space with the z-axis lightness value (V), and divide the input image data into predetermined hue regions using this three-dimensional histogram. For each hue area, the ratio (occupancy ratio) of the pixels in each area to the entire input image data is calculated, and the application ratio of gray balance adjustment is calculated for each hue area based on the calculated occupation ratio. Further, the input image data is divided into predetermined brightness areas using a three-dimensional histogram, and the ratio (occupancy ratio) of the pixels in each area to the entire input image data is calculated for each divided brightness area. The shooting scene of the input image data is estimated based on the occupancy ratio. Then, the application rate of the gray balance adjustment calculated for each hue region is corrected based on the estimation result of the photographing scene, and the gray balance adjustment is performed on the input image data with the application rate of the hue region having the maximum occupation ratio.

  Therefore, by determining the coloration tendency of the shooting scene and changing the application rate of the gray balance adjustment based on the determination result, excessive color deviation correction of artificial light sources such as tungsten and self-luminous light sources is prevented, As a result, color feria is suppressed. Further, by determining the brightness distribution tendency of the shooting scene, it is possible to adjust the application rate of gray balance adjustment according to the shooting scene.

  Note that the processes in the third embodiment and the fourth embodiment are preferably used in combination. For example, steps S31 to S37 in FIG. 11 are executed to calculate the END conversion formula as the gray balance adjustment condition, and the application rate of the gray balance adjustment is calculated when the occupation ratio of each hue region is calculated. In addition, by executing steps S38 to S40 in FIG. 11, the shooting scene is estimated, the low saturation threshold and the application rate are reviewed according to the estimation result of the shooting scene, and corrected as necessary. The gray balance adjustment is performed on the input image data using the gray balance adjustment condition and the application rate. As a result, it is possible to further increase the accuracy of color feria suppression compared to when the gray balance adjustment process C in the third embodiment and the gray balance adjustment process D in the fourth embodiment are executed individually. It becomes.

  Although the first to fourth embodiments have been described above, the image data used in these embodiments is preferably scene reference image data in the case of an image taken with a digital camera. In the scene reference image data, the signal intensity of each color channel based on at least the spectral sensitivity of the image sensor itself has been mapped to a standard color space such as RIMM RGB or ERIMM RGB, and tone conversion, sharpness enhancement, saturation enhancement, etc. This means image data in a state where image processing for modifying the data content is omitted in order to improve the effect at the time of image viewing. Accordingly, the scene reference image data is input to the image recording apparatus 1, and the image adjustment processing unit 701 uses the output destination information input from the operation unit 11 on the output medium (CRT, liquid crystal display, plasma display, silver display). In order to obtain an optimal viewing image on salt-printed paper, inkjet paper, thermal printer paper, etc.), it is converted into viewing image reference data by performing optimization processing including the gray balance adjustment described above. It is possible to form the appreciation image reference data optimized without any information loss on the output medium.

In addition, the description content in each said embodiment is a suitable example of this invention, and is not limited to this.
For example, in the above embodiment, the image recording apparatus having a function of performing image processing on input image data and recording on the output medium has been described as an example. However, the present invention performs image processing on input image data. Needless to say, the present invention can also be applied to an image processing apparatus that outputs an image to an image recording apparatus.

  Further, in each of the above embodiments, the captured image data is divided into the hue area and the brightness area using the two-dimensional histogram and the three-dimensional histogram, but the obtained hue value is used without using these histograms. The captured image data may be divided based on the brightness value and the saturation value. By using the two-dimensional histogram and the three-dimensional histogram, the processing can be made efficient.

  In addition, the detailed configuration and detailed operation of the image recording apparatus 1 can be changed as appropriate without departing from the spirit of the present invention.

1 is a perspective view showing an external configuration of an image recording apparatus 1 according to the present invention. FIG. 2 is a block diagram illustrating an internal configuration of the image recording apparatus 1 in FIG. 1. It is a block diagram which shows the functional structure of the image process part 70 of FIG. 4 is a flowchart showing a gray balance adjustment process A executed by an image adjustment processing unit 701 in FIG. 3. (A) is a figure which shows an example of a two-dimensional histogram, (b) is a figure which shows the example which divided | segmented the two-dimensional histogram into the hue area | region. 4 is a flowchart showing gray balance adjustment condition calculation processing executed by an image adjustment processing unit 701 in FIG. 3. It is a figure which shows an example of the accumulation histogram created by step S11 of FIG. It is a figure which shows an example of the chromaticity diagram produced by step S14 of FIG. FIG. 7 is a diagram illustrating an example of a G / R correlation diagram and a G / Y correlation diagram created in step S <b> 15 of FIG. 6. 4 is a flowchart showing a gray balance adjustment process B executed by an image adjustment processing unit 701 in FIG. 3. 4 is a flowchart showing gray balance adjustment processing C executed by an image adjustment processing unit 701 in FIG. 3. (A) is a figure which shows an example of a three-dimensional histogram, (b) is a figure showing the grid point distributed in the green hue area | region whose hue (H) in the three-dimensional histogram of (a) is 70-184. is there. 4 is a flowchart showing a gray balance adjustment process D executed by the image adjustment processing unit 701 in FIG. 3.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Image recording apparatus 2 Case 3 Magazine loading part 4 Exposure processing part 5 Print preparation part 7 Control part 8 CRT
DESCRIPTION OF SYMBOLS 9 Film scanner part 10 Reflected original input device 11 Operation part 12 Information input means 14 Image reading part 15 Image writing part 30 Image transfer means 31 Image conveying part 32 Communication means (input)
33 Communication means (output)
51 External Printer 70 Image Processing Unit 701 Image Adjustment Processing Unit 702 Film Scan Data Processing Unit 703 Reflected Original Scan Data Processing Unit 704 Image Data Format Decoding Processing Unit 705 Template Processing Unit 706 CRT Specific Processing Unit 707 Print Specific Processing Unit A
708 Print unique processing section B
709 Image data creation processing unit 71 Data storage unit 72 Template storage unit

Claims (51)

In an image processing method for inputting captured image data and outputting image data optimized for viewing on an output medium,
Obtaining a hue value and a saturation value for each pixel of the captured image data;
Dividing the captured image data into at least two hue regions;
Calculating an occupancy ratio indicating a ratio of pixels for each of the divided hue regions to the entire screen of the captured image data;
Calculating a low saturation threshold value for each hue area according to the calculated occupancy ratio for each hue area;
Extracting low saturation pixels used for gray balance adjustment based on the calculated low saturation threshold;
Calculating a gray balance adjustment condition using the extracted low saturation pixel;
Applying gray balance adjustment to the captured image data based on the calculated gray balance adjustment condition;
An image processing method comprising: In an image processing method for inputting captured image data and outputting image data optimized for viewing on an output medium,
Obtaining a hue value and a saturation value for each pixel of the captured image data;
Dividing the captured image data into at least two hue regions;
Calculating an occupancy ratio indicating a ratio of pixels for each of the divided hue regions to the entire screen of the captured image data;
Calculating a gray balance adjustment application rate according to the calculated occupancy for each hue region;
Applying gray balance adjustment based on the calculated application rate;
An image processing method comprising: In an image processing method for inputting captured image data and outputting image data optimized for viewing on an output medium,
Obtaining a hue value and a saturation value for each pixel of the captured image data;
Dividing the captured image data into at least two hue regions;
Calculating an occupancy ratio indicating a ratio of pixels for each of the divided hue regions to the entire screen of the captured image data;
Calculating a low saturation threshold value for each hue area according to the calculated occupancy ratio for each hue area;
Extracting low saturation pixels used for gray balance adjustment based on the calculated low saturation threshold;
Calculating a gray balance adjustment condition using the extracted low saturation pixel;
Calculating a gray balance adjustment application rate according to the calculated occupancy for each hue region;
Applying gray balance adjustment to the captured image data based on the calculated gray balance adjustment condition and application rate;
An image processing method comprising: Creating a two-dimensional histogram of the acquired hue and saturation values;
4. The step of dividing the captured image data into at least two hue regions divides the captured image data into at least two hue regions based on the created two-dimensional histogram. An image processing method according to any one of the above. In an image processing method for inputting captured image data and outputting image data optimized for viewing on an output medium,
Obtaining a hue value, a saturation value, and a brightness value for each pixel of the captured image data;
Dividing the captured image data into at least two hue regions;
Calculating an occupancy ratio indicating a ratio of pixels for each of the divided hue regions to the entire screen of the captured image data;
Calculating a low saturation threshold value for each hue area according to the calculated occupancy ratio for each hue area;
Extracting low saturation pixels used for gray balance adjustment based on the calculated low saturation threshold;
Calculating a gray balance adjustment condition using the extracted low saturation pixel;
Dividing the captured image data into at least two brightness regions;
Calculating an occupancy ratio indicating a ratio of pixels of the divided brightness areas to the entire screen of the captured image data;
Estimating a captured scene of the captured image data according to the calculated occupancy for each brightness region;
Applying gray balance adjustment to the captured image data based on the calculated gray balance adjustment condition and the estimation result of the shooting scene;
An image processing method comprising: In an image processing method for inputting captured image data and outputting image data optimized for viewing on an output medium,
Obtaining a hue value, a saturation value, and a brightness value for each pixel of the captured image data;
Dividing the captured image data into at least two hue regions;
Calculating an occupancy ratio indicating a ratio of pixels for each of the divided hue regions to the entire screen of the captured image data;
Calculating a gray balance adjustment application rate according to the calculated occupancy for each hue region;
Dividing the captured image data into at least two brightness regions;
Calculating an occupancy ratio indicating a ratio of pixels of the divided brightness areas to the entire screen of the captured image data;
Estimating a captured scene of the captured image data according to the calculated occupancy for each brightness region;
Applying gray balance adjustment to the captured image data based on the calculated application rate and the estimation result of the shooting scene;
An image processing method comprising: In an image processing method for inputting captured image data and outputting image data optimized for viewing on an output medium,
Obtaining a hue value, a saturation value, and a brightness value for each pixel of the captured image data;
Dividing the captured image data into at least two hue regions;
Calculating an occupancy ratio indicating a ratio of pixels for each of the divided hue regions to the entire screen of the captured image data;
Calculating a low saturation threshold value for each hue area according to the calculated occupancy ratio for each hue area;
Extracting low saturation pixels used for gray balance adjustment based on the calculated low saturation threshold;
Calculating a gray balance adjustment condition using the extracted low saturation pixel;
Calculating a gray balance adjustment application rate according to the calculated occupancy for each hue region;
Dividing the captured image data into at least two brightness regions;
Calculating an occupancy ratio indicating a ratio of pixels of the divided brightness areas to the entire screen of the captured image data;
Estimating a captured scene of the captured image data according to the calculated occupancy for each brightness region;
Applying gray balance adjustment to the captured image data based on the calculated application rate and the estimation result of the shooting scene;
Applying gray balance adjustment to the captured image data based on the calculated gray balance adjustment condition, application rate, and estimation result of the shooting scene;
An image processing method comprising: Creating a three-dimensional histogram of the acquired hue value, saturation value and brightness value;
The step of dividing the captured image data into at least two hue regions divides the captured image data into at least two hue regions based on the created three-dimensional histogram,
8. The step of dividing the captured image data into at least two brightness regions divides the captured image data into at least two brightness regions based on the created three-dimensional histogram. An image processing method according to any one of the above. Dividing each of the divided at least two hue regions into at least two brightness regions;
Calculating an occupancy ratio indicating a ratio of pixels of a predetermined hue and brightness area in the divided area to the entire screen of the captured image data;
Estimating a shooting scene of the captured image data according to the calculated occupancy for each brightness area and the occupancy of the predetermined hue and brightness area;
The image processing method according to claim 5, further comprising:   In the step of dividing the picked-up image data into at least two hue regions, the picked-up image data is divided with the hue values of the HSV color system 70, 185, 225 as boundaries, and the skin hue region, the green hue region, the sky region The image processing method according to claim 1, wherein the image processing method is divided into four hue areas, a hue area and a red hue area.   In the step of dividing the picked-up image data into at least two lightness areas, the picked-up image data is divided with HSV color system lightness values of 85 and 170 as boundaries, and a shadow area, an intermediate area, and a highlight area. The image processing method according to claim 5, wherein the image processing method is divided into two brightness regions.   The image processing method according to any one of claims 5 to 11, wherein a lightness deviation amount of a skin hue region is calculated among the divided hue regions and used for estimation of the shooting scene. Calculating a low saturation pixel extraction rate for each hue region according to the calculated occupancy rate for each hue region;
Extracting low saturation pixels used for gray balance adjustment based on the calculated low saturation pixel extraction rate; and
The image processing method according to claim 1, further comprising:   The step of calculating the application rate of gray balance adjustment according to the calculated occupancy rate for each hue region is obtained by calculating the application rate for each hue region from each occupancy rate using a different application rate calculation formula for each hue region. 8. The image processing according to claim 2, wherein a maximum value of the application rate calculated for each hue region is set as an application rate of the gray balance adjustment. Method.   When the captured image data is divided into a skin hue area, a sky hue area, a green hue area, and a red hue area, the application ratio calculation formula is an application ratio of the skin hue area and the sky hue area with respect to the same occupation ratio. The image processing method according to claim 14, wherein is set to be smaller than an application rate of other hue regions.   The image processing method according to claim 1, wherein the captured image data is scene reference image data.   The image processing method according to claim 1, wherein the image data optimized for viewing on the output medium is viewing image reference data. In an image processing apparatus for inputting captured image data and outputting image data optimized for viewing on an output medium,
Data acquisition means for obtaining a hue value and a saturation value for each pixel of the captured image data;
Hue area dividing means for dividing the captured image data into at least two hue areas;
A hue area occupancy ratio calculating unit that calculates an occupancy ratio indicating a ratio of pixels for each of the divided hue areas to the entire screen of the captured image data;
Low saturation threshold value calculating means for calculating a low saturation threshold value for each hue region according to the calculated occupancy ratio for each hue region;
Low saturation pixel extraction means for extracting low saturation pixels used for gray balance adjustment based on the calculated low saturation threshold;
A gray balance adjustment condition calculating means for calculating a gray balance adjustment condition using the extracted low saturation pixel;
Based on the calculated gray balance adjustment conditions, gray balance adjustment means for performing gray balance adjustment on the captured image data;
An image processing apparatus comprising: In an image processing apparatus for inputting captured image data and outputting image data optimized for viewing on an output medium,
Data acquisition means for obtaining a hue value and a saturation value for each pixel of the captured image data;
Hue area dividing means for dividing the captured image data into at least two hue areas;
A hue area occupancy ratio calculating unit that calculates an occupancy ratio indicating a ratio of pixels for each of the divided hue areas to the entire screen of the captured image data;
An application rate calculating means for calculating an application rate of gray balance adjustment according to the calculated occupation ratio for each hue region;
Gray balance adjustment means for performing gray balance adjustment based on the calculated application rate;
An image processing apparatus comprising: In an image processing apparatus for inputting captured image data and outputting image data optimized for viewing on an output medium,
Data acquisition means for obtaining a hue value and a saturation value for each pixel of the captured image data;
Hue area dividing means for dividing the captured image data into at least two hue areas;
A hue area occupancy ratio calculating unit that calculates an occupancy ratio indicating a ratio of pixels for each of the divided hue areas to the entire screen of the captured image data;
Low saturation threshold value calculating means for calculating a low saturation threshold value for each hue region according to the calculated occupancy ratio for each hue region;
Low saturation pixel extraction means for extracting low saturation pixels used for gray balance adjustment based on the calculated low saturation threshold;
A gray balance adjustment condition calculating means for calculating a gray balance adjustment condition using the extracted low saturation pixel;
An application rate calculating means for calculating an application rate of gray balance adjustment according to the calculated occupation ratio for each hue region;
Gray balance adjustment means for performing gray balance adjustment on the captured image data based on the calculated gray balance adjustment condition and application rate;
An image processing apparatus comprising: Two-dimensional histogram creation means for creating a two-dimensional histogram of the acquired hue value and saturation value;
The image according to any one of claims 18 to 20, wherein the hue area dividing unit divides the captured image data into at least two hue areas based on the created two-dimensional histogram. Processing equipment. In an image processing apparatus for inputting captured image data and outputting image data optimized for viewing on an output medium,
Data acquisition means for obtaining a hue value, a saturation value and a brightness value for each pixel of the captured image data;
Hue area dividing means for dividing the captured image data into at least two hue areas;
A hue area occupancy ratio calculating unit that calculates an occupancy ratio indicating a ratio of pixels for each of the divided hue areas to the entire screen of the captured image data;
Low saturation threshold value calculating means for calculating a low saturation threshold value for each hue region according to the calculated occupancy ratio for each hue region;
Low saturation pixel extraction means for extracting low saturation pixels used for gray balance adjustment based on the calculated low saturation threshold;
A gray balance adjustment condition calculating means for calculating a gray balance adjustment condition using the extracted low saturation pixel;
Brightness area dividing means for dividing the captured image data into at least two brightness areas;
A brightness area occupancy ratio calculating means for calculating an occupancy ratio indicating a ratio of pixels of the divided brightness area to the entire screen of the captured image data;
In accordance with the calculated occupancy for each brightness area, shooting scene estimation means for estimating a shooting scene of the captured image data;
Gray balance adjustment means for performing gray balance adjustment on the captured image data based on the calculated gray balance adjustment condition and the estimation result of the shooting scene;
An image processing apparatus comprising: In an image processing apparatus for inputting captured image data and outputting image data optimized for viewing on an output medium,
Data acquisition means for obtaining a hue value, a saturation value and a brightness value for each pixel of the captured image data;
Hue area dividing means for dividing the captured image data into at least two hue areas;
A hue area occupancy ratio calculating unit that calculates an occupancy ratio indicating a ratio of pixels for each of the divided hue areas to the entire screen of the captured image data;
An application rate calculating means for calculating an application rate of gray balance adjustment according to the calculated occupation ratio for each hue region;
Brightness area dividing means for dividing the captured image data into at least two brightness areas;
A brightness area occupancy ratio calculating means for calculating an occupancy ratio indicating a ratio of pixels of the divided brightness area to the entire screen of the captured image data;
In accordance with the calculated occupancy for each brightness area, shooting scene estimation means for estimating a shooting scene of the captured image data;
Gray balance adjustment means for performing gray balance adjustment on the captured image data based on the calculated application rate and the estimation result of the shooting scene;
An image processing apparatus comprising: In an image processing apparatus for inputting captured image data and outputting image data optimized for viewing on an output medium,
Data acquisition means for obtaining a hue value, a saturation value and a brightness value for each pixel of the captured image data;
Hue area dividing means for dividing the captured image data into at least two hue areas;
A hue area occupancy ratio calculating unit that calculates an occupancy ratio indicating a ratio of pixels for each of the divided hue areas to the entire screen of the captured image data;
Low saturation threshold value calculating means for calculating a low saturation threshold value for each hue region according to the calculated occupancy ratio for each hue region;
Low saturation pixel extraction means for extracting low saturation pixels used for gray balance adjustment based on the calculated low saturation threshold;
A gray balance adjustment condition calculating means for calculating a gray balance adjustment condition using the extracted low saturation pixel;
An application rate calculating means for calculating an application rate of gray balance adjustment according to the calculated occupation ratio for each hue region;
Brightness area dividing means for dividing the captured image data into at least two brightness areas;
A brightness area occupancy ratio calculating means for calculating an occupancy ratio indicating a ratio of pixels of the divided brightness area to the entire screen of the captured image data;
In accordance with the calculated occupancy for each brightness area, shooting scene estimation means for estimating a shooting scene of the captured image data;
Gray balance adjustment means for performing gray balance adjustment on the captured image data based on the calculated gray balance adjustment condition, application rate, and estimation result of the shooting scene;
An image processing apparatus comprising: A three-dimensional histogram creating means for creating a three-dimensional histogram of the acquired hue value, saturation value and lightness value;
The hue area dividing unit divides the captured image data into at least two hue areas based on the created three-dimensional histogram,
The image according to any one of claims 22 to 24, wherein the lightness region dividing means divides the captured image data into at least two lightness regions based on the created three-dimensional histogram. Processing equipment. Hue lightness area dividing means for dividing each of the divided at least two hue areas into at least two lightness areas;
Hue lightness area occupancy calculating means for calculating an occupancy ratio indicating a ratio of pixels of a predetermined hue and lightness area among the divided areas to the entire screen of the captured image data,
The photographic scene estimation means estimates a photographic scene of the captured image data according to the calculated occupancy ratio for each brightness area and the occupancy ratio of the predetermined hue and brightness area. The image processing apparatus according to any one of 22 to 25.   The hue area dividing means divides the captured image data with HSV color system hue values of 70, 185, and 225 as boundaries, and includes a skin hue area, a green hue area, a sky hue area, and a red hue area. 27. The image processing device according to claim 18, wherein the image processing device is divided into four hue regions.   The brightness area dividing unit divides the captured image data into three brightness areas, a shadow area, an intermediate area, and a highlight area, by dividing the HSV color system brightness values with values of 85 and 170 as boundaries. The image processing apparatus according to any one of claims 22 to 27, wherein:   29. The photographic scene estimation unit calculates a lightness deviation amount of a skin hue region among the divided hue regions, and uses the lightness deviation amount for estimation of the photographic scene. An image processing apparatus according to claim 1. Low saturation pixel extraction rate calculating means for calculating an extraction rate of low saturation pixels for each hue region according to the calculated occupancy rate for each hue region;
The low saturation pixel extraction unit extracts low saturation pixels used for gray balance adjustment based on the calculated low saturation threshold or the calculated extraction ratio of the low saturation pixels. The image processing apparatus according to any one of claims 18, 20, 21, 22, and 24.   The application rate calculating means calculates an application rate for each hue region from each occupancy rate using a different calculation formula for each hue region, and calculates a maximum value of the application rate calculated for each hue region. The image processing apparatus according to any one of claims 19, 20, 23, and 24, wherein an application rate of balance adjustment is used.   When the captured image data is divided into a skin hue area, a sky hue area, a green hue area, and a red hue area, the application ratio calculation formula is an application ratio of the skin hue area and the sky hue area with respect to the same occupation ratio. 32. The image processing apparatus according to claim 31, wherein is set so as to be smaller than an application rate of other hue regions.   The image processing apparatus according to any one of claims 18 to 32, wherein the captured image data is scene reference image data.   The image processing apparatus according to any one of claims 18 to 33, wherein the image data optimized for viewing on the output medium is viewing image reference data. In an image recording apparatus that inputs captured image data, generates image data optimized for viewing on an output medium, and forms the generated image data on the output medium.
Data acquisition means for obtaining a hue value and a saturation value for each pixel of the captured image data;
Hue area dividing means for dividing the captured image data into at least two hue areas;
A hue area occupancy ratio calculating unit that calculates an occupancy ratio indicating a ratio of pixels for each of the divided hue areas to the entire screen of the captured image data;
Low saturation threshold value calculating means for calculating a low saturation threshold value for each hue region according to the calculated occupancy ratio for each hue region;
Low saturation pixel extraction means for extracting low saturation pixels used for gray balance adjustment based on the calculated low saturation threshold;
A gray balance adjustment condition calculating means for calculating a gray balance adjustment condition using the extracted low saturation pixel;
Based on the calculated gray balance adjustment conditions, gray balance adjustment means for performing gray balance adjustment on the captured image data;
An image recording apparatus comprising: In an image recording apparatus that inputs captured image data, generates image data optimized for viewing on an output medium, and forms the generated image data on the output medium.
Data acquisition means for obtaining a hue value and a saturation value for each pixel of the captured image data;
Hue area dividing means for dividing the captured image data into at least two hue areas;
A hue area occupancy ratio calculating unit that calculates an occupancy ratio indicating a ratio of pixels for each of the divided hue areas to the entire screen of the captured image data;
An application rate calculating means for calculating an application rate of gray balance adjustment according to the calculated occupation ratio for each hue region;
Gray balance adjustment means for performing gray balance adjustment based on the calculated application rate;
An image recording apparatus comprising: In an image recording apparatus that inputs captured image data, generates image data optimized for viewing on an output medium, and forms the generated image data on the output medium.
Data acquisition means for obtaining a hue value and a saturation value for each pixel of the captured image data;
Hue area dividing means for dividing the captured image data into at least two hue areas;
A hue area occupancy ratio calculating unit that calculates an occupancy ratio indicating a ratio of pixels for each of the divided hue areas to the entire screen of the captured image data;
Low saturation threshold value calculating means for calculating a low saturation threshold value for each hue region according to the calculated occupancy ratio for each hue region;
Low saturation pixel extraction means for extracting low saturation pixels used for gray balance adjustment based on the calculated low saturation threshold;
A gray balance adjustment condition calculating means for calculating a gray balance adjustment condition using the extracted low saturation pixel;
An application rate calculating means for calculating an application rate of gray balance adjustment according to the calculated occupation ratio for each hue region;
Gray balance adjustment means for performing gray balance adjustment on the captured image data based on the calculated gray balance adjustment condition and application rate;
An image recording apparatus comprising: Two-dimensional histogram creation means for creating a two-dimensional histogram of the acquired hue value and saturation value;
The image according to any one of claims 35 to 37, wherein the hue area dividing unit divides the captured image data into at least two hue areas based on the created two-dimensional histogram. Recording device. In an image recording apparatus that inputs captured image data, generates image data optimized for viewing on an output medium, and forms the generated image data on the output medium.
Data acquisition means for obtaining a hue value, a saturation value and a brightness value for each pixel of the captured image data;
Hue area dividing means for dividing the captured image data into at least two hue areas;
A hue area occupancy ratio calculating unit that calculates an occupancy ratio indicating a ratio of pixels for each of the divided hue areas to the entire screen of the captured image data;
Low saturation threshold value calculating means for calculating a low saturation threshold value for each hue region according to the calculated occupancy ratio for each hue region;
Low saturation pixel extraction means for extracting low saturation pixels used for gray balance adjustment based on the calculated low saturation threshold;
A gray balance adjustment condition calculating means for calculating a gray balance adjustment condition using the extracted low saturation pixel;
Brightness area dividing means for dividing the captured image data into at least two brightness areas;
A brightness area occupancy ratio calculating means for calculating an occupancy ratio indicating a ratio of pixels of the divided brightness area to the entire screen of the captured image data;
In accordance with the calculated occupancy for each brightness area, shooting scene estimation means for estimating a shooting scene of the captured image data;
Gray balance adjustment means for performing gray balance adjustment on the captured image data based on the calculated gray balance adjustment condition and the estimation result of the shooting scene;
An image recording apparatus comprising: In an image recording apparatus that inputs captured image data, generates image data optimized for viewing on an output medium, and forms the generated image data on the output medium.
Data acquisition means for obtaining a hue value, a saturation value and a brightness value for each pixel of the captured image data;
Hue area dividing means for dividing the captured image data into at least two hue areas;
A hue area occupancy ratio calculating unit that calculates an occupancy ratio indicating a ratio of pixels for each of the divided hue areas to the entire screen of the captured image data;
An application rate calculating means for calculating an application rate of gray balance adjustment according to the calculated occupation ratio for each hue region;
Brightness area dividing means for dividing the captured image data into at least two brightness areas;
A brightness area occupancy ratio calculating means for calculating an occupancy ratio indicating a ratio of pixels of the divided brightness area to the entire screen of the captured image data;
In accordance with the calculated occupancy for each brightness area, shooting scene estimation means for estimating a shooting scene of the captured image data;
Gray balance adjustment means for performing gray balance adjustment on the captured image data based on the calculated application rate and the estimation result of the shooting scene;
An image recording apparatus comprising: In an image recording apparatus that inputs captured image data, generates image data optimized for viewing on an output medium, and forms the generated image data on the output medium.
Data acquisition means for obtaining a hue value, a saturation value and a brightness value for each pixel of the captured image data;
Hue area dividing means for dividing the captured image data into at least two hue areas;
A hue area occupancy ratio calculating unit that calculates an occupancy ratio indicating a ratio of pixels for each of the divided hue areas to the entire screen of the captured image data;
Low saturation threshold value calculating means for calculating a low saturation threshold value for each hue region according to the calculated occupancy ratio for each hue region;
Low saturation pixel extraction means for extracting low saturation pixels used for gray balance adjustment based on the calculated low saturation threshold;
A gray balance adjustment condition calculating means for calculating a gray balance adjustment condition using the extracted low saturation pixel;
An application rate calculating means for calculating an application rate of gray balance adjustment according to the calculated occupation ratio for each hue region;
Brightness area dividing means for dividing the captured image data into at least two brightness areas;
A brightness area occupancy ratio calculating means for calculating an occupancy ratio indicating a ratio of pixels of the divided brightness area to the entire screen of the captured image data;
In accordance with the calculated occupancy for each brightness area, shooting scene estimation means for estimating a shooting scene of the captured image data;
Gray balance adjustment means for performing gray balance adjustment on the captured image data based on the calculated gray balance adjustment condition, application rate, and estimation result of the shooting scene;
An image recording apparatus comprising: A three-dimensional histogram creating means for creating a three-dimensional histogram of the acquired hue value, saturation value and lightness value;
The hue area dividing unit divides the captured image data into at least two hue areas based on the created three-dimensional histogram,
The image according to any one of claims 39 to 41, wherein the lightness region dividing unit divides the captured image data into at least two lightness regions based on the created three-dimensional histogram. Recording device. Hue lightness area dividing means for dividing each of the divided at least two hue areas into at least two lightness areas;
Hue lightness area occupancy calculating means for calculating an occupancy ratio indicating a ratio of pixels of a predetermined hue and lightness area among the divided areas to the entire screen of the captured image data,
The photographic scene estimation means estimates a photographic scene of the captured image data according to the calculated occupancy ratio for each brightness area and the occupancy ratio of the predetermined hue and brightness area. The image recording device according to any one of 39 to 42.   The hue area dividing means divides the captured image data with HSV color system hue values of 70, 185, and 225 as boundaries, and includes a skin hue area, a green hue area, a sky hue area, and a red hue area. 44. The image recording apparatus according to any one of claims 35 to 43, wherein the image recording apparatus is divided into four hue regions.   The brightness area dividing unit divides the captured image data into three brightness areas, a shadow area, an intermediate area, and a highlight area, by dividing the HSV color system brightness values with values of 85 and 170 as boundaries. 45. The image recording apparatus according to any one of claims 39 to 44, wherein:   46. The photographic scene estimation means calculates a lightness deviation amount of a skin hue region among the divided hue regions, and uses the lightness deviation amount for estimation of the photographic scene. An image recording apparatus according to claim 1. Low saturation pixel extraction rate calculating means for calculating an extraction rate of low saturation pixels for each hue region according to the calculated occupancy rate for each hue region;
The low saturation pixel extraction means extracts low saturation pixels used for gray balance adjustment based on the calculated low saturation threshold or the calculated extraction ratio of the low saturation pixels. The image recording apparatus according to any one of claims 35, 37, 38, 39, and 41.   The application rate calculating means calculates an application rate for each hue region from each occupancy rate using a different calculation formula for each hue region, and calculates a maximum value of the application rate calculated for each hue region. The image recording apparatus according to any one of claims 36, 37, 40, and 41, wherein the application rate of balance adjustment is used.   When the captured image data is divided into a skin hue area, a sky hue area, a green hue area, and a red hue area, the application ratio calculation formula is an application ratio of the skin hue area and the sky hue area with respect to the same occupation ratio. 49. The image recording apparatus according to claim 48, wherein the image recording apparatus is set to be smaller than an application rate of other hue regions.   The image recording apparatus according to any one of claims 35 to 49, wherein the captured image data is scene reference image data.   51. The image recording apparatus according to claim 35, wherein the image data optimized for viewing on the output medium is viewing image reference data.

Images