JP2002077616A - Image processing method and apparatus, and recording medium - Google Patents

Abstract

PROBLEM TO BE SOLVED: To enhance the image quality of an output image by setting correction conditions appropriately in accordance with a characteristic of an image. SOLUTION: A highlight point is calculated from a histogram which determined by the number of pixels of brightness value indicated by image data (S211) to check the distribution of brightness in a high brightness region of the image (S212). On the other hand, the percentage of a cumulative frequency of a prescribed region in the total pixel number of the histogram is calculated (S221, S222) to examine the balancing degree of the histogram. Next, a shadow point is calculated (S231) to examine the distribution of brightness in a low brightness region of the image (S232) and a gray level correction curve is selected from the the three discriminated parameters in the above.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION The present invention relates to setting gradation correction conditions based on a histogram of an image.

[0002]

2. Description of the Related Art In recent years, with the development of ink-jet printing technology, by using a digital camera with a high pixel count and a printing technology such as an ink-jet printing method, a better printed output image can be obtained as compared with the related art. It is becoming.

[0003] However, when a digital photo image is printed out, there is still a point to be improved in the image quality of the output image. Regarding this, for example, it is considered that one of the causes is that the image is overexposed or underexposed, or the color balance of the entire image is shifted due to a phenomenon called “color fogging”.

[0004] For example, when performing auto shooting with a camera, A
Since E (automatic exposure) functions, when a large part of the background includes, for example, a blue sky, the image becomes dark as a whole and underexposed images are taken, and the subject does not appear in a good state.

Further, taking a digital camera as an example, CC
Since imaging is performed by the D camera, colors having wavelengths that cannot be sensed by the naked eye are also captured as images. When a signal including the signal is processed as R, G, and B signals, a color that cannot be recognized by the naked eye may become apparent and the color balance may be inappropriate. In this case, processing such as an infrared cut filter is performed. However, the processing is not always perfect, and there is a restriction that the color correction balance is real-time correction. Is often difficult to be perfect.

[0006] Such a loss of color balance in a picked-up image results in an influence on a printed image. For this reason, in order to obtain a good print output result, it is desirable to correct the input image itself, such as a captured image, into an appropriate image with a good color balance.

As a method for performing such a correction, there has been proposed a method in which a histogram of an image is analyzed, and a correction condition is set based on the maximum luminance and the minimum luminance obtained thereby.

[0008]

The image quality of an image printed out has the following problems in addition to the above-mentioned "color fogging" and color balance due to exposure.

[0009] Recent advances in ink-jet printing technology have reduced the size of dots formed by ink and have exceeded the visible limit of the naked eye, making it possible to print with "zero graininess". In this regard, the ink jet printing technology can be compared with the printing technology using silver halide photography. However, there remains a problem that the absolute density realized in a printed image is lower than that of a silver halide photograph due to the characteristics of ink used in ink jet printing.

[0010] Such a problem is not a problem peculiar to the ink jet printing technique, but has a similar problem in other types of printing or a display such as a CRT.

In general, it is known that by appropriately increasing the density in an output image, the image is closed and a good image can be obtained. However, if the density is increased uniformly for the purpose of increasing the density of the output image, the gradation of such a portion will be crushed in an image having many dark portions,
Instead, the image quality may be degraded.

On the other hand, in the case of a photograph taken at night or in a dark room, the entire image is dark due to underexposure or the like, and may be too different from the original color. Here, by lowering the density of the output image, a dark image as a whole can be lightened and details can be clearly finished.

In some cases, the dynamic range of the luminance of an image becomes very narrow depending on the accuracy of the CCD of the digital camera and the photographing conditions. In that case, by expanding the gradation of the image, it is possible to convert the image into a contrasting and attractive image.

An object of the present invention is to improve the image quality of an output image by appropriately setting correction conditions according to the characteristics of the image.

[0015]

SUMMARY OF THE INVENTION The present invention is characterized by having the following arrangement in order to achieve the above object.

According to a first aspect of the present invention, the brightness distribution of the image is determined from a histogram of the number of pixels of the value of the component related to the brightness of the image indicated by the image data, and a plurality of gradation corrections are performed based on the determination. One of the conditions is automatically selected, and the component relating to brightness is corrected using the selected gradation correction condition.

According to a second aspect of the present invention, a color distribution of the image is determined from a histogram of the image, a tone correction condition for correcting the image is determined based on the determination, and the determined tone correction condition is determined. An image processing method for correcting the image by using the method, wherein the color distribution of the image is determined based on a highlight point, a shadow point of the image, and a cumulative frequency of a predetermined range in the histogram from the histogram. It is characterized by.

[0018]

Embodiments of the present invention will be described below in detail with reference to the drawings.

<First Embodiment> FIG. 1 is a block diagram showing a schematic configuration of a printing system according to an embodiment of the present invention. This system is generally composed of a host computer 1
00, a printer 106 and a monitor 105. That is, the host computer 10
For example, an inkjet printer 106 and a monitor 105 are connected to “0” so that bidirectional communication is possible.

The host computer 100 has an OS (operating system) 102.
An application 101 such as a word processor, a spreadsheet, an image processing, and an Internet browser, which performs each process under the management of the application 100, and various drawing commands issued by the application and indicating an output image (image drawing commands, text drawing commands, graphics A printer driver 103 that processes print rendering commands to create print data, and an application 101
Has a monitor driver 104 for processing various drawing command groups issued by the PC and displaying it on the monitor 106 as similar software.

The host computer 100 includes a central processing unit CPU 108, a hard disk driver HD 107, a random access memory (RAM) 1 as various hardware operable by the above-mentioned software.
09, a read only memory (ROM) 110 and the like. That is, the CPU 108 executes signal processing relating to processing according to the above-described software, and executes a signal processing on the hard disk or the R driven by the hard disk driver 107.
The OM 110 stores various kinds of software in advance, and reads and uses the software as needed. The RAM 109 is used as a work area for executing signal processing by the CPU 108 and the like.

As an embodiment shown in FIG. 1, for example,
A common example is a computer that uses Microsoft's Windows 98 as an OS, installs an application that can perform any print processing, and connects a monitor and a printer to an IBM AT compatible personal computer that is widely used. it can.

In the printing system having the above configuration, the user uses the application 101 to
While viewing the display image on the monitor 105, image data including text data classified as text such as characters, graphics data classified as graphics such as figures, image image data classified as natural images, and the like is created. can do.

When a print process is instructed by the user, the application 101 issues a print output request to the OS 102 and, at the same time, outputs an output image constituted by using the graphics data portion as a graphics drawing command and the image data portion as an image drawing command. The rendering command group shown is issued to the OS 102. The OS 102 receives a print output request from the application and issues a drawing command group to the printer driver 103.

A printer driver 103 processes a print request and a drawing command group input from the OS 102 and
5 to create print data in a form that can be printed by the printer 1
Transfer to 05. In this case, if the printer 105 is a raster printer, the printer driver 103
The drawing command from S102 is sequentially subjected to image correction processing, rasterized sequentially into an RGB 24-bit page memory, and after all drawing commands are rasterized, the contents of the RGB 24-bit page memory are transferred to the printer 105.
Converts the data into a printable data format, for example, CMYK data, and transfers the data to a printer.

FIG. 2 is a diagram showing processing performed by the printer driver 103. The processing of the printer driver 103 is roughly divided into image correction processing and printer correction processing.

The image correcting process 120 performs an image correcting process on the color information including the luminance signals R, G, and B included in the drawing command group input from the OS 102. Specifically, based on the color information of red (R), green (G), blue (B),
An automatic gradation correction process described later is performed. On the other hand, the printer correction processing unit 121 first rasterizes the drawing instruction of the color information corrected by the image correction processing 120, and performs R, G, B
A raster image is generated in a 24-bit page memory.
Then, cyan (C), magenta (M), yellow (Y), and cyan (C) depending on the color reproducibility of a printer that performs printing for each predetermined pixel.
Black (K) data is generated and transferred to the printer 105.

Next, the automatic gradation correction processing performed in the image correction processing 120 will be described. The automatic gradation correction process
This is performed for an image image among image data indicated by the image drawing command. For example, when a graphics image or an image image is included in the image data, an image image portion is extracted from the image data, and an automatic gradation correction process is performed on the image portion.

The automatic gradation correction according to the present embodiment is performed by determining the brightness of an image to be printed using a histogram obtained by summing up the frequency of each luminance value in the image data, and determining an appropriate correction gradation curve. Things. In the automatic gradation correction of the present embodiment, the density of the entire image is corrected by correcting the image with a γ curve (for example, a downwardly convex gamma curve in FIG. 10) for an image having a high density portion, that is, a shadow portion is small. The overall output density of a silver halide photograph, which is an output device capable of high-density output, is increased, and a gamma curve (for example, an upwardly convex shape in FIG. By correcting the image brightly with a gamma curve, the brightness balance of the entire image can be improved, and for an image having a narrow histogram width, a curve that expands the dynamic range (for example, the S-curve in FIG. 11) ) Can be used to make a contrasting and attractive image.

FIG. 3 is a diagram conceptually showing the automatic gradation correction, and FIG. 4 is a flowchart showing the processing procedure.

(Histogram totalization) In the histogram totalization process in step S1, first, the input RGB image signals are converted into luminance Y, which is a component relating to the brightness of the image, and color difference signals Cr, Cb, which are components relating to the tint. (B1 in FIG. 3). The conversion formula is represented as follows.

Y = 0.299 × R + 0.587 × G + 0.114 × B Cr = RY Cb = BY

Next, of the converted signals Y, Cr and Cb, the luminance value of each pixel in the image data (the value of the signal Y) is examined for the signal Y corresponding to the luminance, and is indicated by 0 to 255. For each luminance value, the frequencies of the pixels having that luminance are totaled, and a luminance histogram (frequency distribution) is created.

For example, when the image data shows a bright image as a whole, the histogram thus created has a distribution biased toward the high luminance side as shown in FIG. 5, while it shows a darker image as a whole. At this time, the distribution is biased toward the low luminance side as shown in FIG.

The above-described creation of the luminance histogram is as follows.
Since the purpose is to check the frequency distribution of the luminance in the entire image, the counting of the frequencies does not necessarily need to be performed for all the pixels. For example, 1600 (pixels) × 1200
For (pixel) image data, 15 (pixels) horizontally,
Aggregation may be performed on pixels that are decimated by 11 (pixels) vertically, or an average value of each of those pixels with surrounding pixels may be used.

(Tone Curve Determination) In step S2, a correction condition setting process (tone curve determination process) is performed based on the obtained histogram. That is, a gradation curve is selected from a plurality of gradation curves for luminance correction prepared in advance, based on the analysis result of the image.

In the gradation curve determination of this embodiment, the brightness of the image is determined based on three parameters (highlight point, shadow point, and the number of pixels in a certain luminance area), and the gradation curve is determined based on this. select.

FIG. 8 is a flowchart showing the details of the gradation curve judgment processing. The gradation curve judgment processing of the present embodiment will be described with reference to FIG.

Highlight Point Determination Unit In the highlight point determination processing in step S21, a highlight point in an image to be processed is calculated from the histogram.

In the present embodiment, in the histogram of the luminance signal Y, from the highest luminance value (luminance value 255) in the luminance range, the frequencies of the respective luminance values are accumulated in order toward the lower luminance side. For example, a luminance value that matches 1.0% of the total number of pixels of the image data to be processed or a luminance value that first exceeds 1.0% of the total number of pixels is obtained.
This point is referred to as a highlight point (hereinafter, referred to as “HLP”).

Next, the HLP is set to a plurality of predetermined thresholds Th.
_H1, Th_H2,... (Th_H1 <Th_H2
<...), the distribution of the histogram in the high luminance region is analyzed.

For example, in the present embodiment, as shown in FIG. 9, a case will be described in which two threshold values in which values 200 and 230 are set in order from the bottom.

When HLP <Th_H1, the image is determined to be a dark image having a small number of high-luminance areas, and
When H1 ≦ HLP <Th_H2, the image is determined to be a dark image as a whole although there is a distribution of high-luminance areas, and Th_H2
If ≦ HLP, the image is determined to be a bright image having a large distribution in the high luminance area.

For example, in the histogram of a bright image shown in FIG. 5, the HLP exceeds the threshold Th_H2 (HLP
> Th_H2), and it is determined that the image has a large distribution in the high luminance region. In the histogram of FIG. 5, the distribution is generally biased toward the high luminance side, and as a result, the HLP is also located on the high luminance side. Images showing such a distribution are generally bright images.

In the histogram shown in FIG. 6, the HLP is lower than the threshold Th_H2 and higher than Th_H1 (Th_H
1 <HLP ≦ Th_H2), the image can be regarded as an image that is not bright although there is some distribution in the high luminance region. In the histogram of FIG. 6, the luminance has a roughly intermediate distribution, and the HLP is located on a relatively low luminance side as compared with FIG.

In the histogram shown in FIG. 7, the HLP is lower than the threshold Th_H1, and the image has no distribution in the high luminance area. In this case, it can be seen that the histogram is biased toward the low luminance side and is a dark image as a whole. In addition, a low HLP also means that the gradation level is narrow. For such an image, correction such as increasing the brightness by γ correction or extending the luminance value to the higher luminance side to increase the brightness is required.

It is not always necessary to calculate the HLP by the above-described method, and a conventionally known method may be appropriately used.

When performing the automatic gradation correction processing of the present embodiment in combination with other image correction processing, for example, the so-called color fogging correction, contrast correction, and saturation correction, the image processing is performed in advance. HLP can also be used. In this case, instead of using the highlight point, the brightness (darkness) of the image can be determined by using the shadow point which is also used in the above color cast correction and the like, and based on this, the following processing is performed. It is obvious from the following description that the following can be performed.

Determination of Balance of Histogram In step S22, the balance of the histogram is determined using the histogram obtained in step S1 of FIG.

In the histogram balance determination process, Slow, which is the ratio of the cumulative frequency of a predetermined area to the total number of pixels of the image to be processed, is determined in step S22. That is, for an image of 256 gradations, for example, the ratio of the cumulative number of pixels having a luminance value of 0 to 128 (half of the histogram) to the total number of pixels is obtained, and the overall balance of the histogram of the image is analyzed.

First, the cumulative frequency S of a certain luminance area (0 to 128) is obtained. The cumulative frequency S is obtained as a cumulative frequency from the lowest luminance value (luminance value 0) in the luminance range to a predetermined luminance value toward the higher luminance side in the histogram. In the present embodiment, the maximum luminance value (luminance value 255) of 1
Although the cumulative frequency up to a luminance value (luminance value 128) of / 2 is obtained as the cumulative frequency S of the low luminance area, other values may of course be used.

Next, the ratio Slow of the cumulative frequency S to the total number of pixels is calculated using the following equation.

Slow = (cumulative frequency S of a certain luminance area) /
(Total number of pixels) (%)

When the thinning histogram is created by thinning out the pixels at the time of the above-described histogram totaling, the denominator in the above Slow definition formula is set to the number of pixels for which the histogram is created.

Next, the threshold value is determined again using Slow obtained as described above. This is to calculate the ratio of the lower half of the histogram to the whole, thereby checking the overall luminance balance of the image. In the above-described highlight point determination, the images are classified into several types according to the distribution state of the high-luminance area of the histogram. However, as shown in FIG. 9, a threshold value is provided for each case, and the degree of balance of the histogram is determined. judge.

For example, in the case of a bright image shown in FIG.
The ratio of the shaded area to the total number of pixels is Slow. In this example, Slow is 20% of the total number of pixels. Therefore, the image is determined to be a bright image by the above HLP determination, and Slow is determined to be in the range of 16 to 50.

On the other hand, in the example of a dark image shown in FIG. 7, the area Slow indicated by oblique lines is 60% of the total number of pixels. It is determined that the range is 80.

In the method of determining the balance of the histogram using only the intermediate value or the average value of the histogram without using the ratio of the cumulative frequency in a certain luminance region, the image distribution in which the actual distribution state of the histogram is not appropriately reflected is considered. The determination regarding the brightness is performed. For example, while the intermediate value or the average value itself shows a relatively high luminance value, actually, the luminance value around the intermediate value or the average value has a frequency distribution peak and the frequency distribution itself in the low luminance region is small. For images,
Luminance correction for increasing the density by erroneously determining that the image is a bright image is selected, and as a result, a dark portion occupying a relatively large portion on the image may be crushed.

On the other hand, as in the present embodiment, the cumulative frequency in an area of luminance values 0 to 128 which is the lower half of the histogram is obtained, and the ratio of the cumulative frequency to the total number of pixels is calculated.
By using Slow, it is possible to determine the brightness distribution of an image in which the actual balance of the histogram is reflected, and it is possible to perform appropriate gradation correction even on a dark image as described above. .

In the above embodiment, the range of luminance values 0 to 128 is equally divided for the range of Slow. However, when more detailed information on the low luminance area is to be obtained, the low luminance area is divided into several parts. Each case may be classified, and if Slow is 0 to 64, it is doubled and 65 to 1
Weights of up to 28 may be multiplied by one and added.

Shadow Point Determination In the shadow point determination processing in step S23, first, a shadow point in an image to be processed is calculated from the histogram.

In this embodiment, the frequency of each luminance value is accumulated from the lowest luminance value (luminance value 0) of the luminance range in the histogram to the higher luminance side in order, and the cumulative frequency obtained here is, for example, A luminance value that matches 1.0% of the total number of pixels of the target image data or a luminance value that exceeds 1.0% of the total number of pixels is first obtained, and this point is referred to as a shadow point (hereinafter, “SDP”). ).

Next, a plurality of threshold values Th_S1, Th_S2,... (Th_S
1 <Th_S2 <...) And analyze the distribution of the histogram in the low luminance region.

Since the shadow point determination used in the present embodiment is performed after performing the highlight point determination and the histogram balance determination, the threshold of the shadow point is determined as shown in FIG. A different value is set according to the result of the balance determination.

If SDP ≧ Th_S2, the image is determined to be a bright image with a small number of low-luminance areas, and
When _S1 ≦ SDP <Th_S2, the image is determined to be a bright image as a whole although there is a distribution of low-luminance areas, and the SDP
In the case of <Th_S1, the image is determined to be a dark image having a large distribution in the low luminance area.

For example, in the histogram of the bright image shown in FIG. 5, the SDP exceeds the threshold value Th_S2 (SDP
> Th_S2), and thus it is determined that the image has no distribution in the low luminance area. In this case, as described above, the distribution of the histogram is generally biased toward the high luminance side, and as a result, the SDP is also located at the relatively high luminance side. Also, SDP
That is, it means that the gradation level is narrow. For such an image, correction such as darkening by γ correction or extending the luminance value to the low luminance side to darken is required.

On the other hand, in the histogram shown in FIG.
P is lower than the threshold Th_S2 and higher than Th_S1 (T
h_S1 <SDP ≦ Th_S2), and it is determined that the image has a certain distribution in the low luminance area but is not dark. In this case, such a determination is made because the luminance has an intermediate distribution and the SDP is also located on the relatively low luminance side.

Next, in the histogram shown in FIG.
Is lower than the threshold Th_S1, and the image has a large distribution in the low luminance region. In this case, it can be seen that the histogram is biased toward the low luminance side and is a dark image as a whole.

Determination of Correction Tone Curve Details of the distribution of the high-luminance area in the above histogram (highlight points), the balance S1 of the histogram
ow, 3 of details of distribution of low luminance area (shadow point)
As shown in FIG. 9, the processing target images are classified into a plurality of types according to their types. Then, in the next step S24, a correction gradation curve is determined using the table shown in FIG.

As is clear from the correction table shown in FIG. 9, the tone curve of the present embodiment comprehensively judges the image type according to three parameters. For example, if the highlight point is relatively low, A gradation curve including a process of cutting the high-luminance area and extending the histogram to the high-luminance side is selected. Further, for an image for which the balance of the histogram is to be adjusted, a gradation curve for performing γ correction is selected. If the balance Slow of the histogram is biased toward the low luminance area, a gradation curve including a process of brightening the entire image by converting a γ value is selected. Further, if the shadow point is relatively high, a gradation curve including a process of cutting the low-luminance area and extending the histogram to the low-luminance side is selected. The determination of the gradation curves for the plurality of classified images uses a determination table shown in FIG.

For example, in the case of an image in which the HLP is as high as 245, the Slow is 20%, and the SDP is as high as 60, a gradation curve that cuts the low luminance area 20 or less is selected.

In the case of a bright image shown in FIG. 5, HLP is larger than the threshold Th_H2, Slow is 20%, and SLP is
Since DP is larger than the threshold Th_S2, this image is determined to be a bright image from the table shown in FIG. 9, and the γ value is set to 1.1. By this determination of the γ value, correction is performed to darken (increase the print density) up to a relatively high luminance area, and a print image having an optimum density as a whole is obtained. Further, since the ratio of the pixels in the low-luminance area is small, the portion where the image is crushed can be reduced.

Next, in the image shown in FIG. 6 where the histogram balance is intermediate, the HLP is larger than Th_H1 and TLP is larger than Th_H1.
It is smaller than h_H2. And Slow is 40%, SDP
Is larger than Th_S1 and smaller than Th_S2, the contrast can be emphasized by making the correction gradation curve an S-shape according to the table shown in FIG. In this way, the entire printed image is sharpened, and the printed image looks good.

On the other hand, in the dark image shown in FIG. 7, HLP is smaller than Th_H1, Slow is 60%, and SDP
Is smaller than Th_S1, a straight line that cuts 200 or more in the high-brightness area is selected according to the table shown in FIG. This brightens the entire printed image, especially expanding the histogram of the image to the higher brightness side,
A printed image is obtained with a balance of density with contrast.

In the above description, in the highlight point determination, the brightness of the high brightness area of the image is determined in three stages. However, in order to obtain a more optimal gradation curve, the number of branches is further increased. More detailed determination may be made in four or more stages. Further, the number of branches may be increased in order to perform more detailed determination in the histogram balance determination and shadow point determination.

(LUT Creation) When the above-described gradation curve determination processing (step S2 in FIG. 4) is completed, an LUT is created in step S3 shown in FIG. A look-up table (LUT) for luminance correction is created based on parameters for creating a tone curve obtained by the tone curve determination process.

The LUT of this embodiment records the tone curve obtained as described above as an exponential function and a quintic function. In other words, the exponential function (Fig. 1
0), and when correcting with a straight line that cuts a high-luminance or low-luminance region and a more complicated curve,
Use 1).

That is, if the LUT L [Y] is an exponential function that performs only γ correction, if the input luminance signal is Y and the output luminance signal is Y ′, Y ′ = 255 × [(Y / 255) 1 / γ] is performed, and γ is determined based on the determination result.
Is given a value.

On the other hand, in the case of a quintic curve, Y ′ = A + B × Y + C × Y ＾ 2 + D × Y ＾ 3 + E × Y ＾
4 + F × Y ＾ 5 The conversion represented by the following equation is performed, and the coefficients A,
By providing B, C, D, E, and F, a fifth-order curve is completed. They are created dynamically. That is, it is created for each processing of the target image. By dynamically creating a correction table in this way, the required memory amount can be reduced.

It is needless to say that the LUT may be statically prepared on a memory in advance for each of the gradation curves instead of being dynamically created.

(Correction) Next, in step S4 shown in FIG. 4, the luminance signal Y is corrected. That is, the created L
By the UT L [Y], the luminance value Y of the input image is represented by Y ′ =
L [Y] and perform luminance correction (B shown in FIG. 3).
2).

Further, the luminance signal Y 'whose luminance has been corrected and the color difference signals Cr and Cb of the input image are returned to R, G and B signals (the processing of B3 shown in FIG. 3), and the corrected image signal R' Create G'B '.

According to the present embodiment, in the histogram, the component value whose cumulative frequency from the maximum value or the minimum value shows a predetermined value in the range of the component value related to the brightness of the image data is obtained, so that the entire image is obtained. And the ratio of the cumulative frequency from the minimum value or the maximum value to the predetermined component value in the total number of pixels in the histogram is obtained, so that the distribution of image brightness can be known. . Then, the distribution of brightness is determined based on these component values and the ratios, and a gradation curve for correction is determined based on the determination. Therefore, the distribution of the brightness and the correction are determined for each overall brightness of the image. The correspondence with the gradation curve can be made different.

That is, using the three parameters of the highlight point, the balance of the histogram, and the shadow point, one of the plurality of corrected gradation curves is finally selected as the most suitable corrected gradation curve for the image. be able to.

As a result, for example, in a dark image as a whole, the distribution indicating the dark range in the brightness distribution corresponding to the correction to make the image brighter (to lower the density in the printed image) can be reduced. Thereby, the balance of brightness in the printed image can be made more preferable. On the other hand, by making the image darker as a whole, the density can be made higher in the printed image, thereby compensating for the density output characteristic that the printing device can realize only a relatively low density by nature. Density printing can be performed. In the case of an image that originally has a narrow dynamic range, by expanding the width, it is possible to print an image with contrast, sharpness, and good appearance.

In the present embodiment, the determination is made in the order shown in FIG. 8, but the order may be changed.

The calculation of HLP and SDP does not necessarily need to be calculated by the above-described method, and a conventionally known method may be appropriately used.

As described above, the highlight point is used to check the brightness distribution in the high brightness area, and the shadow point is used to check the brightness distribution in the low brightness area. However, for example, if the details of the brightness distribution in the high-brightness area are known by another parameter that changes the highlight point, that parameter may be used. That is, the ratio of the cumulative frequency from the maximum value of the histogram to a certain luminance value (for example, the luminance value 220) in the total number of pixels may be obtained, and the distribution of the brightness of the high luminance region may be determined from the value. In this case, as in the case of the highlight point, a plurality of thresholds are provided for the parameter, and the case determination is performed. Of course, in the low luminance region, instead of the shadow point, the ratio of the cumulative frequency from the minimum value of the histogram to a certain luminance value (for example, luminance value 30) in the total number of pixels may be obtained, and the same determination may be made.

Further, in the above-described embodiment, the correction regarding the luminance value Y has been described. However, the same correction may be directly performed on each of the R, G, and B signals. At this time, the above L
Using a UT, in the LUT instead of Y, R, G,
The correction can be performed using R ', G', B 'instead of B, Y'. The correction for the R, G, B signals is RG
Since the B-YCrCb conversion is unnecessary, the processing speed can be improved.

<Other Embodiments> The present invention, as described above,
The present invention may be applied to a system including a plurality of devices (for example, a host computer, an interface device, a reader, a printer, and the like) or may be applied to an apparatus including one device (for example, a copying machine and a facsimile machine).

FIG. 4 is a block diagram of a computer connected to an apparatus or a system connected to various devices to operate various devices so as to realize the functions of the above-described embodiment. The present invention also includes a software program code as shown in FIG. 7, which is implemented by operating a computer (CPU or MPU) of the system or apparatus according to a stored program to operate the various devices. It is.

In this case, the software program code itself implements the functions of the above-described embodiment, and the program code itself and means for supplying the program code to the computer, such as the program code The stored storage medium constitutes the present invention.

Examples of a storage medium for storing such a program code include a floppy (registered trademark) disk, a hard disk, an optical disk, a magneto-optical disk, and a CD-R.
An OM, a magnetic tape, a nonvolatile memory card, a ROM, or the like can be used.

When the computer executes the supplied program code, not only the functions of the above-described embodiment are realized, but also the OS (Operating System) running on the computer or another program. Needless to say, the program code is included in the embodiment of the present invention even when the functions of the above-described embodiment are realized in cooperation with application software or the like.

Further, the supplied program code is stored in a memory provided in a function expansion board of a computer or a function expansion unit connected to the computer, and then stored in the function expansion board or the function storage unit based on the instruction of the program code. It is needless to say that the present invention includes a case where a provided CPU or the like performs part or all of the actual processing, and the processing realizes the functions of the above-described embodiments.

[0096]

According to the present invention, the image quality of an output image can be improved by appropriately setting the correction conditions according to the characteristics of the image.

[Brief description of the drawings]

FIG. 1 is a block diagram illustrating a configuration of a print system according to an embodiment of the present invention.

FIG. 2 is a diagram illustrating processing of a printer driver in the system.

FIG. 3 is a diagram mainly illustrating a configuration of signal conversion in an automatic gradation correction process performed as an image correction process in a process of a printer driver.

FIG. 4 is a flowchart illustrating a procedure of an automatic gradation correction process.

FIG. 5 is a diagram illustrating a histogram when an image to be processed by the automatic gradation correction process is a bright image.

FIG. 6 is a diagram illustrating a histogram when the brightness of an image to be processed by the automatic gradation correction process is an intermediate image.

FIG. 7 is a diagram illustrating a histogram when an image to be processed by the automatic gradation correction process is a dark image.

FIG. 8 is a flowchart showing a procedure of a gradation curve determination process in the automatic gradation correction process shown in FIG.

FIG. 9 is a diagram showing the contents of a table used in a gradation curve determination process and explaining how to determine a gradation curve according to the type of image.

FIG. 10 is a diagram showing a conversion characteristic curve (exponential function) of a luminance correction table according to a γ value.

FIG. 11 is a diagram illustrating a conversion characteristic curve (fiveth-order curve) of a luminance correction table other than a simple γ correction.

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Claims (13)

[Claims] 1. A distribution of the brightness of an image is determined from a histogram of the number of pixels of a component value related to the brightness of the image indicated by the image data, and one of a plurality of gradation correction conditions is determined based on the determination. An image processing method for automatically selecting one of the two and correcting a component related to brightness using the selected gradation correction condition. 2. The image processing method according to claim 1, wherein the distribution of the brightness of the image is determined based on a cumulative frequency of a predetermined range in the histogram. 3. The image processing method according to claim 2, wherein the brightness distribution of the image is determined based on a ratio of the cumulative frequency of the predetermined range to the total number of pixels of the histogram. 4. The image processing method according to claim 1, wherein the gradation correction condition is selected based on a combination of a highlight point and a shadow point of the image and a distribution of brightness of the image. 5. The method according to claim 1, wherein the density is increased when the proportion of the highlight area in the histogram is large, the density is decreased when the proportion of the shadow area is large, and the dynamic range is increased when the histogram width is small. The image processing method according to claim 1, wherein 6. When the ratio of the highlight region is large, the degree of increase in density is greater than when the ratio of the highlight region is small. When the ratio of the shadow region is large, the ratio of the shadow region is large. 6. The image processing according to claim 5, wherein the degree of reduction in density is greater than when the width of the histogram is small, and the degree of expansion of the dynamic range is larger when the histogram width is smaller than when the histogram width is larger. Method. 7. A color distribution of the image is determined from a histogram of the image, a tone correction condition for correcting the image is determined based on the determination, and the image is determined using the determined tone correction condition. An image processing method for correcting the color distribution of the image, wherein the determination of the color distribution of the image is performed based on a highlight point, a shadow point, and a cumulative frequency of a predetermined range in the histogram from the histogram. Processing method. 8. The method according to claim 1, wherein the density is increased when the proportion of the highlight area in the histogram is large, the density is decreased when the proportion of the shadow area is large, and the dynamic range is increased when the histogram width is small. The image processing method according to claim 7, wherein 9. When the proportion of the highlight area is large, the degree of increase in density is greater than when the proportion of the highlight area is small. When the proportion of the shadow area is large, the proportion of the shadow area is large. 9. The image processing method according to claim 8, wherein the degree of reduction in density is larger than when the width of the histogram is small, and the degree of expansion of the dynamic range is larger when the histogram width is smaller than when the histogram width is larger. Method. 10. A discriminating means for discriminating a distribution of brightness of an image from a histogram relating to the number of pixels of a value of a component relating to brightness of an image indicated by image data, and a plurality of gradation correction conditions based on the discrimination. An image processing method comprising: selecting means for automatically selecting one from among them; and correcting means for correcting a component related to brightness using the selected gradation correction condition. 11. A determining means for determining a color distribution of the image from a histogram of the image, a means for determining a tone correction condition for correcting the image based on the determination, and the determined tone correction condition Correction means for correcting the image using, the determination means, based on the highlight points of the image, shadow points and the cumulative frequency of a predetermined range in the histogram from the histogram, the color distribution of the image An image processing device for performing determination. 12. A recording medium on which a program is recorded so as to be readable by a computer, wherein a distribution of the brightness of the image is determined from a histogram relating to the number of pixels of a component value relating to the brightness of the image indicated by the image data. Based on the determination, automatically selecting one of a plurality of tone correction conditions, and using the selected tone correction condition to correct a component related to brightness. A recording medium characterized by recording the above program. 13. A color distribution of the image is determined from a histogram of the image, a tone correction condition for correcting the image is determined based on the determination, and the image correction is performed using the determined tone correction condition. A program for realizing an image processing method for correcting the image, wherein the determination of the color distribution of the image includes determining a highlight point, a shadow point, and a predetermined range in the histogram from the histogram. A recording medium which is performed based on the cumulative frequency.