JP2003281535A - Image processor and image output device - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To naturally improve acutance of an image by reasonably applying sharpness processing. <P>SOLUTION: The image processor 10 applies the sharpness processing to image data So to output corrected image data Sc. In applying the sharpness processing, an approximate degree of a pixel value of a particular pixel in the image data to a particular color such as a flesh color is determined. A correction amount by the sharpness processing is corrected in response to the approximate degree of the pixel to the particular color. By this, the correction amount of the sharpness processing with respect to the image data is adjusted to an amount responding to a property of the particular color. Namely, if a flesh colored area is included in the image data, the sharpness processing is applied in a method adapted to a flesh colored image in regard to the area. Consequently, if a pixel in the image data is close to the particular color, appropriate sharpness processing can be carried out in regard to the particular color and a sharp image without unnaturalness can be provided. <P>COPYRIGHT: (C)2004,JPO

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an image processing device, and more particularly to a sharpness processing method suitable for displaying an image on a device having a relatively small display area such as a mobile phone or a portable terminal device.

[0002]

2. Description of the Related Art In image display devices and the like, so-called sharpness processing is performed on a displayed image so that the contour of the image is sharpened to make the image easier to see. By the sharpness processing, the slightly blurred original image can be displayed clearly. Also, in devices with a relatively small display area, such as mobile phones and portable terminal devices,
It is known that the sharpness of the sharpness processing makes the contour of the displayed image clear and makes it easier to see.

[0003]

However, there are cases where it is known to some extent how effective the sharpness processing is for the specific image content and the specific color image portion. For example, if too much sharpness is applied to a skin color portion such as an image of a human face, the skin display image may give an unnatural impression. In particular, if too much sharpness is applied to the uneven parts of the face image or the outline parts of the face, the level of the flesh-colored part will be saturated and become white (this phenomenon , Also called "white skipper".).

In order to prevent such a defect, it is sufficient to increase the sharpness with a strength that does not cause defects such as blown-out highlights. On the other hand, if the sharpness processing is too weak, Depending on the image, the sharpness is insufficient, resulting in a blurred image.

The present invention has been made in view of the above points, and it is an object of the present invention to naturally improve the sharpness of an image by appropriately performing sharpness processing.

[0006]

An image processing apparatus according to the present invention comprises a specific color determining means for determining the degree to which a pixel value of a specific pixel in image data approximates a specific color, and a determination result by the specific color determining means. According to the correction amount correction means for correcting the correction amount by the sharpness processing, and according to the correction amount,
Sharpness processing means for performing sharpness processing on the image data to generate corrected image data.

The above image processing apparatus performs sharpness processing on image data and outputs corrected image data. In performing the sharpness processing, the degree to which the pixel value of a specific pixel in the image data is approximate to a specific color such as a skin color is determined. Then, the correction amount by the sharpness processing is corrected according to the degree to which the pixel approximates the specific color. As a result, the correction amount of the sharpness processing for the image data is adjusted to an amount according to the property of the specific color. That is, if the image data includes a flesh-colored area, that area is subjected to sharpness processing by a method adapted to the flesh-colored area. Therefore, when the pixel in the image data is close to the specific color, it is possible to perform an appropriate sharpness process for the specific color and obtain a sharp image without an unnatural feeling.

In one aspect of the image processing apparatus, the specific color determining means can output a determination result indicating whether or not the pixel value is close to a specific color. Therefore, the correction amount can be corrected by a simple method by making a binary determination as to whether or not the pixel value is close to the specific color.

The specific color may include at least one of flesh color, sky blue and natural green. Appropriate sharpness processing can be performed on images of human faces by making judgments on skin colors, and appropriate sharpness processing can be made on sky parts in images by making judgments on sky colors. It can be carried out. Further, by determining the natural green color, it is possible to perform appropriate sharpness processing on a natural green image portion such as a forest or a lawn in the image.

In another aspect of the image processing apparatus, the specific color includes a flesh color, and the correction amount correction means can reduce the correction amount for a portion determined to approximate the flesh color. . Further, in still another aspect of the image processing device, the specific color includes a flesh color, and the correction amount correcting unit increases the luminance level of the pixel with respect to the portion determined to be close to the flesh color. It is possible to reduce the correction amount of. In a skin-colored image area such as a human face, if the sharpness processing is performed too strongly, the pixel level may be saturated on the white side, resulting in an unnatural image. Can be reduced to prevent such problems.

In still another aspect of the above image processing apparatus, the specific color determining means determines a specific color probability indicating the degree to which the pixel value approximates a specific color, and the specific color probability is determined by the determination result. And the correction amount correction means corrects the correction amount at a rate according to the specific color probability.

According to this aspect, the degree of approximation to the specific color is numerically indicated by the specific color probability instead of the binary judgment result as to whether or not the specific pixel approximates the specific color. The judgment result is obtained. Therefore, even if it is difficult to completely determine whether or not the color is the specific color by changing the correction degree of the correction amount according to the specific color probability, the specific color of the specific color is changed according to the degree of approximation to the specific color. Sharpness correction suitable for the property can be performed.

In still another aspect of the above image processing apparatus, the image forming apparatus further comprises contour determining means for determining whether or not the specific pixel is within a predetermined range from a contour portion of the area of the specific color. The correction amount correction means changes the correction ratio of the correction amount according to the determination result of the contour determination means.

According to this aspect, the correction amount of the sharpness process for the particular pixel is adjusted depending on whether or not the particular pixel is near the contour portion of the area of the particular color. Therefore, for example, near the contour part of the skin color area, sharpness processing is applied relatively strongly to emphasize the contours of the face, and in areas not close to the contour part, the sharpness processing is weakened to make the skin look smoother. It will be possible.

Further, an image output apparatus of the present invention comprises the above image processing apparatus, image size determination means for determining the image size when outputting the image data, and image size determined by the image size determination means. Is smaller than a predetermined image size, the image processing apparatus outputs control means for executing sharpness processing on the original image data and corrected image data obtained by the original image data or the sharpness processing. And output means.

According to the above image output device, the image size for outputting the image data is determined, and when the output image size is large, the sharpness processing according to the present invention is not performed. On the other hand, when the output image size is small, by performing the sharpness processing according to the present invention, appropriate sharpness correction is performed, and it is possible to obtain a clear image with sharpening even in a small output image.

[0017]

BEST MODE FOR CARRYING OUT THE INVENTION Preferred embodiments of the present invention will be described below with reference to the drawings.

[Apparatus Configuration] FIG. 1 shows a schematic configuration of a main part of an image processing apparatus according to an embodiment of the present invention. As shown in FIG. 1, the image processing apparatus 10 of the present invention includes a sharpness processing section 11, a driver 12, and a display section (or a printing section) 13. The image processing apparatus 10 of the present invention is preferably applied to a terminal device including a relatively small display unit such as a mobile phone or a mobile terminal device. Further, the image processing apparatus 10 of the present invention can be applied to a color printer or the like, and in that case, the printing unit 13 is provided.

The original image data So is input to the sharpness processing unit 11. When the image processing device 10 is a mobile phone or the like, the original image data So is image data received by the mobile phone via the communication path. When the image processing device 10 is a printer, the original image data So is image data supplied to the printer from a personal computer or the like.

The sharpness processing section 11 performs sharpness processing on the input original image data So to generate corrected image data Sc, and supplies the corrected image data Sc to the driver 12. The driver 12 uses the corrected image data S
The display unit (or print unit) 13 is driven according to c.
Thereby, the image is displayed or printed.

FIG. 2 shows the configuration of the sharpness processing section 11. As shown in FIG. 2, the sharpness processing unit 11 includes a correction reference amount determination unit 15, a correction amount calculation unit 16, a sharpness processing unit 17, and a specific color determination unit 18. The original image data So input to the sharpness processing unit 11 includes the correction reference amount determining unit 15 and the specific color determining unit 1.
8 and the sharpness processing means 17.

The correction reference amount determining means 15 determines the correction reference amount using the original image data So. Here, the correction reference amount is an amount indicating the degree of sharpness processing to be performed on the original image data So, and is specifically a concept including a Laplacian signal and a difference signal, which will be described later. The correction reference amount calculation means 15 generates a correction reference amount signal Sr based on the original image data So, and supplies this to the correction amount calculation means 16.

The specific color determining means 18 detects a specific color portion included in the original image. Here, the specific color includes flesh color, sky blue and natural green. Specifically, the specific color determination unit 18 analyzes the color data of each of the RGB colors of the original image data So and determines the specific color portion included in the original image. Then, when the specific color determination unit 18 detects the specific color portion included in the original image, the specific color determination unit 18 supplies the determination result signal Sk to the correction amount calculation unit 16. As will be described later in detail, the present invention relates to a specific color portion included in the original image data,
The sharpness processing suitable for the property of the specific color is performed.

The correction amount calculation means 16 includes a correction reference amount signal S.
The correction reference amount indicated by r and the determination result signal Sk
The correction amount by the sharpness processing is calculated based on the presence or absence of the specific color portion indicated by, and the correction amount signal Sa is supplied to the sharpness processing means 17. The correction amount corresponds to the amount by which the level of image data to be sharpened is changed by the sharpness processing. That is, when the absolute value of the correction amount is large, the level of the image data is greatly changed by the sharpness processing, and the sharpness of the image is greatly increased. On the other hand, when the absolute value of the correction amount is small, the level change of the image data due to the sharpness processing is small and the sharpness of the image does not increase so much. Further, whether the correction amount is positive or negative indicates whether the image data is changed to the white side or the black side by the sharpness processing.

The sharpness processing means 17 changes the level of the original image data So according to the correction amount indicated by the signal Sa supplied from the correction amount calculating means 16 to generate corrected image data Sc which is image data after sharpness processing. . The sharpness processing means 17 supplies the generated corrected image data Sc to the driver 12 shown in FIG.

[Specific Color Judgment Process] Next, the specific color judgment process executed by the specific color judgment means 18 will be described in detail. The present invention is characterized in that a specific color portion in an image is detected and sharpness processing is performed by a method adapted to the property of the specific color. In this embodiment, flesh color, sky blue, and natural green are determined as the specific colors. The skin color is the color of human skin, and is determined in order to provide appropriate sharpness to the human face portion in the image. The sky blue color is mainly determined in order to give appropriate sharpness to the sky portion in the image. In addition, the natural green color is determined mainly for giving appropriate sharpness to the forest, lawn, etc. in the image. In addition, "natural green"
The expression "and" is used to distinguish from the general green (G) in RGB, and is used in the sense of green corresponding to images of natural forests, lawns, trees, and the like.

FIG. 3 shows flesh color, sky blue and natural green as RGB.
And YMC (Y: yellow, M: magenta, C: cyan) components are shown on the color chart. In this way, the specific colors of skin color, sky blue and natural green are the R included in it.
It can be specified by the ratio of GB components. Therefore,
If the determination condition for each specific color is set in advance based on the color component of each specific color, the specific color determination can be performed based on the above determination condition for the pixel to be determined in the image data. . Then, by performing the sharpness processing according to the processing method determined in advance for each specific color, it is possible to perform the sharpness processing suitable for the property of the specific color.

FIG. 4 shows the judgment conditions and processing methods for each specific color. In FIG. 4, the “determination condition” column shows the condition required to determine a certain part in the image data as each specific color, and a certain pixel or a certain region in the image data satisfies this determination condition. If so, the pixel or the region is determined to have a specific color. Further, the column of “processing method” indicates by what method the sharpness processing is performed when it is determined that each of the specific colors.

Hereinafter, with reference to FIGS. 3 and 4, the determination conditions and processing methods for each specific color will be described.

The flesh-colored portion has a relatively large amount of R components of RGB, and the luminance level belongs to a region from gray to white. Therefore, (1) the brightness level is higher than the intermediate level between white and black, and the RGB components of the image data,
(2) The value of (RB) is larger than the preset threshold value RB, and (3) The value of (RG) is larger than the preset threshold value RG. When the two conditions are satisfied, the pixel or area is determined to be a skin color.

The flesh color is intended mainly for human faces, and it is better to sharpen the contours of the face, the eyes, the nose and other parts with sharpness. On the other hand, the other part, for example, the cheeks, may look smoother if it is not sharpened by the sharpness processing. In particular, in the case of a face image, if the sharpness processing is too strong, there is a problem that a so-called “whiteout” occurs in which the brightness level is saturated on the white side. Therefore, the sharpness processing is applied to the contour portion with a normal correction amount, and the correction amount is reduced in the portion other than the contour portion. In addition, it is also possible to reversely smooth a portion other than the contour such as a cheek portion to obtain a smooth image.

The sky blue portion has a relatively large B component of RGB, and the luminance level belongs to a gray to white region. Therefore, (1) the brightness level is higher than the intermediate level between white and black, and the RGB components of the image data,
(2) The value of (B-G) is larger than the preset threshold value BG, and (3) The value of (BR) is larger than the preset threshold value BR. 3 When one of the two conditions is satisfied, the pixel or area is determined to be sky blue.

The sky color is aimed at the sky in the image, and basically, it is better to suppress the correction amount of the sharpness processing to obtain a smooth image, and it is also effective to perform the smoothing processing at this point. However, it is preferable that the boundary between the sky and a region other than the sky is sharpened by sharpness processing to clarify the boundary. Therefore, the sharpness processing is performed on the boundary portion with the normal correction amount, and the correction amount of the sharpness processing is reduced on the portion other than the boundary portion.

The natural green portion has a relatively large G component of RGB. However, since the brightness level varies from a level close to black to a level close to white, there is not much effect of using the brightness level for determination. Therefore, for the RGB components of the image data, (1) the value of (GR) is larger than a preset threshold GR, and (2)
When the two conditions that the value of (GB) is larger than a preset threshold value GB are satisfied, the pixel or region is determined to be a natural green color.

The natural green color corresponds to various images such as forests, trees, mountains, lawns, fields, etc., but it is clearer and more impactful for the image to express a fine shape by the branches and leaves of the tree. Becomes Therefore, unlike flesh color and sky blue, it is preferable to sharpen the image by performing sharpness processing as a whole.

Here, the method for determining the specific color has been described by way of example, but the intermediate level value does not necessarily have to be at the center between the white level and the black level and may be a specific value or a specific value. Instead of the RGB space for color judgment, the image data is converted to another color space such as L * a * b * space, and R
There is also a method of determining in a color space different from the GB space.
The present invention can be applied regardless of the specific method of the specific color determination means.

The determination of the specific color is basically performed for each pixel forming the image data according to the determination method as illustrated in FIG. 4, but whether or not the pixel is located at the contour portion or the boundary portion. The determination will be made in consideration of adjacent pixels. That is, a determination area defined by a predetermined number of pixels is set in advance (for example, an area of 10 pixels in each of the vertical and horizontal directions), and whether or not the target pixel is located in the contour portion or the boundary portion in the determination area. To judge. For the extraction of contours and boundaries, various known contour extraction methods such as a method using Laplacian described later can be used. If the contour or the boundary is extracted by a known method and the contour or the boundary is located within a predetermined number of pixels (for example, 3 pixels) from the target pixel, it is determined that the pixel belongs to the contour / boundary portion, and within the range. If no contour or boundary is located at, it is determined that the pixel does not belong to the contour / boundary portion.

[Correction Reference Amount] Next, the correction reference amount will be described. The correction reference amount is a reference amount for determining the correction amount for performing the sharpness processing, and the correction reference amount signal Sr is a signal indicating this reference amount. Specifically, either the Laplacian signal of the original image data So or the difference signal generated from the original image data So can be used as the correction reference amount signal Sr.

First, the case where the Laplacian signal is used as the correction reference amount signal will be described. FIG. 6A shows
An example of waveforms of the original image data So, the correction reference amount signal Sr, the correction amount signal Sa, and the correction image data Sc when the Laplacian signal is used as the correction reference amount signal is shown. In addition,
In FIG. 6A, the vertical axis direction of each waveform represents the luminance of the original image data, and the upper direction in the figure is white and the lower direction is black. The horizontal axis of each waveform represents time.

The Laplacian signal is obtained by applying filtering by the Laplacian filter to the original image data So. The Laplacian filter is
It is a kind of differential filter used for spatial filtering of images, and is a filter that mathematically performs a process corresponding to a quadratic differential operation. Examples of coefficients of the Laplacian filter are shown in FIGS. 6B and 6C. The Laplacian filter has a function of emphasizing a contour portion or a high frequency component in which the density in the image changes abruptly. As shown in FIG. 6 (a),
The Laplacian signal has unevenness in the waveform at a portion where the level of the original image data So starts increasing and a portion where the level ends after the increase reaches a constant level. The Laplacian signal is negative in the portion where the waveform of the original image data So is convex downward, and is positive in the portion where the waveform of the original image data So is convex upward. The Laplacian signal can be used as a correction reference amount signal to determine the degree of sharpness processing.

Next, the difference signal will be described. Figure 7
Original image data So, unsharp data Su, correction reference signal S when a difference signal is used as the correction reference amount signal
The waveform examples of r, the correction amount signal Sa, and the correction image data Sc are shown. Note that, in FIG. 7, the vertical axis direction of each waveform represents the luminance of the original image data, and the upper direction in the figure is white and the lower direction is black. The horizontal axis of each waveform represents time. The difference signal is
This signal is obtained by subtracting the unsharp data Su from the original image data So, and is a signal indicating a contour portion or a high frequency portion in the image, like the Laplacian signal. The unsharp data Su is generated by applying an averaging filter to a predetermined range of the original image data So.

That is, as shown in FIG. 7, the original image data S
Unsharp data Su by locally averaging o
Is obtained, and the difference signal Sr is obtained by subtracting this from the original image data So. Therefore, Sr = So-Su
Becomes

Both the Laplacian signal and the difference signal are signals indicating the contour portion and the high frequency portion in the image, and both can be used as the correction reference amount signal in the present invention. However, depending on the method of generating the unsharp signal,
The Laplacian signal and the difference signal do not always match.

[Method of Determining Correction Amount] Next, the method of determining the correction amount by the correction amount calculation means will be described in detail. In the present invention, first, the correction amount used in the sharpness processing is determined based on the correction reference amount obtained by the correction reference amount determination means 15. Then, the correction amount is corrected based on the determination result of the specific color obtained by the specific color determination means 18. That is, first, when the correction amount is determined based on the correction reference amount and the specific image is included in the target image, the correction amount is corrected according to the processing method corresponding to the specific color as described above. It

Specifically, the correction amount is defined as a function of the correction reference amount with correction amount = F (Sr), and the correction amount is determined according to the correction reference amount Sr. Then, the correction amount is corrected by multiplying the correction amount by the correction coefficient according to the specific color determination result. That is, assuming that the correction coefficient for the specific color is M, the correction image data Sc = the original image data So + the correction amount = the original image data So + F (Sr) · M.

The correction coefficient M for the specific color is set to M = 1 when the sharpness processing is performed normally, and is set to 1.0 when the sharpness processing is applied more strongly than usual.
Is set in the range from 1.5 to 1.5, and M is set in the range from 0.6 to 1.0, for example, in order to suppress sharpness processing more than usual. Further, the correction coefficient M is applied to the pixel or region which is not determined to be a specific color by the specific color determination unit 18.
= 1 and the correction amount F calculated based on the correction reference amount
Sharpness processing is performed using (Sr) as it is. The numerical value of the correction coefficient M is determined in advance as illustrated in the processing method for each specific color in FIG. 4, and is stored in the memory or the like.

In this way, by correcting the correction amount determined based on the correction reference amount based on the specific color determination result, it is possible to perform the sharpness processing suitable for each specific color.

[Method of Determining Correction Amount Based on Correction Reference Amount]
Next, a specific method of determining the correction amount based on the correction reference amount Sr will be described with reference to the correction amount determination patterns illustrated in FIGS. In each of the correction amount determination patterns of FIGS. 8 to 12, the horizontal axis represents the correction reference amount Sr, and the vertical axis represents the correction amount of the sharpness processing. That is, FIGS.
3A and 3B are graphs showing how the correction amount of sharpness changes with respect to the change of the correction reference amount.

As a method of actually determining the correction amount based on the correction reference amount, the change characteristic of the correction amount with respect to the correction reference amount as shown in FIGS. 8 to 12 is stored in advance in a lookup table or the like. There is a method of obtaining the correction amount by referring to it. Alternatively, there is also a method in which a function that defines the change characteristic of the correction amount with respect to the correction reference amount is prepared in advance, and the correction amount is calculated from the correction reference amount using the function. Which one is actually adopted depends on the processing speed, processing accuracy, etc. required by the apparatus to which the present invention is applied.

FIG. 8A shows an example 1a of the correction amount determining method. Example 1a shows a method in which the sharpness correction amount is proportional to the correction reference amount, that is, a constant multiple of the correction reference amount is added to the original image data to perform the sharpness processing.

FIG. 8B shows an example 1b of the correction amount determining method. In Example 1b, the correction amount is basically proportional to the correction reference amount. However, the correction reference amount is larger than the predetermined value a, or
When it is smaller than the predetermined value -a, the correction reference amount is -a to +.
The increase rate of the correction amount is smaller than that in the case of within the range of a. The fact that the absolute value of the correction reference amount is large indicates that the level of the image in that portion is abruptly changed to the white or black side. Therefore, in such an area where the image level changes abruptly, the increase rate of the correction amount due to the increase of the correction reference amount is somewhat suppressed. That is, the correction amount by the sharpness processing is suppressed in the region where the change in luminance is large, and the image is prevented from being unnatural due to excessive emphasis by the sharpness processing. According to this, as described above, it is possible to prevent the outline portion of the face image from being displayed in a white state due to excessive emphasis of the sharpness processing.

An example 1c shown in FIG. 8C is a modification of the example 1b. When the absolute value of the correction reference amount is less than the predetermined value b, the correction amount is set to zero, that is, the sharpness correction is not performed. Is the method. The correction reference amount indicates a sharp change in the level of the image data, which means that the level of the image data greatly changes in the region where the correction reference amount is large. On the other hand, in the area where the correction reference amount is small, the level of the image data does not change so much. That is, a relatively small change in the correction reference amount does not correspond to a level change due to the content of the image itself, but often corresponds to noise added to the image data. From this point of view, when the absolute value of the correction reference amount is less than the predetermined value b, it is considered that such a change in the correction reference amount is due to noise, and the sharpness processing is not performed. When sharpness is applied to a region with a large amount of noise in an image, the noise tends to be emphasized and increased to make the image difficult to see. Therefore, as in Example 1c, a change in the correction reference amount equal to or less than the predetermined value b is referred to as noise. It is effective to consider the image and exclude it from the sharpness processing target in order to easily display an image with a relatively large amount of noise.

The example 1d shown in FIG. 8D is basically the example 1c.
Based on the same idea, the sharpness processing is not performed in the area where the absolute value of the correction reference amount is less than the predetermined value c. However, in Example 1d, as the correction reference amount enters from the region where the sharpness processing is not performed to the region where the sharpness processing is performed (that is, when the correction reference amount increases from less than the predetermined value c and exceeds the predetermined value c). , The correction amount is gradually increased from zero. As a result, it is possible to prevent the image from becoming unnatural due to a sharp change in the application or non-application of the sharpness processing in the region where the absolute value of the correction reference amount is close to the predetermined value c.

In both Example 2a and Example 2b shown in FIG. 9, the correction amount is proportional to the correction reference amount, but the correction amount is different between the region where the correction reference amount is positive and the region where the correction reference amount is negative. The feature is that the rate of increase is different.

The correction reference amount is defined by the target pixel and
It is determined by the relative level difference between pixels around the pixel, and is an amount irrelevant to the level of the target pixel itself. If the correction reference amount is positive, it indicates that the pixel data is convex upward, that is, the pixel is closer to the white side than the peripheral pixels. When the correction reference amount is negative, the pixel data is convex downward, that is, the pixel is closer to the black side than the peripheral pixels. In the sharpness processing, generally, when the correction reference amount is positive, the sharpness correction amount is also corrected to the positive side, that is, the white side, and when the correction reference amount is negative, the sharpness correction amount is also corrected to the negative side, that is, the black side. . However, when the correction reference amount is small, the sharpness correction may not be performed in some cases. That is, even if the correction reference amount is positive, the correction is not performed on the white side, and even if the correction reference amount is negative, the correction is not performed on the black side in some cases.

In Example 2a, the area in which the correction reference amount is positive (that is, the image data is convex upward, that is, the corresponding pixel is closer to the white side than the peripheral pixels, and the area to be corrected is the white direction) ) Is more negative than the area in which the correction reference amount is negative (that is, the image data is convex downward, that is, the pixel is closer to the black side than the surrounding pixels, and the correction direction is black). The increase rate of the correction amount is suppressed. This example is particularly effective for preventing the phenomenon that part of the human face described above becomes white. Since the brightness level of human skin color is higher than that of white, in a place on the display image where there is a rapid change in brightness in the brightness region of the image data that is higher than white, the skin color region is It is possible to effectively prevent the defect that the white portion is removed.

On the other hand, in the example 2b, in the area where the correction reference amount is negative, the increase rate of the correction amount is suppressed more than in the area where the correction reference amount is positive. This is the opposite of the idea of preventing the skin-colored portion from becoming white, but depending on the source of the image data and / or the processing method of the image data, the original image data that is the target of the sharpness processing is more than black. In the case where the brightness level of 1 has a unique characteristic in which noise is likely to be contained, it is effective for suppressing such noise.

Both Example 3a and Example 3b shown in FIG. 10 are methods for suppressing the increase rate of the correction amount when the correction reference amount exceeds the predetermined value d, and this also prevents the defect of white spots in the skin color part. Is effective for. In Example 3a, if the correction reference amount is equal to or less than the predetermined value d, the correction amount is determined with the same increase rate with respect to the correction reference amount even if the correction reference amount is negative. On the other hand, in Example 3b, the increase rate of the correction amount with respect to the correction reference amount is suppressed even when the correction reference amount is smaller than the predetermined value e.

In each of Examples 4a to 4d shown in FIG. 11, the increase rate of the correction amount in the region where the correction reference amount is positive is set smaller than that in the region where the correction reference amount is negative. As a result, the white spots of the skin color are prevented as described above. Further, in both cases, when the absolute value of the correction reference amount is less than the predetermined value f, the correction amount is set to zero and the sharpness processing is not performed to prevent noise from being emphasized. Example 4
In a, the correction amount is discontinuously determined in a region where the absolute value of the correction reference amount is the predetermined value f. That is, the sharpness process is not performed until the absolute value of the correction reference amount reaches f, but when the absolute value of the correction reference amount becomes f, the sharpness process is performed with a predetermined correction amount. On the other hand, in Examples 4b and 4c, when the absolute value of the correction reference amount becomes f, the correction amount gradually increases from zero, and the image is gradually emphasized by the sharpness process. . In Example 4b, after the absolute value of the correction reference amount becomes f, the correction amount first increases in a curve and then linearly, whereas in Examples 4c and 4d, the absolute value of the correction reference amount is After reaching f, the correction amount increases linearly from the beginning. In addition, the example 4c is an example in which the correction amount increases along two straight lines having different inclinations, and the example 4d is an example in which the correction amount increases along one straight line.

In Example 5 shown in FIG. 12A, sharpness processing is not performed when the absolute value of the correction reference amount is small, and the correction reference amount is in the positive direction (the degree of convexity in the white direction) and When increasing in the negative direction (the degree of convexity in the black direction), the value for starting the sharpness processing is made different. That is, when the correction reference amount increases in the positive direction, sharpness processing is not performed until the correction reference amount reaches the predetermined value g, whereas when the correction reference amount increases in the negative direction,
Sharpness processing is started with a predetermined value −h (h <g).
Further, when the correction reference amount is larger than the predetermined value j, the correction amount is set to a constant value L1, and when the correction reference amount is smaller than the predetermined value −k, the correction amount is set to a constant value L2. That is, when the correction reference amount exceeds the predetermined value, the correction amount is maintained at a constant value,
Prevents excessive emphasis due to sharpness processing. Also,
Even in that case, when the correction reference amount increases in the white direction, the correction amount is maintained at a value (L1) smaller than that in the case where the correction reference amount increases in the black direction to prevent white spots in the skin color portion.

In Example 6 shown in FIG. 12B, the correction amount is set stepwise with respect to the correction reference amount. According to this method, when the correction amount based on the correction reference amount is determined by using the look-up table, the amount of data to be stored in the look-up table can be reduced, and based on the correction reference amount. When the correction amount is determined by calculation using a predetermined function, there is an advantage that the processing load and processing time required for the calculation can be reduced.

Further, as shown in FIG. 12C, a change in the correction amount with respect to the correction reference amount is defined by a predesigned curve according to the characteristics of the image data to be sharpened, and the curve is smoothed. The correction value can also be controlled.

[Sharpness Processing] Next, the flow of sharpness processing according to the present invention will be described. Figure 13
It is a flowchart of the sharpness process of this invention. In the following description, a case where the image processing apparatus that executes the sharpness processing according to the present invention is applied to a mobile phone will be described.

First, the mobile phone receives the original image data So to be displayed and supplies it to the sharpness processing section 11 shown in FIG. 1 (step S1).

The sharpness processing section 11 temporarily expands the supplied original image data So in a work memory or the like (not shown). Then, the correction reference amount determining means 15 determines the correction reference amount using the original image data So, and generates the correction reference amount signal Sr (step S2). The correction reference amount signal Sr may be the Laplacian signal shown in FIG. 6A or the difference signal shown in FIG. 7 as described above. In the case of a Laplacian signal, the correction reference amount determining unit 15 filters the original image data So expanded in the working memory or the like by using a Laplacian filter as shown in FIG. 6B or 6C, for example. Then, the Laplacian signal is generated and used as the correction reference amount signal Sr. On the other hand, when the correction reference amount signal Sr is a difference signal, the correction reference amount determining means 15 uses the predetermined unsharp filter to generate the unsharp data shown in FIG. 7, and the unsharp data from the original image data So. Is subtracted to generate a difference signal, which is used as a correction reference amount signal Sr.

Next, the specific color judging means 18 shown in FIG.
Using each judgment condition illustrated in FIG. 4, the original image data S
The specific color portion included in o is determined, and the determination result signal Sk
Is generated and supplied to the correction amount calculation means 16 (step S
3).

Next, the correction reference amount signal S thus obtained
The correction amount calculating means 16 shown in FIG. 2 calculates the correction amount using r and the determination result signal Sk to generate the correction amount signal Sa (step S4). Specifically, the correction amount calculation means 1
6 calculates the correction amount F (Sr) using one of the correction amount determination patterns illustrated in FIGS. 8 to 11. The correction amount calculation means 16 refers to the look-up table for each correction reference amount included in the correction reference amount signal Sr, or performs a calculation according to a function to correct the correction amount F.
(Sr) is calculated.

Next, the correction amount calculating means 16 determines whether the determination result signal Sk indicates any of flesh color, sky blue or natural green, and if any of the specific colors is indicated, the determination is made. The correction amount is corrected by multiplying the correction amount F (Sr) by the correction coefficient M shown in the processing method corresponding to the color (see FIG. 4), and the corrected correction amount is corrected by the correction amount signal Sa.
Is output to the sharpness processing means 17. When the determination result signal Sk indicates that no specific color is detected, the correction amount calculation means 16 sets the correction coefficient M = 1 and the correction calculated based on the correction reference amount signal. The amount is output as it is to the sharpness processing means 17 as the correction amount signal Sa.

The sharpness processing means 17 performs sharpness processing on the original image data So based on the correction amount signal Sa to generate corrected image data Sc (step S5). Specifically, as shown in FIGS. 6A and 7, the correction amount signal Sa is added to the original image data So to generate the correction image data Sc. Then, the generated corrected image data Sc is output to the driver 12.

The driver 12 displays the received corrected image data Sc on the display unit 13 (step S6). Thus, the sharpness processing according to the present invention is performed, and the corrected image data Sc corrected by the sharpness processing is displayed on the display unit 13. Although the specific color is described here as three colors of skin color, sky blue, and natural green, at least one of them may be used, or any other color may be set as the specific color.

[Modification] In the above description, the specific color area included in the image data is detected based on the determination condition illustrated in FIG. That is, a certain area in the image data is
2 whether it is a specific color such as skin color, sky blue or natural green
The value is being judged. However, it is often difficult to accurately determine whether or not the color is the specific color. Therefore, the concept of a specific color probability indicating the possibility (likelihood) of the specific color is introduced instead of the binary judgment of whether or not the color is the specific color, and the probability of the specific color is determined according to the magnitude. It is effective to adjust the sharpness processing method.

Specifically, the specific color judging means 1 shown in FIG.
Reference numeral 8 determines, based on the original image data So, a specific color probability indicating that a region included in the image has a specific color (likelihood), and uses the value as a determination result signal Sk to perform a correction amount calculation means. Supply to 16.

An example of the method of calculating the specific color probability will be described with reference to FIG. FIG. 5 shows an example of conditions for determining a skin color probability for a skin color that is one of the specific colors. The determination of the skin color probability is basically similar to the specific color determination condition for skin color shown in FIG. That is, it is determined whether the brightness level of the target image area satisfies the condition of "brightness level> intermediate level" shown in FIG. And if you are satisfied,
The determination is performed using a plurality of threshold values for the (RB) value and the (RG) value. That is, although only the threshold value RB and the threshold value RG are used in the binary determination of whether the color is a skin color, in the example of the method of determining the skin color probability shown in FIG.
B) For values, use thresholds RB1 to RB5,
For the (RG) value, determination processing is performed using threshold values RG1 to RG5. Here, the threshold values RB1 to RB5
Is set so that RB1 is the largest and the value decreases toward RB5. (RB) value and (RB-
Regarding the G) value, the probability of being a skin color is high at a certain value, and the probability of being a skin color is lower as the value is farther from that value. In this example shown in FIG. 5, the threshold value RB
2, RB2 is the closest to the skin color (RB) value and (RB-
G) value, and the skin color probability at that time is 90%. Therefore, in FIG. 5, as understood from the values of the skin color probability corresponding to the threshold levels 1 to 5, (RB)
As the value and the (RG) value deviate from the values of RB2 and RG2, respectively, the skin color probability decreases.

The specific color determination means 18 compares the (RB) value and the (RG) value in a certain area of the image data with five threshold levels illustrated in FIG. 5, and (RB) If the value and the (RG) value satisfy the threshold level 1, it is determined that the skin color probability is 880%, and the threshold level 1 is not satisfied, but the threshold level 2 is satisfied. If so, the skin color probability is determined to be 90%. In this way, the skin color probability for the target image area can be determined. Similarly, by setting a plurality of threshold levels for sky blue and natural green, specific color probabilities such as sky blue probability and natural green probability can be determined.

When the specific color probability is determined by the specific color determination means 18, the correction amount calculation means 16 corrects the correction amount according to the specific color probability. Specifically, a correction coefficient M ′ is prepared for each specific color probability, and this is multiplied by the correction amount F (Sr) determined based on the correction reference amount to obtain the correction amount S ′.
It suffices to determine a. The correction coefficient M ′ may be a value obtained by adjusting the correction amount when the image area is determined to be a specific color according to the specific color probability with the specific color probability. For example, assuming that the correction coefficient M is M = 1.2 when it is determined that the color is natural green as shown in FIG. 4, the correction coefficient M ′ = 1 when the natural green probability is 90%. .17,
When the natural green color probability is 80%, M '= 1.15, etc., and the probability is lower than that in the case where the natural green color is determined to be completely natural (that is, the natural green color probability is 100%). The sharpness processing peculiar to the specific color may be reduced by a certain amount.

By doing so, even if it is difficult to clearly determine whether or not the image area in the image is the specific color, the sharpness processing unique to the specific color is taken into consideration in consideration of the possibility of the specific color. It can be performed.

[Other Modifications] Also, depending on the image display device and the like, a predetermined color correction or the like may be performed on the image area determined to be a specific color. For example, color data may be corrected so that an image area determined to have a skin color is displayed as the most natural skin color on the image display device. In such an apparatus, the sharpness process for each specific color may be applied to the corrected color data according to the present invention.

In the above embodiment, the correction amount of the sharpness process is adjusted for the specific color according to the property of the specific color. However, not only the correction amount of the sharpness process is adjusted but also FIG. It is possible to display an image more naturally by combining smoothing processing and the like as illustrated in FIG.

[Application Example] Next, an application example of the sharpness processing of the present invention will be described. First, a first application example will be described with reference to FIG.

FIG. 14A shows an example in which the sharpness processing of the present invention is applied to the transmission of image data via a network. The server, which is the image sender, transmits certain image data to the recipient A and the recipient B via the network.
Recipient A uses a mobile phone as an image display device, and recipient B uses a personal computer (P
C) is used. Here, the server grasps what the image display device used by each of the recipients A and B (whether it is a mobile phone or a PC) from the data included in the communication signal. That is, during communication with the recipient A, the server receives device type information indicating that the device used by the recipient A is a mobile phone, and during communication with the recipient B. The device type information indicating that the device used by the recipient B is a PC is received from the PC.

When the server receives a transmission request for specific image data from the receiver A, the server transmits the requested image data for the image data. At that time, since the display area of the mobile phone is generally small, the image data to be transmitted is subjected to the sharpness processing according to the present invention and then transmitted. Since the mobile phone of the recipient A displays the image data received from the server as it is on the display unit of the mobile phone,
An image in which the image is effectively sharpened by the sharpness processing is displayed even on the display unit of a mobile phone having a relatively small display area.

When the server receives a request for transmitting specific image data from the receiver B, the server transmits the requested image data for the image data. At that time, since the display area of the PC is generally relatively large, the image data to be transmitted is transmitted without being subjected to the sharpness processing according to the present invention. Since the recipient B's PC directly displays the image data received from the server on the display unit of the mobile phone, an image subjected to appropriate sharpness processing is displayed on the display unit of the mobile phone having a relatively large display area. To be done.

If the device type information that can be received by the server from the mobile phone or PC includes information indicating the size of the image display area of the mobile phone or PC, the server device determines the image display area of the image display area. Depending on the size, it can be determined whether or not the sharpness processing of the present invention is applied to the image data to be transmitted.

Next, a second application example will be described with reference to FIG. The second application example is one in which the sharpness processing of the present invention is applied to print output of an image by a color printer. The printer receives print instructions and print data from an external PC or the like. The print instruction includes print size information indicating the size of image data to be printed. Therefore, the printer refers to the print size information and prints the print data without performing the sharpness processing according to the present invention when performing printing larger than a predetermined print size. On the other hand, when the print size information included in the print instruction specifies a print size smaller than the predetermined print size, the print data after the sharpness processing according to the present invention is applied is printed. This allows
When the size of the print data is small, the sharpened image data is printed by the sharpness processing of the present invention, so that a clear image can be printed even when the print size is small.

[Brief description of drawings]

FIG. 1 shows a schematic configuration of a main part of an image processing apparatus that performs sharpness processing according to the present invention.

FIG. 2 shows a configuration of a sharpness processing section shown in FIG.

FIG. 3 shows characteristics of each specific color on a color chart.

FIG. 4 shows an example of a specific color determination method.

FIG. 5 shows an example of skin color probability determination conditions for a skin color that is one of the specific colors.

FIG. 6 shows an example of each signal when a Laplacian signal is used as a correction reference amount signal and an example of coefficients of a Laplacian filter.

FIG. 7 shows an example of each signal when a difference signal is used as a correction reference amount signal.

FIG. 8 shows an example of a correction amount determination pattern used by the sharpness processing of the present invention.

FIG. 9 shows another example of a correction amount determination pattern used by the sharpness processing of the present invention.

FIG. 10 shows still another example of the correction amount determination pattern used by the sharpness processing of the present invention.

FIG. 11 shows still another example of the correction amount determination pattern used by the sharpness processing of the present invention.

FIG. 12 shows still another example of the correction amount determination pattern used by the sharpness processing of the present invention.

FIG. 13 is a flowchart of sharpness processing of the present invention.

FIG. 14 shows an application example of the sharpness processing of the present invention.

[Explanation of symbols]

10 Image processing device 11 Sharpness processing section 12 drivers 13 Display section (print section) 15 Correction reference amount determining means 16 Correction amount calculation means 17 Sharpness processing means 18 Specific color determination means

Front page continuation (51) Int.Cl. ⁷ Identification code FI theme code (reference) H04N 1/60 H04N 1/46 ZF term (reference) 5B057 CA01 CA08 CA12 CA16 CB01 CB08 CB12 CB16 CC03 CE03 CE17 DA08 DA17 DB02 DB06 DB09 DC16 DC25 DC36 5C077 LL01 LL19 MP07 MP08 PP03 PP09 PP32 PP43 PQ03 PQ05 PQ12 5C079 HB01 LA05 LA15 MA11 MA20 NA02 NA06 5L096 AA02 AA06 DA01 FA06 FA15 GA30 GA38 MA03

Claims (8)

[Claims] 1. A correction amount by sharpness processing is corrected according to a specific color determination means for determining the degree to which a pixel value of a specific pixel in image data approximates to a specific color, and a determination result by the specific color determination means. An image processing apparatus comprising: a correction amount correction unit; and a sharpness processing unit that performs a sharpness process on the image data according to the correction amount to generate corrected image data. 2. The image processing apparatus according to claim 1, wherein the specific color determination unit outputs a determination result indicating whether or not the pixel value is close to a specific color. 3. The image processing apparatus according to claim 1, wherein the specific color includes at least one of flesh color, sky blue, and natural green. 4. The specific color includes a flesh color, and the correction amount correction means reduces the correction amount for a portion determined to be close to the flesh color.
The image processing apparatus according to any one of items 1 to 3. 5. The specific color includes a flesh color, and the correction amount correction means decreases the correction amount in a direction of increasing a luminance level of a pixel in a portion determined to be close to the flesh color. The image processing apparatus according to any one of claims 1 to 3. 6. The specific color determination means determines a specific color probability indicating the degree to which the pixel value approximates a specific color, and outputs the specific color probability as the determination result, the correction amount correction means, The image processing apparatus according to claim 1, wherein the correction amount is corrected at a rate according to the specific color probability. 7. A contour determination unit for determining whether or not the specific pixel is within a predetermined range from a contour portion of the region of the specific color, wherein the correction amount correction unit is the contour determination unit. 7. The image processing apparatus according to claim 1, wherein the correction ratio of the correction amount is changed according to the determination result of. 8. The image processing apparatus according to claim 1, an image size determination unit that determines an image size when the image data is output, and an image size determination unit that determines the image size. When the image size is smaller than a predetermined image size, the image processing device performs a sharpness process on the original image data, a control unit, and the corrected image data obtained by the original image data or the sharpness process. An image output device comprising: an output unit that outputs