JP2000224607A - Image processor - Google Patents

Abstract

PROBLEM TO BE SOLVED: To automatically correct a saturation optimum corresponding to the sharpness of an image. SOLUTION: An input image is separated into color data and luminance data by a color converting means 12, the correction quantity of the saturation is calculated from the respective data by a saturation correction quantity calculating means 13, and the saturation is corrected by a saturation correcting means 14. In the saturation correction quantity calculating means 13, concerning the image of a low saturation, the saturation correction quantity is suppressed so that over correction is prevented from being loaded to an achromatic portion. Besides, the image is divided into several areas, the saturation correction is performed on the basis of a saturation level in the area of the highest saturation level, a saturation level in the area of most contours or a saturation level in the area where skin colors are distributed much, and the optimum saturation correction matched with human senses is performed.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an image processing apparatus, and more particularly to adjusting the color saturation of an image in a television, a scanner, a printer, a digital still camera, and the like.
It relates to a device for performing saturation correction.

[0002]

2. Description of the Related Art Conventionally, as a means of correcting saturation for correcting the vividness of an image, for example, there is a method for converting the saturation of an image and a device for converting the saturation of an image disclosed in Japanese Patent Application Laid-Open No. 8-329217. Hereinafter, this conventional example will be described. FIG.
FIG. 1 is a block diagram of a saturation conversion device as a conventional example. 9, reference numeral 111 denotes an image input unit, 112 denotes a buffer, 113 denotes a uniform perceived color space conversion unit, 114 denotes a saturation conversion amount calculation unit, 115 denotes a saturation conversion unit, and 116 denotes an inversely perceived color space conversion unit. Is an image output means.

The operation will be described below. The image to be processed is input by the image input unit 111, and is temporarily stored in the buffer 112.
4 and the uniform perceived color space conversion means 113.
The saturation conversion amount calculating means 114 determines the spread of the saturation of the input image and calculates a coefficient x for increasing the saturation. The uniform perceived color space conversion means 113 converts the input image data into an RGB (red, green and blue) color space or a YMC color space.
(Yellow, magenta and cyan) color space is converted to a uniform perceived color space (such as CIE1976 (L * a * b *)). Then, the saturation is enhanced by multiplying the color component by a factor x by the saturation conversion means 115, the original color space is converted by the inverse uniform perceived color space conversion means 116, and the color is converted by the image output means 117. Outputs an image with emphasized degrees.

When calculating the coefficient x, the saturation conversion amount calculation means 114 increases the value of x in order to emphasize the saturation more strongly, since the image having a low saturation has a small spread of the saturation. To correct the saturation. On the other hand, in an image with high saturation, the spread of saturation is large, and accordingly, the coefficient x
To reduce the amount of emphasis.

The spread of the saturation is calculated by calculating a luminance value, R + G-2G, representing the saturation component of each pixel, creating a histogram representing the number of pixels for each luminance value, and integrating the number of pixels from the larger luminance value. Then, a luminance value at a point exceeding a preset threshold value is used, and based on this luminance value, a saturation correction coefficient x is calculated as shown in FIG.

[0006]

However, according to the conventional image saturation conversion apparatus, a strong saturation enhancement is applied to an image having low saturation, so that, for example, a building appears in the entire image. In a low-saturation image as described above, the saturation of the entire image is determined to be low, so that excessive saturation enhancement is applied, and a color which is originally an achromatic portion is added to the image. Person P in front of building B like
In the case where is standing, the ratio of the building B to the entire screen is large relative to the person P, so that the saturation of the entire image is also determined to be low. Had the problem of being done.

SUMMARY OF THE INVENTION The present invention has been made to solve the above-mentioned conventional problems, and has as its object to provide an image processing apparatus capable of performing optimal saturation correction.

[0008]

An image processing apparatus according to a first aspect of the present invention is an image processing apparatus for correcting the saturation of an image in accordance with the vividness of the image. A saturation correction amount calculating unit that calculates a saturation correction amount from the image data; and a saturation correction unit that performs saturation correction based on the correction amount calculated by the saturation correction amount calculation unit. The amount calculating means calculates a correction amount that gradually increases according to the size of the saturation component when the saturation component of the input image is smaller than the reference value, and calculates the saturation amount when the saturation component is larger than the reference value. The degree of correction is calculated such that the degree of correction decreases gradually in accordance with the magnitude of the saturation component.

According to a second aspect of the present invention, in the image processing apparatus according to the first aspect, the saturation correction amount calculating means extracts a saturation component of the input image. The image processing apparatus includes a component extracting unit and a saturation correction amount calculating unit that calculates a saturation correction amount from the saturation component extracted by the saturation component extracting unit.

According to a third aspect of the present invention, in the image processing apparatus according to the first aspect, the saturation correction amount calculating means divides the input image into a plurality of areas. Means, a saturation component extraction means for extracting a saturation component of image data for each of the plurality of divided regions, and a saturation component extraction region for each of the plurality of regions. And a chroma correction amount calculating means for calculating a chroma correction amount for correcting the chroma of the entire image based on the chroma component.

According to a fourth aspect of the present invention, in the image processing apparatus according to the first aspect, the saturation correction amount calculating means is configured to divide the input image into a plurality of areas. Means, a contour component extracting means for extracting a contour component of image data for each of the plurality of divided areas, and a contour component averaging means for calculating an average value of the contour components extracted for each of the plurality of areas. A saturation component extracting means for extracting a saturation component of the image data in an area where the average value of the contour components is the largest, and a saturation correction amount for correcting the saturation of the entire image from the extracted saturation component. And a saturation correction amount calculating means.

According to a fifth aspect of the present invention, in the image processing apparatus according to the first aspect, the saturation correction amount calculating means is configured to divide the input image into a plurality of areas. Means, a flesh color component extracting means for extracting a flesh color component of image data for each of the plurality of divided regions, and a chroma component for extracting a chroma component of image data in a region where the flesh color component is distributed most. The image processing apparatus further includes an extraction unit and a saturation correction amount calculation unit that calculates a saturation correction amount for correcting the saturation of the entire image from the extracted saturation component.

[0013]

BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a block diagram showing a first embodiment of the present invention; (Embodiment 1) FIG. 1 is a block diagram of an image processing apparatus according to a first aspect of the present invention, showing a basic configuration of the present invention.
In FIG. 1, reference numeral 11 denotes an image input means for inputting image data to be processed. Reference numeral 12 denotes a color conversion unit that converts a color system of the input image data into a set of luminance data and color data. Reference numeral 13 denotes a saturation correction amount calculating unit that creates saturation data based on the color data of the image, and determines a saturation correction amount from the saturation information. Reference numeral 14 denotes a saturation correction unit that performs saturation correction based on the correction amount of the saturation correction amount calculation unit 13. Reference numeral 15 denotes color conversion means for converting a set of luminance data and color data into a color system of the original image data.
Reference numeral 16 denotes an image output unit that outputs processed image data.

Next, the operation will be described. For example, data obtained by capturing a television signal by video capture, data read from a scanner, data from a digital still camera, and the like are input by the image input unit 11, and the types of the input data are red (R) and green. (G) and blue (B) data.

In the color conversion means 12, the input image data R
GB is converted into luminance data (Y) and color data (Cr, Cb). For example, Rec60 of the International Radio Communication Advisory Committee
In 1-2, the conversion from the image data RGB into the luminance data (Y) and the color data (Cr, Cb) is performed by the conversion formula Y = 0.299R + 0.587 * G + 0.114G Cr = 0.713 (R− Y) Cb = 0.564 (B−Y). The input image is converted into luminance data and color data based on the conversion formula.

The saturation correction amount calculating means 13 calculates saturation data from the input color data (Cr, Cb), and calculates a saturation correction amount based on the saturation level.

FIG. 2 is a block diagram showing the configuration of the saturation correction amount calculating means 13, which corresponds to the second aspect of the present invention. The saturation correction amount calculation means in FIG. 2 includes a saturation data creation means 21 and a saturation level calculation means 22, and includes a saturation component extraction means for extracting a saturation component of the input image, and a saturation correction coefficient generation means. 23 and a saturation correction amount calculating means for calculating a saturation correction amount from the saturation component extracted by the saturation component extracting means.

First, the color data Cr and Cb are converted into the chroma data S by the chroma data creating means 21. The conversion is performed using a conversion equation S = √ (Cr2 + Cb2).

Next, the saturation level calculation means 22 calculates the saturation level of the image, that is, the degree of vividness of the input image, from the saturation data S. The saturation level is determined by the average value or the maximum value of the saturation data S. When calculating with the average value, the saturation level is (saturation level) = (all saturation data) / (total number of pixels).

When the calculation is performed using the maximum value, the maximum value of the input saturation data is not used as it is.
As shown in FIG. 3, when the saturation data of the input image is distributed, the number of distributions of each saturation data exceeds a certain threshold value TH1, and the maximum saturation level A among them exceeds the maximum value. By doing so, the saturation level can be calculated without being affected by noise. Alternatively, a saturation level at which the integrated amount of the distribution from the maximum value of the saturation level exceeds a certain threshold value may be calculated as the maximum value.

Then, a saturation correction coefficient is calculated by the saturation correction coefficient generating means 23 based on the saturation level. The saturation correction coefficient generation means 23 calculates a correction coefficient based on the relationship between the saturation level and the correction coefficient shown in FIG. In FIG. 4, when the saturation level is small, it is assumed that the image is originally close to an achromatic color, and the correction amount is small, and when the saturation level is large, the image is sufficiently vivid and the correction amount is suppressed. Calculate the correction coefficient. That is, a characteristic is provided such that the correction coefficient increases as the saturation level increases up to the saturation level L2, and the correction coefficient decreases as the saturation level increases from the saturation level L2 to L3. When the saturation level is equal to or higher than L3, the saturation level of the image is sufficiently large. Therefore, the correction coefficient is 1, that is, the saturation correction is not performed.

For example, when the correction coefficient is set as K1 in FIG. 4, the correction coefficient for the saturation level is calculated by the following equation. When (saturation level) <L2, (correction coefficient) = (saturation level) / L2 + 1 When L2 ≦ (saturation level) <L3, (correction coefficient) = (saturation level) / (L2-L3) + 2-L2 / (L2-L3) = {(saturation level) -L2} / (L2-L3) +2, and as the (saturation level) increases from L2 to L3, the correction coefficient becomes the maximum value ( In this case, it gradually decreases from 2). When L3 ≦ (saturation level) (correction coefficient) = 1.0 Also, by setting a correction coefficient as indicated by K2 in FIG. It is determined that the image is close to coloring, and the correction coefficient = 1.
The saturation may not be corrected by setting it to 0. By setting the correction coefficient in this manner, it is possible to prevent an image having many achromatic colors from being subjected to excessive saturation correction. Although the relationship between the saturation level and the correction coefficient is determined as shown in FIG. 4, this association is not limited to this as long as the characteristic of the correction coefficient has an upwardly convex shape. Further, the numerical value example of the correction coefficient is not limited to the example of FIG.

The saturation correction means 14 shown in FIG. 1 corrects the saturation using the correction coefficient generated from the saturation correction coefficient generation means 23 shown in FIG. Saturation is corrected by multiplying the color signal converted by the color conversion means 12 by a correction coefficient. That is, for the color data Cr and Cb, the corrected color data Cr ′ and Cb ′ are calculated as Cr ′ = Cr × (correction coefficient) Cb ′ = Cb × (correction coefficient), thereby correcting the saturation. Do.

Next, the color conversion means 15 of FIG. 1 converts the luminance data (Y) and the color data (Cr ', Cb') into the original RGB data. The conversion formula in this case is as follows: R = Y + 1.4026 Cr ′ G = Y−0.7144 Cr′−0.3444 Cb ′ B = Y + 1.7730 Cb ′ The converted image data is output by the image output means 16.

As described above, according to the first embodiment, input image data is converted into luminance data and color data, saturation data is calculated from the color data, and the saturation data is calculated based on the saturation level. While the saturation level is low, the correction coefficient increases as the saturation level increases, and the correction coefficient decreases as the saturation level exceeds a certain value, and decreases when the saturation level of the image is sufficiently high. Is 1, that is, a correction coefficient for not performing the saturation correction is calculated, and the saturation correction is performed by multiplying the color data by the correction coefficient. By returning the image data to the same format as the data, it is possible to prevent the image having many achromatic colors from being subjected to excessive saturation correction, and to perform optimal saturation correction.

(Embodiment 2) Next, a case where an input image is divided into a plurality of regions, a saturation level is calculated for each region, and saturation is corrected will be described below. This corresponds to the invention of claim 3 of the present application. First, the input image is divided into several areas as shown in FIG. 5, and in the example of FIG.
The image data is divided into regions # 1 to # 9, and saturation correction data is created for each region to calculate a saturation level. The saturation correction is performed using the largest saturation level as the saturation level of the entire image. Is what you do.

If the input image is such that a person P stands in front of a building B as shown in FIG. 11, for example, if the saturation level is calculated for the entire image and the saturation correction is performed, then FIG. Areas # 1, # 2, # 3, # 4, # 6, #
In areas corresponding to # 7 and # 9, there are many achromatic components, and therefore, a direction of increasing vividness, that is, a large saturation correction is applied. This is because saturation correction is performed in a form that matches the saturation level of the building. Therefore, the saturation correction amount calculating means 13 shown in FIG. 1 is configured as shown in FIG. 6, and calculates the saturation level for each of the divided areas. When the saturation correction is performed, for example, it is possible to perform the saturation correction based on the person in the region # 5, and the optimum saturation correction can be performed.

The saturation correction amount calculating means shown in FIG. 6 includes an area dividing means 61, and an area dividing means for dividing an input image into a plurality of areas, and a saturation data creating means 62 and a saturation level calculating means 63. And a saturation component extraction unit for extracting a saturation component of image data for each of the plurality of divided regions; a saturation level maximum value calculation unit 64 and a saturation correction coefficient generation unit 65; A saturation correction amount calculating means for calculating a saturation correction amount for correcting the saturation of the entire image based on the saturation component of the region having the largest saturation component from the extracted saturation components It is.

First, the image is divided into a plurality of areas by the area dividing means 61, and the saturation data for each area is created by the saturation data creating means 62, and the saturation level for each area is calculated by the saturation level calculating means 63. Is calculated. Next, the maximum saturation level calculation unit 64 calculates the maximum saturation level from the saturation level of each region, and based on the saturation level, calculates a saturation correction coefficient by the saturation correction coefficient generation unit 65. calculate.

At this time, even if the saturation correction coefficient is determined using the characteristic shown in FIG. 10 of the conventional example, no excessive correction is applied. That is, as described above, the image is divided into a plurality of regions, saturation correction data is created for each region, the saturation level is calculated, and the largest saturation level is set as the saturation level of the entire image. Since the saturation correction is performed, if the image has low saturation in the whole image, it will be shifted to the low saturation part, and even the high saturation part will be shifted. This eliminates the problem that the correction is excessively performed and the excessively high saturation is originally applied to the high saturation portion. Also,
By determining the saturation correction coefficient using the characteristics shown in FIG. 4, more optimal saturation correction can be performed.

As described above, according to the second embodiment, the input image is divided into a plurality of regions, the saturation level is calculated for each region, and the maximum value of the saturation level is obtained. Since the saturation level is used as the saturation level of the entire image and the saturation is corrected, even if the image has a low saturation portion in the entire image, the saturation is low. Since the saturation correction coefficient is not generated based on the portion, it is possible to prevent the portion having originally high saturation from being excessively emphasized.

(Embodiment 3) Next, a case where an input image is divided into regions and a contour component is extracted for each region to correct the saturation will be described below. This corresponds to the invention of claim 4 of the present application. In this case, the saturation correction amount calculating means 13 has a configuration as shown in FIG. The saturation correction amount calculating means in FIG. 7 includes an area dividing means 71, an area dividing means for dividing an input image into a plurality of areas, and an outline data calculating means 72. A contour component extracting means for extracting a contour component of data, and a contour data average value calculating means 73, a contour component averaging means for calculating an average value of the contour components extracted for each of a plurality of regions, and a work area designating means 74 A saturation component extracting unit for extracting a saturation component of image data in a region where the average value of the contour components is the largest, and a saturation correction coefficient generating unit 77. And a saturation correction amount calculating means for calculating a saturation correction amount for correcting the saturation of the entire image from the extracted saturation components.

Both the luminance data and the color data output from the color conversion means 12 in FIG. 1 are input to the saturation correction amount calculation means. The luminance data output from the color conversion unit 12 is divided into regions by the region dividing unit 71, and contour data for each region is calculated by the contour data calculating unit 72. The contour data is calculated from the absolute value of the difference between the target pixel and its surrounding pixels. For example, when contour data is obtained from a target pixel and pixels located above, below, left, and right, (contour data) = | (target pixel data) − (upper pixel data) − (lower pixel data) − (left (Pixel data) − (right pixel data) |

Next, the average value of the outline data for each area is calculated by the outline data average value calculation circuit 73, the area having the largest average value is calculated, and the area for calculating the saturation data is sent to the work area specifying means 74. To specify. According to this work area, the saturation data of the range is created by the saturation data creation means 75, and the saturation level is calculated by the saturation level calculation means 76 using the average value or the maximum value of the saturation, and the saturation correction is performed. Coefficient generating means 7
At 7, a saturation correction coefficient is generated based on the saturation level, and the saturation is corrected.

As described above, according to the third embodiment, the overall saturation is determined on the basis of the saturation level of the region having the largest number of contour components. Since the saturation correction is performed at the center, it is possible to perform an optimally correct saturation correction.

At this time, even if the correction coefficient is determined using the characteristic shown in FIG. 10 of the conventional example, no excessive correction is applied. This is because, even in an image in which a low-saturation portion occupies a large portion in the entire image, a saturation correction coefficient is not generated based on the low-saturation portion. Further, by determining the saturation correction coefficient using the characteristics shown in FIG. 4, the saturation correction is performed centering on the most conspicuous portion in the image, so that the optimum saturation correction can be achieved. It can be carried out.

(Embodiment 4) Next, a case in which an input image is divided into a plurality of regions, skin color components are extracted for each region, and saturation is corrected will be described below. This corresponds to the invention of claim 5 of the present application.
3 has a configuration as shown in FIG. The saturation correction amount calculating means in FIG. 8 includes an area dividing means 81, an area dividing means for dividing an input image into a plurality of areas, and a flesh color data extracting means 82. On the other hand, a skin color component extracting unit for extracting a skin color component of image data, a skin color data distribution number calculating unit 83, a work area designating unit 84, a saturation data creating unit 85, and a saturation level calculating unit 8
6, and a saturation correction coefficient generating means 87 for extracting a saturation component of the image data of the area where the skin color component is distributed most. The saturation of the entire image is calculated from the extracted saturation component. And a saturation correction amount calculating means for calculating a saturation correction amount for correcting the color difference.

First, the color data output from the color conversion means 12 shown in FIG. 1 is divided into a plurality of areas by the area dividing means 81, and a skin color pixel is extracted by the skin color data extracting means 82 for each area. I do. To determine whether the pixel is a skin color, the hue data H is obtained from the color data Cr and Cb, and if the hue data is a numerical value within a certain range, the pixel is determined to be a skin color. This hue data is calculated by the following equation. H = tan-1 (Cr / Cb) FIG. 12 is a diagram showing the relationship between the hue and the flesh color distribution, and the flesh color is distributed in a specific hue region. Therefore, when the hue data H has a value from H1 to H2, it is extracted as a flesh color. The values of H1 and H2 are, for example, H
1 = 110 ° and H2 = 130 °. The skin color data distribution number calculation circuit 83 measures the distribution number of the skin color data for each area. Next, an area in which the number of distributions of the skin color data is largest is calculated, and an area in which the saturation data is calculated is designated as the work area designating means 84.
Specify with.

According to the work area, the saturation data in the range is created by the saturation data creation means 85, and the saturation level calculation means 86 calculates the saturation level by the average value or the maximum value of the saturation. Saturation correction coefficient generation means 87 generates a saturation correction coefficient based on the saturation level, and performs saturation correction.
In this case, the values of H1 and H2 are H1 = 110 ° and H2 = 1.
The angle does not need to be 30 ° and may be set according to the characteristics of the image processing apparatus. In this way, by determining the overall saturation based on the saturation level of the area where the most skin color is distributed, it is possible to perform optimal saturation correction based on the person at the center of the screen it can.

At this time, even if the correction coefficient is determined using the characteristic shown in FIG. 10 of the conventional example, no excessive correction is applied. This is because, even if an image has low saturation in the entire image, saturation correction can be performed based on the person at the center of the screen. This is because there is no need to generate a saturation correction coefficient based on Further, by determining the saturation correction coefficient using the characteristics shown in FIG. 4, more optimal saturation correction can be performed.

[0041]

As described above, according to the image processing apparatus of the first aspect of the present invention, in the image processing apparatus for correcting the saturation of an image in accordance with the vividness of the image, the color data of the input image And a saturation correction amount calculation unit that calculates a saturation correction amount from the luminance data, and a saturation correction unit that performs saturation correction based on the correction amount calculated by the saturation correction amount calculation unit, The saturation correction amount calculating means calculates a correction amount that gradually increases according to the size of the saturation component when the saturation component of the input image is smaller than the reference value, and calculates the correction amount when the saturation component is larger than the reference value. Calculates the amount of saturation correction that gradually decreases in accordance with the size of the saturation component, so when correcting the saturation of an image to match the vividness of the image, The saturation correction amount can be suppressed for low images, There is an effect that it is possible to prevent the portion to excessive correction from being applied.

According to the image processing apparatus of the second aspect of the present invention, in the image processing apparatus of the first aspect, the saturation correction amount calculating means extracts a saturation component of the input image. The image processing apparatus further includes a chroma component extracting unit and a chroma correction amount calculating unit that calculates a chroma correction amount from the chroma component extracted by the chroma component extracting unit. Specific configuration of an apparatus that can suppress the amount of saturation correction for an image with low saturation when correcting according to the image, and can prevent excessive correction from being applied to an achromatic portion. The effect is obtained.

According to the image processing apparatus of the third aspect of the present invention, in the image processing apparatus of the first aspect, the saturation correction amount calculating means divides the input image into a plurality of areas. Region dividing means, a saturation component extracting means for extracting a saturation component of image data for each of the plurality of divided regions, and a largest saturation component from the saturation components extracted for each of the plurality of regions. A means for calculating a saturation correction amount for correcting the saturation of the entire image based on the saturation component of the region, so that achromatic image data and chromatic image data In the case where are mixed, the correction amount can be determined based on the saturation information in the region with the largest saturation, and there is an effect that the optimum saturation correction can be performed.

According to the image processing apparatus of the present invention, in the image processing apparatus of the first aspect, the saturation correction amount calculating means divides the input image into a plurality of areas. Region dividing means; contour component extracting means for extracting a contour component of image data for each of the plurality of divided areas; contour component averaging for calculating an average value of the contour components extracted for each of the plurality of areas Means, a saturation component extracting means for extracting a saturation component of the image data of the region where the average value of the contour components is the largest, and a saturation correction amount for correcting the saturation of the entire image from the extracted saturation component. Since there is provided a saturation correction amount calculating means for calculating, it is possible to determine the correction amount based on the saturation information of a portion having a large number of contour components, that is, a central portion of the image, so that the optimum saturation correction is performed. The effect that can be performed There is.

According to a fifth aspect of the present invention, in the image processing apparatus according to the first aspect, the saturation correction amount calculating means divides the input image into a plurality of regions. Region dividing means, skin color component extracting means for extracting a flesh color component of image data for each of the plurality of divided regions, and chroma for extracting a chroma component of image data of a region where the flesh color component is most distributed. Since the image processing apparatus has the saturation component extracting means and the saturation correction amount calculating means for calculating the saturation correction amount for correcting the saturation of the entire image from the extracted saturation components, the image is most noticed in the image. The saturation correction amount can be calculated based on the saturation information of the person, and there is an effect that the optimum saturation correction suited to human sensitivity can be performed.

[Brief description of the drawings]

FIG. 1 is an overall block diagram of an image processing apparatus according to a first embodiment of the present invention.

FIG. 2 is a block diagram of a saturation correction amount calculating unit according to the first embodiment of the present invention;

FIG. 3 is a distribution diagram of chroma data according to the first embodiment of the present invention.

FIG. 4 is a characteristic diagram of a saturation correction coefficient generation unit according to the first embodiment of the present invention.

FIG. 5 is an explanatory diagram of area division according to Embodiment 2 of the present invention;

FIG. 6 is a block diagram of a saturation correction amount calculating unit according to the second embodiment of the present invention;

FIG. 7 is a block diagram of a saturation correction amount calculating unit according to the third embodiment of the present invention;

FIG. 8 is a block diagram of a saturation correction amount calculating unit according to the fourth embodiment of the present invention.

FIG. 9 is a block diagram of a conventional image saturation conversion device.

FIG. 10 is an explanatory diagram of a conventional image saturation conversion method.

FIG. 11 is a diagram showing an input image sample according to the first to fourth embodiments of the present invention.

FIG. 12 is a diagram showing the relationship between hue and skin color distribution according to the fourth embodiment of the present invention.

[Explanation of symbols]

 DESCRIPTION OF SYMBOLS 11 Image input means 12, 15 Color conversion means 13 Saturation correction amount calculation means 14 Saturation correction means 15 Color conversion means 16 Image output means 21, 62, 75, 85 Saturation data creation means 22, 63, 76, 86 Saturation level calculation means 23, 65, 77, 87 Saturation correction coefficient generation means 61, 71, 81 Area dividing means 64 Saturation level maximum value calculation means 72 Outline data calculation means 73 Outline data average value calculation means 74, 84 Work area Designating means 82 Skin color data extracting means 83 Skin color data distribution number calculating means

 ──────────────────────────────────────────────────続 き Continued on front page F term (reference) 5B057 CA01 CB01 CE17 DC16 DC25 5C066 AA03 AA05 BA20 CA17 DA01 DC01 EA05 EB03 EB07 EB11 EC02 EE02 EF02 GA02 GB01 HA06 JA02 KA13 KD03 KD06 KE01 KM11 LA02 5C037 PP08 LA02 NA01

Claims (5)

[Claims] 1. An image processing apparatus for correcting the saturation of an image in accordance with the vividness of the image, comprising: a saturation correction amount calculating means for calculating a saturation correction amount from color data and luminance data of an input image; Saturation correction means for correcting the saturation based on the correction amount calculated by the saturation correction amount calculation means, wherein the saturation correction amount calculation means is provided when the saturation component of the input image is smaller than a reference value. Calculates a correction amount that gradually increases according to the size of the saturation component, and when the saturation component is larger than the reference value, the correction amount that gradually decreases according to the size of the saturation component An image processing apparatus for calculating an amount. 2. The image processing apparatus according to claim 1, wherein the saturation correction amount calculating unit extracts a saturation component of an input image, and extracts the saturation component from the input image. An image processing apparatus comprising: a saturation correction amount calculating unit configured to calculate a saturation correction amount from a saturation component. 3. The image processing apparatus according to claim 1, wherein the saturation correction amount calculating unit includes: a region dividing unit that divides an input image into a plurality of regions; A saturation component extracting means for extracting a saturation component of the image data, and correcting a saturation of the entire image based on a saturation component of a region having a largest saturation component from the saturation components extracted for each of the plurality of regions. A color saturation correction amount calculating means for calculating a color saturation correction amount. 4. The image processing apparatus according to claim 1, wherein the saturation correction amount calculating unit includes: a region dividing unit that divides an input image into a plurality of regions; Contour component extracting means for extracting a contour component of image data; contour component averaging means for calculating an average value of the contour components extracted for each of the plurality of regions; image data of a region having the largest average value of the contour components And a saturation correction amount calculating means for calculating a saturation correction amount for correcting the saturation of the entire image from the extracted saturation components. Characteristic image processing device. 5. The image processing apparatus according to claim 1, wherein the saturation correction amount calculating unit includes: an area dividing unit that divides an input image into a plurality of areas; Skin color component extraction means for extracting a skin color component of image data; saturation component extraction means for extracting a saturation component of image data in an area where the skin color component is distributed most; and An image processing apparatus comprising: a saturation correction amount calculating unit configured to calculate a saturation correction amount for correcting saturation.