JP2009130630A - Color processor and method thereof - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To adjust the color tone of an image to be adjusted without sense of incongruity when performing color balance adjustment when making the color tone of the image to be adjusted close to the color tone of a reference image. <P>SOLUTION: A reference image to be reference of color tone and an image of a color tone adjustment target are input (S301), feature quantity of the reference image and feature quantity of the image of a color tone adjustment target are calculated, and the saturation of the reference image and the saturation of the image of a color tone adjustment target are calculated (S302, S304). A color tone adjustment condition on the image of a color tone adjustment target is set so as to make the color tone of the image of a color tone adjustment target close to the color tone of the reference image on the basis of the feature quantity and saturation of the reference image and the image of a color tone adjustment target (S305 to S308). Then, the set color tone adjustment condition is used to perform color adjustment of the image of a color tone adjustment target (S309). In such a case, if the saturation of the image of a color tone adjustment target is lower than the saturation of the reference image, the color tone adjustment condition is adjusted so as to reduce an adjustment amount of color adjustment. <P>COPYRIGHT: (C)2009,JPO&INPIT

Description

  The present invention relates to image color adjustment.

  It is common to perform color balance adjustment on an image taken by an image generation device such as a digital still camera. Various techniques for more appropriately expressing the colors of individual images by color balance adjustment processing have been proposed.

  Patent Document 1 sets a plurality of achromatic object color ranges (hereinafter referred to as achromatic object color ranges) in advance, investigates in which achromatic object color range the pixels of the processing target image are distributed, A technique for performing balance adjustment is disclosed.

  Patent Document 2 discloses a technique for performing color balance adjustment while paying attention to a human face. That is, one density correction curve is generated from the basic density correction coefficient obtained from the entire image and the face-dependent density correction coefficient obtained from the average density value of the face image with emphasis on the face image area.

  The above technique corrects the color balance of each image to reduce the color cast generated in the image, and correctly reproduces the image pickup target that is originally white in white.

  Further, a technique has been proposed in which a target image is set and an image whose color is to be matched is matched with the color tone of the target image.

  Patent Document 3 classifies image shooting scenes into a plurality of categories such as landscapes and portraits, and determines the most ideal color distribution in each category. Then, a technique for color correcting each image with an ideal color distribution of the category as a target is disclosed. Patent Document 4 discloses color balance adjustment that performs color matching of a specified area, not the entire image.

  However, none of the above techniques is suitable for color balance adjustment in which, for example, an album-like image in which a plurality of images are laid out on one page, all images have the same atmosphere.

  The techniques disclosed in Patent Documents 1 and 2 are basically techniques for reducing color cast. For example, a wedding album is desired to be finished in an album with a warm color, so an image with a color cast from a little red to yellow is preferred. However, if you remove the color cast from each individual image, the entire wedding album will have a cool impression. Therefore, the color balance is adjusted by adjusting the color balance of each image, and then the color of the entire image is adjusted. However, similar processing is repeated twice, and the work efficiency is poor.

  In addition, when applying the technique disclosed in Patent Document 3 to an image on an album page, if different target images such as portrait and landscape images are prepared for different scenes, the color of the image on the same page The difference between the two may make the viewer feel uncomfortable. Also, if a target image is set for each page, not for each scene, there is a risk that the quality of the image itself will be greatly impaired even if the atmosphere of the image is unified.

  For example, when laying out a photo A of an object illuminated with a light bulb indoors (yellow) and a photo B of an object located outdoors or near a window or illuminated with a white fluorescent lamp on the same page think of. In this case, if the color balance adjustment is performed using the image of the photo A as the target image, the leaves of the plant shown in the photo B become too yellow and wither the impression that the human face becomes yellow.

  Also, consider a case where the technique disclosed in Patent Document 4 is applied to images taken at various places under various light sources such as a wedding album. In this case, for example, if a specific area such as a face or a dress is designated and color balance adjustment is performed, the color of the entire image becomes unnatural. Alternatively, there arises a problem that an area that can be regarded as the same color cannot be set in all the target images for color balance adjustment.

JP 2005-318489 JP 2007-018073 JP JP 2003-134341 A JP-A-8-297731

  An object of the present invention is to adjust the color tone of a color adjustment target image without a sense of incongruity when performing color adjustment that brings the color tone of an image to be color adjusted close to the color tone of a reference image.

  The present invention has the following configuration as one means for achieving the above object.

  The color processing apparatus according to the present invention is a color processing apparatus that corrects the color tone of the processing target image to match the color tone of the reference image, and is used to input a reference image that is a reference for the color tone and an image that is a color adjustment target. Means for calculating the feature amount of the reference image and the feature amount of the color adjustment target image, calculation means for calculating the saturation of the reference image and the saturation of the color adjustment target image, Based on the feature amount and saturation of the reference image and the color adjustment target image, color adjustment conditions for the color adjustment target image are set so that the color tone of the color adjustment target image approaches the color tone of the reference image. A setting unit; and an adjusting unit configured to perform color adjustment on the color adjustment target image using the set color adjustment condition, wherein the setting unit determines the color adjustment target based on a saturation of the reference image. When image saturation is low So as to reduce the adjustment amount of the color adjustment, and adjusting the color adjustment conditions.

  A color processing method according to the present invention is a color processing method for correcting a color tone of a processing target image so as to match a color tone of a reference image, and a step of inputting a reference image serving as a color tone reference and an image to be color adjusted Calculating a feature amount of the reference image and a feature amount of the color adjustment target image; calculating a saturation of the reference image and a saturation of the color adjustment target image; and A setting step for setting color adjustment conditions for the color adjustment target image so that the color tone of the color adjustment target image approaches the color tone of the reference image based on the feature amount and saturation of the color adjustment target image; Adjusting the color adjustment target image using the set color adjustment condition, and the setting step includes a saturation of the color adjustment target image rather than a saturation of the reference image. If low, to reduce the adjustment amount of the color adjustment, and adjusting the color adjustment conditions.

  According to the present invention, when performing color adjustment that brings the color tone of the color adjustment target image closer to the color tone of the reference image, the color tone of the color adjustment target image can be adjusted without a sense of incongruity.

  Hereinafter, color processing according to an embodiment of the present invention will be described in detail with reference to the drawings.

  In the first embodiment, an example in which the color tones of a plurality of images laid out in one page are matched will be described.

[Device configuration]
FIG. 1 is a block diagram illustrating a configuration example of an image processing apparatus (color processing apparatus) according to an embodiment.

  The microprocessor (CPU) 104 executes various programs stored in the read only memory (ROM) 105 and the storage unit 103 using the random access memory (RAM) 106 as a work memory, and will be described later via the system bus 109. Control the configuration. And various processes including the process mentioned later are performed.

  The input / output unit 101 includes a serial bus interface such as USB or IEEE1394 for connecting an input device such as a keyboard and mouse, a memory card reader / writer, and an image input / output device such as a digital camera or printer. In addition, a network interface card (NIC) for connecting to the network is provided.

  The CPU 104 inputs user instructions from the input device via the input / output unit 101, and inputs various data such as image data from a memory card, an image input device, and a server on the network. Also, the image data of the color processing result is output to an image output device or output to a memory card or a server via the input / output unit 101.

  The display unit 102 is a liquid crystal panel, for example. The CPU 104 displays a graphic user interface (GUI) or the like on the display unit 102. The storage unit 103 is, for example, a hard disk drive (HDD), and stores various programs such as an operating system (OS) and processing programs described later, and various data such as image data.

[Example of album]
FIG. 2 is a diagram showing an example of an album in which a plurality of photographic images are laid out on one page.

The shooting location of each photographic image is as follows.
Photo images 201 and 202: Inside the reception hall
Photo images 203 and 204: Inside the ceremony hall
Photo 205: Outdoor,
Photo 206: Studio

  From the relationship of the shooting location, it is assumed that the photographic image 202 is the most yellowish and the photographic image 203 has the next strongest color cast. Even in the case of photographic images taken at the same reception hall, the color cast degree of the photographic image 201 is the third strongest due to lighting, and the following is the order of the photographic images 204, 205, 206 in order of the strong color cast degree. To do. Note that the photographic image 205 taken outdoors and the photographic image 206 taken by adjusting the light in the studio have almost no color cast.

  In the following, an example of adjusting the color balance of these photographic images will be described. All photographic images are RGB digital data of 8 bits.

[Color balance adjustment]
FIG. 3 is a flowchart for explaining the color balance adjustment method, which is a process executed by the CPU 104.

The CPU 104 inputs a plurality of images (n images) to be laid out on one page according to a user instruction (S301). At that time, the CPU 104 inputs an image to be used as a reference for the color balance specified by the user as the reference image Is, and inputs other images as I [0] to I [n-2]. Here, a case where the photographic image 201 shown in FIG. 2 is designated as the reference image Is will be described. The relationship between the photographic images other than the photographic image 201 and the arrangement is as follows.
I [0] = Photo 202,
I [1] = Picture image 203,
I [2] = Picture 204
I [3] = Picture image 205,
I [4] = Picture 206

  If there are two images to be laid out on one page, there will be one image whose color is to be matched to one reference image.

Next, the CPU 104 calculates an average RGB value (Rs, Gs, Bs) that is an average value of each color component of the reference image Is according to the equation (1), and calculates an average from the average RGB value according to the equations (2) and (2 ′). The saturation Ss is calculated (S302).
Rs = ΣR / N
Gs = ΣG / N (1)
Bs = ΣB / N
Where ΣR is the sum of R values of all pixels,
ΣG is the sum of G values of all pixels,
ΣB is the sum of B values of all pixels,
N is the number of pixels
Ss' = {MAX (Rs, Gs, Bs) -MIN (Rs, Gs, Bs)} / MAX (Rs, Gs, Bs)… (2)
Where MAX () is the function to find the maximum value,
MIN () is a function to find the minimum value

Ss ′ calculated by Equation (2) has a value between 0 and 1. Therefore, Ss ′ is multiplied by 255 and converted to an average saturation Ss having a value of 0 to 255.
Ss = 255 × Ss '… (2')

For example, it is assumed that the average RGB value and the average saturation Ss of the reference image Is are as follows.
Rs = 189, Gs = 149, Bs = 88,
Ss = 136

  Next, the CPU 104 initializes a counter k for counting images for which color balance adjustment is completed to zero (S303), and averages RGB values (Rk, Gk, Bk) of the image I [k] (adjustment target image). ) And average saturation Sk are calculated (S304). Note that the formulas for calculating the average RGB values (Rk, Gk, Bk) and the average saturation Sk are the same as the formulas (1), (2), and (2 ′).

  FIG. 4 is a diagram showing an example of the calculation result of the average RGB value and the average saturation S of the photographic images 201 to 206.

  Next, the CPU 104 determines the color balance adjustment process from the average RGB values (Rs, Gs, Bs) of the reference image Is and the average RGB values (Rk, Gk, Bk) of the image I [k], as will be described in detail later. Conditions (color adjustment conditions) are calculated. That is, basic color balance adjustment coefficients (hereinafter, basic correction coefficients) γro and γbo, which are color adjustment coefficients, are calculated (S305).

  Next, the CPU 104 calculates the absolute value Δs = | Ss−Sk | (hereinafter simply referred to as the difference) of the difference between the average saturation Ss of the reference image IS and the average saturation Sk of the image I [k]. The difference Δs is compared with a predetermined threshold Tx (S306). If the difference is larger than the threshold value (Δs> Tx), final color balance adjustment coefficients (hereinafter referred to as final correction coefficients) γr and γb adjusted to approach 1.0 according to the difference Δs are calculated (S307).

  When Δs> Tx, the color cast of the reference image Is is considerably stronger than the color cast of the image I [k], and the color balance of the image I [k] is adjusted according to the average saturation Ss of the reference image Is. In this case, the image often has a sense of incongruity. Therefore, as will be described in detail later, the degree of color balance adjustment (adjustment amount) is reduced by bringing the final correction coefficient γ closer to 1.0.

  If the difference is equal to or smaller than the threshold (Δs ≦ Tx), the CPU 104 sets the basic correction coefficients γro and γbo to the final correction coefficients γr and γb as they are (S308).

  Next, although details will be described later, the CPU 104 performs color balance adjustment (correction) on the image I [k] using the final correction coefficients γr and γb (S309). Then, the counter k is incremented (S310), the value of the counter k is compared with n-2 (S311), and if k <n-2, the process returns to step 304 and reaches k = n-2. Then, the process ends.

Correction of Adjustment Level Based on Saturation Difference FIG. 5 is a diagram for explaining a method of correcting the final correction coefficient γ and shows an example in which Tx = 40 and Tx ′ = 80.

A region 501 where Δs ≦ Tx is set to a final correction coefficient γ = γo (no correction). Further, the region 502 where Tx <Δs <Tx ′ is corrected so that the final correction coefficient γ approaches 1.0 in proportion to Δs according to the following equation. In the region 503 where Tx ′ ≦ Δs, the final correction coefficient γ = 1.0.
γ = (1-γo) Δs / (Tx '-Tx) + 2γo-1… (3)
Where Tx <Δs <Tx '

  FIG. 5 shows a correction line of the region 502 when γo <1.0, but there may be a case where γo> 1.0. Needless to say, when γo> 1.0, the correction line in the region 502 has a negative slope.

Calculation of Basic Correction Coefficient FIG. 6 is a flowchart for explaining the calculation of basic correction coefficient γo (S305).

The CPU 104 calculates the target value (Rf) of the average RGB value after color balance adjustment from the average RGB value (Rs, Gs, Bs) of the reference image Is and the average RGB value (Rk, Gk, Bk) of the adjusted image I [k]. , Gf, Bf) is calculated (S501). To give the same color cast to the image I [k] after color balance adjustment as in the reference image Is, the ratio of the G color component value and the R color component value, the G color component value, and the B color of the image I [k] The ratio of the component values may be almost the same as that of the reference image Is. Therefore, when the G color component value is fixed and the target values (Rf, Gf, Bf) are calculated, the result is as follows.
Rf = Rs / Gs × Gk
Gf = Gk (4)
Bf = Bs / Gs × Gk

Next, the CPU 104 calculates basic correction coefficients γro and γbo from the following equations using the average values Rk and Bk and target values Rf and Bf of the image I [k] (S502).
γro = log (Rk / 255) / log (Rf / 255)
γbo = log (Bk / 255) / log (Bf / 255)… (5)
Where 0 <Rk ≦ 255, 0 <Bk ≦ 255,
0 <Rf <255, 0 <Bf <255

  FIG. 7 is a diagram illustrating calculation results of the basic correction coefficient γo and the final correction coefficient γ of the image I [k]. The images I [0] and I [1] satisfy Δs ≦ Tx (= 40), and the basic correction coefficient γo and the final correction coefficient γ are equal. On the other hand, the images I [2] to I [4] satisfy Δs> Tx (= 40), and the final correction coefficient γ approaches 1.0 or is corrected to 1.0.

Color Balance Adjustment FIG. 8 is a flowchart for explaining the color balance adjustment of the image I [k].

  The CPU 104 compares the final correction coefficients γr and γb with 1.0 (S601), and if both are 1.0, the process ends without performing color balance correction.

  If either of the final correction coefficients γr and γb is other than 1.0, the CPU 104 uses the final correction coefficients γr and γb to generate a one-dimensional lookup table (LUT) that represents the correspondence between the R value and B value before and after adjustment. Create (S602). If the value after adjustment exceeds 255, clip to 255. Then, using the created LUT, the R value and the B value of all the pixels of the image I [k] are corrected (S603).

  FIG. 5 shows a method of correcting the final correction coefficient γ by setting the final correction coefficient γ to 1.0 (no color balance correction is performed) when the saturation difference Δs is larger than the threshold value Tx ′ (= 80). However, as shown in FIG. 9, the final correction coefficient γ can be smoothly approached to 1.0 from the threshold Tx (= 40) so that the final correction coefficient γ is 1.0 when the saturation difference Δs is around the maximum value 255. Good. FIG. 9 shows a correction line when γo <1.0, but there are also cases where γo> 1.0. Needless to say, when γo> 1.0, the correction line has a negative slope.

  When the difference Δs in saturation between the reference image and the adjustment target image is larger than the predetermined threshold Tx, it is considered that the color fog degree of the reference image Is is considerably stronger than the color fog degree of the image I [k]. Therefore, when Tx <Δs, correction is performed so that the correction coefficient γ for color balance adjustment approaches 1.0 (the degree of adjustment is weakened). As a result, when adjusting the color balance of multiple images laid out on a single page and giving them the same atmosphere, it is possible to prevent the problem that the face of a person included in an image with originally low saturation is overcast. it can.

  The color processing according to the second embodiment of the present invention will be described below. Note that the same reference numerals in the second embodiment denote the same parts as in the first embodiment, and a detailed description thereof will be omitted.

  In the first embodiment, the final correction coefficient γ is set using the difference in saturation, but in the second embodiment, the face detection technology is used to reduce the color in the case of a photographic image with low saturation and a face. A method of weakening the balance adjustment will be described.

  FIG. 10 is a flowchart for explaining the color balance adjustment method according to the second embodiment.

  The CPU 104 performs the same processing (S301 to S306) as in the first embodiment. If the saturation difference exceeds a predetermined threshold (Δs> Tx), face detection is performed on the image I [k] (S701), and whether or not the image I [k] includes a face image. Is determined (S702). Various methods for detecting the face have been proposed, and any method may be adopted.

  When the image I [k] includes a face image, the CPU 104 performs a process (S307) similar to that in the first embodiment and calculates a final correction coefficient γ. If the image I [k] does not include a face image, the same processing (S308) as in the first embodiment is performed to determine the final correction coefficient.

  Next, the CPU 104 performs the same processing (S309 to S311) as in the first embodiment.

  That is, in Example 2, when the saturation of the adjustment target image I [k] is lower than that of the reference image Is and the adjustment image I [k] includes a face image, the degree of adjustment of the color balance is weakened. For other images, color balance adjustment is performed in accordance with the reference image Is. Although the face detection process takes a little time, it is possible to perform color balance adjustment that places importance on the face image (does not give an uncomfortable feeling to the face color).

  Hereinafter, color processing according to the third embodiment of the present invention will be described. Note that the same reference numerals in the third embodiment denote the same parts as in the first and second embodiments, and a detailed description thereof will be omitted.

  In the second embodiment, an example has been described in which the basic correction coefficient γo is used as it is as the final correction coefficient γ when there is no face even if the saturation of the adjustment target image I [k] is lower than the reference image Is. However, with this method, when the image I [k] does not include a face image, for example, the problem that the leaves of the plant are too fogged still remains. In addition, there remains a problem that a side face or a crawling face that is difficult to detect a face is too colored. Thus, in the third embodiment, color balance adjustment that considers the color cast of an image that does not include a face image while giving importance to the face image will be described.

  FIG. 11 is a flowchart for explaining the color balance adjustment method according to the third embodiment.

  The CPU 104 performs the same processing (S301 to S306) as in the first embodiment. If the saturation difference exceeds a predetermined threshold (Δs> Tx), face detection is performed on the image I [k] (S701), and the image I [k] It is determined whether or not includes a face image (S702). Various methods for detecting the face have been proposed, and any method may be adopted.

  When the image I [k] includes a face image, the CPU 104 calculates the final correction coefficients γr and γb using the first function F1 (Δs, γo) that can easily calculate the correction coefficient γ close to 1.0 (S801). . In other words, a weak final correction coefficient γ is calculated.

  On the other hand, when the image I [k] does not include a face image, the CPU 104 uses the second function F2 (Δs, γo) that calculates a correction coefficient γ stronger than the function F1, and calculates the final correction coefficients γr and γb. Calculate (S802). In other words, a slightly weaker final correction coefficient γ is calculated.

  Next, the CPU 104 performs the same processing (S309 to S311) as in the first embodiment.

  Although not limited to FIGS. 5 and 9, for example, the function F1 is a function as shown in FIG. 5, and the function F2 is a function as shown in FIG. In other words, when the image I [k] includes a face image, the function F1 that easily calculates a weak correction coefficient γ close to 1.0 is used, and when the image I [k] does not include a face image, the correction coefficient γ close to the basic correction coefficient γo. The function F2 is used to easily calculate

  In the third embodiment, since two functions are selected according to the face detection result, the second embodiment is slightly more complicated than the second embodiment. However, even when a face is not detected, the final correction coefficient γ can be slightly weakened if the saturation of the image I [k] is smaller than the saturation of the reference image Is. As a result, it is possible to perform correction in consideration of an image included in each photographic image as compared with the second embodiment.

  The color processing according to the fourth embodiment of the present invention will be described below. Note that the same reference numerals in the fourth embodiment denote the same parts as in the first to third embodiments, and a detailed description thereof will be omitted.

  When shooting time information (for example, Exif data) is added to a photographic image, the information can be used. In the fourth embodiment, an example in which color balance adjustment is performed using a shooting scene type tag of Exif data will be described. The shooting scene type tag indicates the type of the subject at the time of shooting, and the shooting mode at the time of shooting may be recorded. The tag number is 41990, and a value of “2” indicates that the subject type is a person.

  FIG. 12 is a flowchart for explaining the color balance adjustment method according to the fourth embodiment.

  The CPU 104 performs the same processing (S301 to S306) as in the first embodiment. If the saturation difference exceeds a predetermined threshold (Δs> Tx), the Exif data of the image I [k] is acquired (S901), and the value of the tag number 41990 is “2” (the shooting scene type is It is determined whether or not a person is present (S902).

  When the shooting scene type is a person, the CPU 104 performs the same processing (S307) as that of the first embodiment and calculates the final correction coefficient γ. If the shooting scene type is other than person, the same processing (S308) as in the first embodiment is performed to determine the final correction coefficient. Alternatively, as in the third embodiment, when the shooting scene type is a person, the function F1 (Δs, γo) is used to calculate γr and γb (S801), and when the shooting scene type is other than a person, the function F2 (Δs , γo) may be used to calculate γr and γb (S802).

  Next, the CPU 104 performs the same processing (S309 to S311) as in the first embodiment.

  Exif 2.2 defines the shooting scene type as a recommended tag and is not always described in the data file of the photographic image. Also, there is no tag corresponding to the shooting scene type before Exif 2.2. However, using the Exif information added at the time of shooting can prevent the face image from being overcast without reducing the face detection process (reducing processing time and processing load). .

[Modification]
In the above embodiment, the basic color balance adjustment coefficient is obtained using the average RGB values (Rs, Gs, Bs) of the reference image Is and the average RGB values (Rk, Gk, Bk) of the adjusted image I [k]. However, the basic color balance adjustment coefficient may be obtained using the feature amount of another image. For example, a weighted average value weighted according to a color, an average RGB value of a highlight portion or a shadow portion, or the like may be used as the feature amount.

  Further, instead of using adjustment coefficients for the R component and the B component as in the above embodiment, other adjustment methods such as a matrix may be used.

  In the second and third embodiments, the degree to which the color balance adjustment is weakened (correction coefficient γ) is determined depending on whether or not the adjustment target image I [k] includes a face image, but the ratio of the area of the face image to the entire image ( The correction coefficient γ may be determined according to the occupation ratio. For example, when the occupation ratio is 15% or more, it can be determined that the photograph image is taken with emphasis on the face. Therefore, if the occupation ratio is 15% or more, the value obtained by weakening the basic correction coefficient γo is used as the final correction coefficient γ, and if it is less than 15%, the atmosphere is emphasized and the basic correction coefficient γo is used as it is as the final correction coefficient γ .

  In the fourth embodiment, an example in which the shooting scene type tag is used has been described. However, an exposure program tag may be used. The value “7” of the exposure program tag represents the portrait mode. Accordingly, in step S902, it is determined whether or not the value of the exposure program tag (tag number 34850) in the Exif IFD is “7” (portrait mode). If “7”, the process proceeds to step S307 (S801), and if other than “7”, the process proceeds to step S308 (S802).

  Of course, the final correction coefficient γ may be determined using both the shooting scene type tag and the exposure program tag. Further, as the additional information at the time of photographing, information unique to each company recorded in the maker note tag may be used.

  Alternatively, information indicating that a person is captured may be added to the image data at the time of editing or selecting after shooting, and the final correction coefficient γ may be determined using the additional information. Further, the function for calculating the final correction coefficient γ may be divided according to the case where information indicating that a person is photographed is obtained from the information added to the image data and the case where the information is not obtained.

[Other embodiments]
Note that the present invention can be applied to a system constituted by a plurality of devices (for example, a computer, an interface device, a reader, a printer, etc.), but an apparatus (for example, a copier, a facsimile machine, a control device) composed of a single device. Etc.).

  Another object of the present invention is to supply a storage medium storing a computer program for realizing the functions of the above-described embodiments to a system or apparatus, and the computer (CPU or MPU) of the system or apparatus executes the computer program. But it is achieved. In this case, the software read from the storage medium itself realizes the functions of the above embodiments, and the computer program and the computer-readable storage medium storing the computer program constitute the present invention. .

  Further, the above functions are not only realized by the execution of the computer program. That is, according to the instruction of the computer program, the operating system (OS) and / or the first, second, third,... This includes the case where the above function is realized.

  The computer program may be written in a memory of a device such as a function expansion card or unit connected to the computer. That is, it includes the case where the CPU of the first, second, third,... Device performs part or all of the actual processing according to the instructions of the computer program, thereby realizing the above functions.

  When the present invention is applied to the storage medium, the storage medium stores a computer program corresponding to or related to the flowchart described above.

FIG. 1 is a block diagram illustrating a configuration example of an image processing apparatus (color processing apparatus) according to a first embodiment; A diagram showing an example of an album in which a plurality of photo images are laid out on one page, A flowchart for explaining a color balance adjustment method; The figure which shows an example of the calculation result of the average RGB value and average saturation S of a photograph image, A diagram for explaining a method of correcting the final correction coefficient γ, A flowchart for explaining the calculation of the basic correction coefficient γo; The figure which shows the calculation result of basic correction coefficient γo and final correction coefficient γ of image I [k] A flowchart for explaining color balance adjustment of an image I [k], The figure explaining another method of correcting the final correction coefficient γ, Flowchart for explaining a color balance adjustment method in Embodiment 2, Flowchart for explaining a color balance adjustment method in Embodiment 3, 10 is a flowchart for explaining a color balance adjustment method in Embodiment 4.

Claims (11)

A color processing device that corrects the color tone of a processing target image to match the color tone of a reference image,
An input means for inputting a reference image as a reference for color tone, and an image to be color-adjusted;
Calculating means for calculating the feature amount of the reference image and the feature amount of the image to be color-adjusted;
Calculating means for calculating the saturation of the reference image and the saturation of the image to be color-adjusted;
Based on the feature amount and saturation of the reference image and the color adjustment target image, a color adjustment condition for the color adjustment target image is set so that the color tone of the color adjustment target image approaches the color tone of the reference image Setting means to
Adjusting means for adjusting the color of the image to be color-adjusted using the set color adjustment conditions;
The setting means adjusts the color adjustment condition so as to reduce the adjustment amount of the color adjustment when the saturation of the color adjustment target image is lower than the saturation of the reference image. Color processing device.   The setting means does not adjust the color adjustment condition when the absolute value of the difference between the saturation of the reference image and the saturation of the image to be color adjusted does not exceed a predetermined threshold. Item 2. A color processing apparatus according to Item 1.   3. The color according to claim 2, wherein when the absolute value of the difference exceeds the predetermined threshold, the setting unit reduces the adjustment amount of the color adjustment by adjusting the color adjustment condition. Processing equipment. Furthermore, it has a detection means for detecting a face image from the color adjustment target image,
When the absolute value of the difference exceeds the predetermined threshold and the face image is detected, the setting unit adjusts the color adjustment condition to reduce the adjustment amount of the color adjustment. The color processing apparatus according to claim 2. Furthermore, it has a detection means for detecting a face image from the color adjustment target image,
If the absolute value of the difference exceeds the predetermined threshold and the face image is detected, the setting means adjusts the color adjustment condition using a first function to reduce the adjustment amount of the color adjustment If the face image is not detected, the color adjustment condition is adjusted by a second function to reduce the adjustment amount of the color adjustment, and the first function is less than the second function. 3. The color processing apparatus according to claim 2, wherein the reduction of the color is large. Furthermore, it has acquisition means for acquiring shooting time information from the color adjustment target image,
When the absolute value of the difference exceeds the predetermined threshold value and the shooting time information represents shooting of a person, the setting means adjusts the color adjustment condition to reduce the adjustment amount of the color adjustment. 3. The color processing apparatus according to claim 2, wherein the color processing apparatus is characterized.   7. The setting unit according to claim 1, wherein the setting unit sets the color adjustment condition from an average value of each color component of the reference image and the color adjustment target image. Color processing device.   The setting means sets a color adjustment coefficient of the R color component as the color adjustment condition from a ratio of an average value of the R color component to an average value of the G color component of the reference image and the color adjustment target, and the reference image 8. The color adjustment coefficient of the B color component is set as the color adjustment condition from the ratio of the average value of the B color component to the average value of the G color component of the color adjustment target. Color processing device. A color processing method for correcting the color tone of a processing target image to match the color tone of a reference image,
A step of inputting a reference image to be a reference for color tone and an image to be color-adjusted;
Calculating a feature amount of the reference image and a feature amount of the color adjustment target image;
Calculating the saturation of the reference image and the saturation of the color adjustment target image;
Based on the feature amount and saturation of the reference image and the color adjustment target image, a color adjustment condition for the color adjustment target image is set so that the color tone of the color adjustment target image approaches the color tone of the reference image A setting step to
Adjusting the color adjustment target image using the set color adjustment conditions,
The setting step adjusts the color adjustment condition so as to reduce an adjustment amount of the color adjustment when the saturation of the image to be color adjusted is lower than the saturation of the reference image. Color processing method.   9. A computer program for controlling a computer device to function as each unit of the color processing device according to claim 1.   11. A computer-readable storage medium in which the computer program according to claim 10 is recorded.

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