JP2006173825A - Image processing apparatus and method thereof - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To perform color gamut mapping suitable for the visual characteristic of a human being by introducing color gamut mapping using a color appearance space suitable for the visual characteristic of the human being. <P>SOLUTION: An image processing apparatus has an XYZ calculating means for converting color values represented by a first color space into CIE tristimulus values XYZ, a color appearance conversion means for converting the XYZ values calculated by the XYZ calculating means into appearance space values by using a color appearance conversion formula, and a color conversion means for converting the appearance space values acquired by the color appearance conversion means into color values represented by a second color space. <P>COPYRIGHT: (C)2006,JPO&NCIPI

Description

  The present invention relates to an image processing apparatus and method for performing color matching.

In recent years, with the spread of personal computers, images are input using image input devices such as digital cameras and color scanners, and the images are displayed and confirmed using image display devices such as CRT and LCD. The image is output by the image output device. In this case, color matching processing (color gamut mapping) is usually performed in order to correct a difference in appearance due to a difference in color gamut between the input device and the display / output device. By performing this color matching process, the difference in color appearance between devices is absorbed. One method of color gamut mapping is a process commonly called Colorimetric. Colorimetric processing is a method in which the common area of the input device color gamut and the output device color gamut is expressed as it is, and the non-common area is mapped to the surface of the output device color gamut. Various techniques have been proposed as a mapping method. For example, it is determined whether or not an input color is within the color gamut of the target output device, and a color outside the color gamut is assigned to the latest in the output device color gamut. There have been proposed a method of mapping to a neighboring point, a method of mapping a color outside the color gamut to a color having the same lightness and hue and the highest saturation. However, in the former method, the hue of the color outside the color gamut may change, and the color of the output image may feel uncomfortable. In the latter method, brightness and hue are maintained, but depending on the compression area, the saturation is remarkably lowered, and this may also be felt as an unnatural image. In addition, both have a problem that gradation is completely ignored for colors outside the output device gamut. In order to solve such problems, for example, “IEPariser,“ An Investiation of Color Gamut Reduction Techniques ”, IS & T Symp. Elec. Prepress Tech. -Color Printing, pp. 105-107 (1991)” A method of mapping the color gamut toward the direction of one point (50,0,0) on the gray axis in space was proposed. In addition, a method has been proposed in which area division is performed for each hue in the CIELAB space and mapping is performed in an optimal compression direction for each area.
EGPariser, "An Investiation of Color Gamut Reduction Techniques", IS & T Symp. Elec. Prepress Tech.-Color Printing, pp.105-107 (1991)

  As described above, a large number of out-of-gamut mapping methods in the Colorimetric method have been proposed so far. In particular, the two methods described above preserve hues even for colors outside the color gamut, and also express gradations, albeit slightly, so that it is possible to obtain a good image with no sense of incongruity. However, when actually observing an image that has been color gamut compressed using the above-described method, it may appear as if the hue of the color having the same hue in the input image is bent. This is because both methods are based on the CIELAB space. CIELAB space is a device-independent color space devised to match human visual characteristics, but in reality it does not completely match human visual characteristics. For example, as illustrated in FIG. 1, the blue gradation felt by human vision does not have the same hue, that is, a straight line in the CIELAB space. Nevertheless, in the above method, as shown in FIG. 2, the hue is bent because the mapping is performed in the uniform hue on the CIELAB space.

In recent years, a color appearance model has been announced as a color appearance model that models human visual characteristics with higher accuracy than conventional CIELAB. The color value of the color appearance space (for example, J ^* a ^* b ^* value) can be calculated from the CIE tristimulus values XYZ. By using this J ^* a ^* b ^* , it is possible to perform color gamut mapping more suitable for human visual characteristics. The conversion to obtain the color value J ^* a ^* b ^* in the color appearance space is performed by, for example, chromatic adaptation conversion, cone response conversion, and opposite color response conversion, so that the value approaches human visual characteristics. Go. The conversion method of chromatic adaptation conversion is, for example, linear conversion represented by matrix conversion as shown in Expression (1), and cone response conversion is exponential operation as shown in Expression (2) in addition to matrix conversion. Also do. The inverse color response conversion is a conversion to make the value after the cone response conversion the value of the coordinates J ^* a ^* b ^* adapted to human visual characteristics, and linear conversion and trigonometric function conversion are typical. Conversion formula.

ただし、a₀₀, a₀₁, a₀₂, a₁₀, a₁₁, a₁₂, a₂₀, a₂₁, a₂₂,b,c,d,e,f,gは定数であり、Aは変換前の値、A'は変換後の値を表す。

However, a ₀₀ , a ₀₁ , a ₀₂ , a ₁₀ , a ₁₁ , a ₁₂ , a ₂₀ , a ₂₁ , a ₂₂ , b, c, d, e, f, g are constants, and A is before conversion The value A ′ represents the converted value.

  In this color appearance space, when a blue gradation in human vision is displayed as in FIG. 1, it is as shown in FIG. That is, in order to improve the above-described phenomenon in which the hue appears to be bent, it is necessary to perform mapping to the equal hue in this color appearance space.

  The present invention has been made in order to solve the above-described problems, and is intended to preserve hues and gradations outside the color gamut when mapping the input color gamut to the output device color gamut, The purpose is to perform color gamut mapping that matches human visual characteristics by incorporating color gamut mapping using a color appearance space that is suitable for the visual characteristics of humans.

  To achieve the above object, the present invention provides an XYZ calculating means for converting a color value expressed by a first color space into a CIE tristimulus value XYZ in an image processing apparatus that performs color matching for matching the colors of an image. A color appearance conversion means for converting the XYZ values calculated by the XYZ calculation means into appearance space values using a color appearance conversion formula, and the appearance space value obtained by the color appearance conversion means is expressed by a second color space. And a color conversion means for converting the color value into a converted color value.

  According to the present invention, when mapping an input color gamut to an output device color gamut using Colorimetric processing, weighting is applied to the color outside the output device color gamut by weighting the brightness and preserving the hue. However, a good hue and gradation can be provided. Furthermore, by using a color appearance space that is more suitable for human visual characteristics than the conventional CIELAB space for the device non-color space used for mapping, it is possible to improve the phenomenon in which hues appear to be bent as before. .

(First embodiment)
FIG. 4 is a block diagram showing a hardware configuration according to the first embodiment of the present application.
In FIG. 4, reference numeral 1 denotes an image processing apparatus.

Further, 101 is an input means for inputting image data, 102 is an XYZ calculation means for converting the RGB value of the image input in the input means 101 into CIE tristimulus values XYZ values, and 103 is in the XYZ calculation means 102 Color appearance conversion means for calculating a J ^* a ^* b ^* value from the calculated XYZ values using a color appearance conversion formula, 104 is an output means for outputting an image converted to a color space value of an output device, 105 is A gamut inside / outside determination means for determining whether or not the J ^* a ^* b ^* value obtained in the color appearance conversion means 103 exists in the color gamut of the output device held in the gamut holding means 108; 106 is a color gamut mapping means for mapping J ^* a ^* b ^* calculated by the color appearance conversion means 103 to the color gamut of the output device held in the color gamut holding means 108, and 107 is a mapping Later color apia Color space value calculating means for calculating the color space value of the output device from the sensed value; 108, a color gamut holding means for holding the color gamut of the output device; and 109, a buffer memory for temporarily storing calculation results during processing .

<Operation in the image processing apparatus 1>
FIG. 5 is a flowchart of exemplary processing executed by the image processing apparatus 1.

  In step S1, image data is input by the data input means 101.

  In step S2, the XYZ calculation means 102 converts the RGB value of the image input in step 1 into a CIE tristimulus value XYZ value. The conversion formula is specific to the color space representing the image. In the present embodiment, the following processing will be described assuming that the color space representing an image is an sRGB color space which is an international standard color space. Formula (3) shows the conversion formula of sRGB color space to XYZ.

式(3)に示した変換式を用いて入力画像の各画素のRGB値をXYZ値に変換する。得られたXYZ値はバッファメモリ109に保存する。

The RGB value of each pixel of the input image is converted into an XYZ value using the conversion equation shown in Equation (3). The obtained XYZ values are stored in the buffer memory 109.

In step S3, the color appearance conversion means 103 converts it into a J ^* a ^* b ^* value using a color appearance conversion formula based on the XYZ value calculated by the XYZ calculation means 102 in step S2. Specific processing of the color appearance conversion means 103 will be described later.

In step S4, the color gamut inside / outside determination means 105 acquires the J ^* a ^* b ^* value of each pixel of the input image obtained in step S3, and the color of each pixel is held in the color gamut holding means 108. It is determined whether or not it exists in the color gamut of the output device. Hereinafter, the color gamut inside / outside determination method will be described.

<Color gamut inside / outside judgment method>
The output device color gamut of this embodiment is held in the color gamut holding means 108. The format of the output device color gamut held in the color gamut holding means 108 is shown in FIG. As shown in Figure 6, RGB sliced data (729 colors) in the range of 0≤R, G, B≤255 and data describing the output J ^* a ^* b ^* value of the corresponding output device are stored. Has been. This J ^* a ^* b ^* value is calculated from data obtained by patching the 9-slice RGB data in advance, outputting the data from the output device, and measuring the color using a colorimeter or the like. That is, the color gamut of the output device in the present embodiment is composed of 729 points of data obtained by slicing RGB into nine slices, that is, 512 hexahedrons. That is, when performing the color gamut inside / outside determination, it is only necessary to determine whether the color appearance values J, a, and b of the input pixel are in any of the 512 hexahedrons. Therefore, it is determined whether the hexahedrons are included one by one. In this embodiment, the hexahedron is further divided into six tetrahedrons (see FIG. 7), and inside / outside determination processing is performed for each tetrahedron to determine whether or not the hexahedron is in the hexahedron.

  Hereinafter, tetrahedral inside / outside determination will be described with reference to FIG.

  As shown in Fig.8, if each point of the tetrahedron is A, B, C, D and the input data is P,

と表すことができる。この時、点Pが四面体ABCDの中であれば、以下の式(5)、式(6)が成り立つ。

It can be expressed as. At this time, if the point P is in the tetrahedron ABCD, the following equations (5) and (6) hold.

式(5)、式(6)が成り立てば、四面体の中、成り立たなければ四面体の外と判定する。

If equations (5) and (6) hold, it is determined that the tetrahedron is inside, and if not, it is determined that the tetrahedron is outside.

  By performing the above tetrahedron inside / outside judgment on six tetrahedrons, hexahedron inside / outside judgment can be performed, and furthermore, by applying this hexahedron inside / outside judgment to 512 hexahedrons, the output device color gamut The inside / outside judgment is completed.

  In this manner, in step S4, the inside / outside determination of the output device color gamut is performed in units of pixels of the input image, and the result is stored in the buffer memory 109.

In step S5, the color gamut mapping means maps the J ^* a ^* b ^* value of each pixel of the input image to the output device color gamut. Specific processing of the color gamut mapping means 106 will be described later.

In step S6, the color space value calculation unit 107 converts the J ^* a ^* b ^* value mapped in step S5 into the color space value of the output device. Specific processing of the color space value calculating unit 107 will be described later.

  In step S7, the output unit 104 outputs the image converted into the color space value of the output device stored in the buffer memory 109.

<Operation of color appearance conversion means 103>
The operation of the color appearance conversion means 103 in step S3 will be described in detail with reference to FIG.

  FIG. 9 shows the flow of processing of the color appearance conversion means 103 in step S3.

  In step S31, the color appearance conversion unit 103 acquires XYZ lattice point data from the buffer memory 109.

In step S32, the color appearance conversion means 103 converts the XYZ values acquired in step S31 into J ^* a ^* b ^* values that are color values in the color appearance space.

In step S33, the color appearance conversion unit 103 stores the J ^* a ^* b ^* value calculated in step S32 in the buffer memory 109.

  In step S34, the color appearance conversion unit 103 determines whether or not color appearance conversion has been performed on all pixels of the input image held in the buffer memory 109. If all the pixels have been converted, the process ends. If not, the process returns to step S31.

<Operation of color gamut mapping means 106>
FIG. 10 shows an example of a specific processing flow of the color gamut mapping means 106 in step S5.

In step S51, the color gamut mapping unit 106 receives, from the buffer memory 109, the J ^* a ^* b ^* data J _i , a _i , b _i of each pixel of the input image and the determination result in the color gamut inside / outside determination unit 105. get.

  In step S52, the color gamut mapping unit 106 acquires the color gamut of the output device from the color gamut holding unit 108.

In step S53, the color gamut mapping means 106 refers to the color gamut inside / outside determination result obtained in step S51. If it is determined that it is within the color gamut, J _i , a _i , and b _i are stored in the buffer memory 109 as mapping destinations, and the process proceeds to step S55. If it is determined that it is out of the color gamut, the process proceeds to step S54.

In step S54, the color gamut mapping means 106 performs mapping processing to the output device color gamut for the inputs J _i , a _i , and b _i determined to be out of the output device color gamut. For the mapping process, for example, a method as shown in FIG. 11 is used. As shown in Fig. 11, the point (J ^* a ^* b ^* ) = (J _t , 0, 0) on the gray axis of the color appearance space for the input J _i , a _i , b _i outside the output device gamut Define. Then, as shown in FIG. 11, mapping is performed on the intersection of the line segment connecting the points J _i , a _i , b _i and the points J _t , 0, 0 and the surface of the output device color gamut. Here, J _t is a value determined depending on the lightness of the input point, that is, J _i , and is calculated using, for example, the following equation (7).

式(7)によれば、該グレー軸上の点J_t,0,0は入力点J_i,a_i,b_iの明度J_i ( 0≦J_i≦100 )によって、kから100-kの間を移動する。kが大きい場合、J_t,0,0はグレー軸上の広い範囲を動くようになり、一方、kが小さい場合は、J_t,0,0はグレー軸上の狭い範囲を動くことになる。このように定数kを用いるのは、明度に重みを持たせたマッピングを行うことが目的であり、これにより出力デバイス色域外の色の明度と彩度のトレードオフの解決を図ることが出来、さらに、良好な階調性を得ることが出来る。なお、定数kは自由に設定することができ、用途によって使い分けることが出来る。

According to equation (7), the point J _t , 0,0 on the gray axis is calculated from k to 100-k depending on the lightness J _i (0 ≦ J _i ≦ 100) of the input points J _i , a _i , b _i. Move between. When k is large, J _t , 0,0 will move over a wide range on the gray axis, whereas when k is small, J _t , 0,0 will move over a narrow range on the gray axis. . The purpose of using the constant k in this way is to perform weighted mapping, which can solve the trade-off between brightness and saturation of colors outside the output device color gamut, Furthermore, good gradation can be obtained. The constant k can be set freely and can be used properly depending on the application.

Next, as shown in FIG. 11, a line segment connecting the points J _i , a _i , b _i and the points J _t , 0, 0 is defined, and the intersection points J _j , a _j , b Calculate _j . The method of intersection calculation is shown below.

<Intersection calculation method>
In the intersection calculation, first, the surface data of the output device color gamut is acquired from the color gamut holding means 108. The surface data is data in which any of RGB has 0 or 255. In addition, as shown in FIG. 12, the surface data includes a surface with R of 255 (hereinafter referred to as R255 surface), a surface with G of 255 (hereinafter referred to as G255 surface), a surface with B of 255 (hereinafter referred to as B255 surface), R Is a surface with 0 (hereinafter referred to as R0 surface), a surface with G as 0 (hereinafter referred to as G0 surface), and a surface with B as 0 (hereinafter referred to as B0 surface). Each surface is composed of 81 lattice points, that is, 128 triangular patches, and data obtained by converting these triangular patches into J ^* a ^* b ^* values (held in the color gamut holding means 108). Acquire for each surface.

  Next, intersection points with straight lines are calculated for 128 triangles on each surface. The intersection can be easily obtained from the plane equation of each triangle and the equation of a straight line. Then, it is determined whether or not the intersection is within the triangle. This inside / outside determination method is a two-dimensional version of the above-described tetrahedron inside / outside determination method. That is, as shown in FIG. 13, if each point of the triangular patch is A, B, C and the intersection is P,

と表すことができる。この時、点Pが三角形ABCの中であれば、以下の式(9)、式(10)が成り立つ。

It can be expressed as. At this time, if the point P is in the triangle ABC, the following expressions (9) and (10) are established.

このようにして各表面の三角形パッチ128個に対し、内外判定を行う。交点が三角形の中に入っている点が出力デバイス色域と直線の交点J_j,a_j,b_jとなる。

In this way, the inside / outside determination is performed on 128 triangular patches on each surface. The point where the intersection point is in the triangle is the intersection point J _j , a _j , b _j between the output device color gamut and the straight line.

In step S54, the intersections J _j , a _j , b _j obtained as described above are stored in the buffer memory 109 as mapping destinations.

  In step S55, the color gamut mapping unit 106 determines whether or not the mapping of all the pixels has been completed. If all pixels have been mapped, the process is terminated. Otherwise, the process returns to step S62.

<Operation of Color Space Value Calculation Means 107>
FIG. 14 shows an example of a specific processing flow of the color space value calculation means 107 in step S6.

In step S61, the color space value calculating unit 107 acquires, from the buffer memory 109, the J ^* a ^* b ^* value of one pixel of the input image mapped to the output device color gamut by the color gamut mapping unit 106.

  In step S 62, the color space value calculation unit 107 acquires the output device color gamut from the color gamut holding unit 108.

In step S63, the color space value calculation unit 107 searches for tetrahedrons including the post-mapping data J _m , a _m , and b _m acquired in step S61 among the tetrahedrons constituting the output device color gamut. Since the tetrahedron search method to be included is the same as the tetrahedron in-vivo determination described above, the description thereof is omitted.

In step S64, the color space value calculating unit 107 calculates the color space value of the output device corresponding to the post-mapping data J _m , a _m , and b _m by a complementary operation. An example of the color space value calculation method will be described with reference to FIG. As described above, the points in the tetrahedron can be expressed as in Expression (4) and satisfy Expressions (5) and (6). Therefore, the color space value of the corresponding output device can be obtained by using the following equation (11) using s, t, u in equation (4).

ただし、R_A,G_A,B_Aは点Aに対応する出力デバイス色空間値、R_B,G_B,B_Bは点Bに対応する出力デバイス色空間値、R_C,G_C,B_Cは点Cに対応する出力デバイス色空間値、R_D,G_D,B_Dは点Dに対応する出力デバイス色空間値であり、それぞれステップS62において取得している。

However, R _A , G _A , B _A are output device color space values corresponding to point A, R _B , G _B , _BB are output device color space values corresponding to point B, R _C , G _C , B _C Is an output device color space value corresponding to the point C, and R _D , G _D , and B _D are output device color space values corresponding to the point D, and are obtained in step S62.

Output device color space values corresponding to the post-mapping data J _m , a _m , and b _m obtained as described above are stored in the buffer memory 109.

  In step S65, the color gamut mapping unit 106 determines whether or not the processing has been completed for all the pixels of the input image. If all pixels have been processed, the process ends. If all the pixels have not been processed, the process returns to step S61.

  According to the technology described above, when mapping an input image to an output device color gamut using Colorimetric processing, the mapping is performed by assigning a weight to the brightness and preserving the hue. A good hue and gradation can be imparted to the other colors. Furthermore, by using a color appearance space that is more suitable for human visual characteristics than the conventional CIELAB space for the device-independent color space used for mapping, it is possible to improve the phenomenon in which hues appear to be bent as before. I can do it.

(Other embodiments)
In the above embodiment, the expression color space of the input image is the sRGB color space which is an international standard color space. However, the present invention is not particularly limited to the sRGB color space, and in any color space. It may be expressed.

  In the above embodiment, the image is read by the data input means 101. However, the input data may be not only an image but also simple RGB data. This is used, for example, when creating a color conversion LookUpTable using the technique described in the present embodiment.

In the above embodiment, the color gamut of the output device is held in advance in the color gamut holding means 108, but it goes without saying that the input means 101 may read it together with the image data. In this case, the data to be read may not be the color appearance value J ^* a ^* b ^* , but may be the CIE tristimulus value XYZ value. In that case, the color appearance value J ^* a ^* b ^* of the output device color gamut is converted by the color appearance conversion means 102.

In the above embodiment, the output device color gamut is the color appearance value J ^* a ^* b ^{* of} 9 slices of RGB. However, the color gamut of the output device is not limited to 9 slices. Any number of slices is acceptable. For example, the number of slices may be reduced to 5 or 7 in order to reduce the amount of calculation, or it may be increased to 12 or 17 in order to increase the accuracy.

  Further, in the color gamut mapping means in the above-described embodiment, at the time of color gamut mapping, a point where the lightness of the mapping source is weighted on the gray axis of the color appearance space is defined using Equation (7). Although a straight line used for mapping is obtained, the present invention is not limited to the equation (7), and a weight may be given according to lightness, saturation, and hue. Also, the point to be defined is not limited to the gray axis, and may be anywhere within the color gamut of the output device.

  Further, in the present invention, a color appearance space in which human visual characteristics are modeled with high accuracy is used. Examples of the color appearance space in the present invention include color appearances such as CIECAM97s and CIECAM02 recommended by CIE, for example. There is space.

  In addition, the present invention can be applied to a system composed of a plurality of devices (for example, a host computer, interface device, reader, printer, etc.), or a device composed of a single device (for example, a copier, a facsimile device, etc.). You may apply to.

  Another object of the present invention is to supply a storage medium storing software program codes for realizing the functions of the above-described embodiments to a system or apparatus, and the computer (or CPU or MPU) of the system or apparatus stores the storage medium. Needless to say, this can also be achieved by reading and executing the program code stored in.

  In this case, the program code itself read from the storage medium realizes the functions of the above-described embodiments, and the storage medium storing the program code constitutes the present invention.

  As a storage medium for supplying the program code, for example, a floppy (registered trademark) disk, hard disk, optical disk, magneto-optical disk, CD-ROM, CD-R, magnetic tape, nonvolatile memory card, ROM or the like is used. I can do it.

  In addition, by executing the program code read by the computer, not only the functions of the above-described embodiments are realized, but also an OS (operating system) running on the computer based on the instruction of the program code. It goes without saying that a part of the actual processing is performed and the functions of the above-described embodiments are realized by the processing.

  Further, after the program code read from the storage medium is written to a memory provided in a function expansion board inserted into the computer or a function expansion unit connected to the computer, the function expansion is performed based on the instruction of the program code. It goes without saying that the CPU or the like provided in the board or the function expansion unit performs part or all of the actual processing, and the functions of the above-described embodiments are realized by the processing.

The figure which shows the example which projected the blue gradation in human vision on the a * b * plane of CIELAB space. The figure which shows the example which mapped blue gradation in human vision to the output device color gamut in CIELAB space. The figure which shows the example which projected the blue gradation in human vision on the a * b * plane of the color appearance space. The block diagram of the Example of this application. 3 is a diagram illustrating an exemplary process flow in the image processing apparatus 1. FIG. The figure which shows the example of the color gamut data of an output device. The figure which shows the example which divides | segments a hexahedron into six tetrahedrons. The figure which shows the example of the relationship between a tetrahedron and input data. The figure which shows the flow of the exemplary process in the color appearance conversion means 103. FIG. 4 is a diagram showing an exemplary process flow in a color gamut mapping unit 106. 3 is a conceptual diagram of color gamut mapping in a color gamut mapping unit 106. FIG. The figure which showed the surface data of the output device color gamut in RGB space. The figure which showed the example of the relationship between a triangle patch and an intersection. The figure which shows the flow of the exemplary process in the color space value calculation means 107.

Claims (13)

In an image processing apparatus that performs color matching to match the color of an image,
XYZ calculating means for converting the color value represented by the first color space into CIE tristimulus values XYZ;
Color appearance conversion means for converting the XYZ values calculated by the XYZ calculation means into appearance space values using a color appearance conversion formula;
An image processing apparatus comprising: color conversion means for converting an appearance space value obtained by the color appearance conversion means into a color value expressed by a second color space.   The color conversion means determines whether the color expressed by the first color space is within the color gamut of the second color space, and determines whether the color is out of the color gamut by the color gamut inside / outside determination means. A color gamut mapping means for mapping to the color gamut of the second color space, and a color space value calculation means for converting the mapped color appearance space value to the color value of the second color space. 2. The image processing apparatus according to claim 1, wherein:   The color gamut mapping means maps to a cross point between a straight line connecting a color expressed by the first color space and a predetermined color in the second color space and a color gamut surface of the second color space. The image processing apparatus according to claim 2.   4. The image processing apparatus according to claim 1, wherein a color appearance conversion formula in the color appearance conversion means is a CIECAM02 conversion formula.   4. The image processing apparatus according to claim 1, wherein the color appearance conversion formula in the color appearance conversion means is a CIECAM97s conversion formula.   6. The image processing apparatus according to claim 1, wherein the color gamut of the second color space is held in a color gamut holding unit. In an image processing method that performs color matching to match the color of an image,
An XYZ calculation step of converting color values expressed by the first color space into CIE tristimulus values XYZ;
A color appearance conversion step of converting the XYZ values calculated in the XYZ calculation step into appearance space values using a color appearance conversion formula;
A color conversion step of converting the appearance space value obtained by the color appearance conversion step into a color value expressed by the second color space.   The color conversion step determines whether the color expressed by the first color space is within the color gamut of the second color space, and determines whether the color is out of the color gamut by the color gamut inside / outside determination step. A color gamut mapping step for mapping to the color gamut of the second color space, and a color space value calculation step for converting the color appearance space value after mapping into the color value of the second color space. The image processing method according to claim 7.   The color gamut mapping step maps to a point of intersection between a straight line connecting a color represented by the first color space and a predetermined color in the second color space and a color gamut surface of the second color space. The image processing method according to claim 8.   The image processing method according to claim 7, wherein the color appearance conversion formula in the color appearance conversion step is a CIECAM02 conversion formula.   The image processing method according to any one of claims 7 to 9, wherein the color appearance conversion formula in the color appearance conversion step is a CIECAM97s conversion formula.   12. The image processing method according to claim 7, wherein the color gamut of the second color space is held in a color gamut holding step. In a recording medium on which an image processing program is recorded, in image processing for performing color matching for matching the color of an image,
XYZ calculation function that converts the color value expressed by the first color space into CIE tristimulus values XYZ;
A color appearance conversion function for converting the XYZ values calculated by the XYZ calculation function into appearance space values using a color appearance conversion formula;
A recording medium for recording a program for realizing a color conversion function for converting an appearance space value obtained by the color appearance conversion function into a color value expressed by a second color space.

Images