Color processing method

Abstract

To enable correction of an index representing a color using a second color matching function according to a color difference assessment result of a user.SOLUTION: A color processing method disclosed herein involves correcting an index representing a color using a second color matching function that uses a color matching function as an initial value and is updated by performing update processing according to an assessment result of a color difference judgment experiment performed by a user.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present disclosure relates to a color processing method. [Background technology]

[0002] Patent Document 1 describes a technique for correcting differences in color appearance due to individual differences in color matching functions. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2009-89364 A Summary of the Invention [Problem to be solved by the invention]

[0004] There was a difference between the color shades measured by a colorimeter using color matching functions defined by the International Commission on Illumination (CIE) and the color shades that people actually perceive. In particular, the colorimeter's measurements for lightness tended to be far removed from the degree of brightness that people actually perceive. For this reason, the colorimeter's measurements could not be used directly in the appearance inspection of paint colors, and visual inspection by humans was required. Therefore, there was a need for technology that could reduce the time required for visual inspection by humans. [Means for solving the problem]

[0005] The present disclosure can be realized in the following forms.

[0006] (1) According to one embodiment of the present disclosure, there is provided a color processing method, which corrects an index representing a color by using a second color matching function that uses a color matching function as an initial value and that is updated by executing an update process according to an evaluation result of a color difference judgment experiment by a user. According to the above aspect, since the index representing the color can be corrected using the second color matching function determined according to the user's evaluation result of the color difference, it is more likely that visual inspection will not be necessary.

[0007] (2) In the color processing method of the above embodiment, (A) preparing result data of a color difference judgment experiment in which a user evaluates the color difference between a reference color and a sample color obtained by changing at least one of the lightness, hue, and saturation of the reference color, using a pair of the reference color and the sample color; (B) preparing measurement data by measuring the spectral reflectance of each of the reference color and the sample color using a measuring instrument; (C) calculating tristimulus values ​​and a first value for the reference color using the spectral reflectance of the reference color and the second color matching function; (D) calculating the tristimulus values ​​and second values ​​for the sample colors using the spectral reflectances of the sample colors and the second color matching functions; (E) calculating ΔL*, which is a difference between an index L* representing the lightness of the reference color and the index L* of the sample color, using the tristimulus values ​​of the reference color, the tristimulus values ​​of the sample color, the first value, and the second value; (F) calculating Δa*, which is a difference between an index a* representing the saturation and hue of the reference color and the index a* of the sample color, by using the tristimulus values ​​of the reference color and the tristimulus values ​​of the sample color; (G) calculating Δb*, which is a difference between an index b* representing the saturation and hue of the reference color and the index b* of the sample color, using the tristimulus values ​​of the reference color and the tristimulus values ​​of the sample color; (H) calculating a correlation coefficient between the color difference represented by the calculated ΔL*, Δa*, and Δb* and the color difference evaluated by the user represented by the result data; (I) changing the second color matching function according to a correlation between the color difference represented by the ΔL*, the Δa*, and the Δb* and the color difference evaluated by the user represented by the result data; (J) repeating steps (C) to (I) until the correlation coefficient satisfies a predetermined reference condition; (K) calibrating the measuring instrument using a formula for calculating the index L*, the index a*, and the index b* that represent the color including the finally changed second color matching function, and parameters of the formula; It may further include. According to the above embodiment, the second color matching function is adjusted until the correlation coefficient between the color difference based on the measurement value by the measuring instrument and the color difference evaluated by the user satisfies the reference condition, so that the measuring instrument can be calibrated according to the color difference actually perceived by a person. [Brief description of the drawings]

[0008] [Figure 1] 1 is a block diagram showing a schematic configuration of a color processing system according to an embodiment. [Diagram 2] 1 is a flowchart showing the flow of processing executed by the color processing system. [Diagram 3] FIG. 11 is a diagram showing an example of evaluation result data stored in a memory. [Figure 4] FIG. 4 is a diagram showing an example of measurement data stored in a memory. [Diagram 5] FIG. 13 is a diagram showing data including parameters that define color matching functions and parameters that define second color matching functions. [Figure 6] FIG. 11 is an explanatory diagram of a second color matching function updating method. [Figure 7] 4 is a diagram showing the spectral reflectance curves of a sample color and a reference color; FIG. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] A. Embodiment: 1 is a block diagram showing a schematic configuration of a color processing system 10 according to an embodiment. The color processing system 10 includes a spectrophotometer 50 and a processing device 100. The color processing system 10 outputs an equation for calculating an index L* that represents a lightness corresponding to a color difference actually perceived by a human.

[0010] The spectrophotometer 50 has a sensor for measuring the color of an object and color matching function data, and has a function for measuring the spectral reflectance of the object and calculating an index representing the color and a color difference. The spectrophotometer 50 supplies the measurement value to the processing device 100 via wireless communication or wired communication. The spectrophotometer 50 is also called a measuring instrument.

[0011] The processing device 100 is a computer including a memory 110 , an interface unit 120 , an input device 130 , a display device 140 , and a CPU 150 .

[0012] The memory 110 stores various programs and data used for various processes executed by the processing device 100. The memory 110 stores evaluation result data D1, measurement data D2, and data D3, which will be described later. The interface unit 120 is connected to the spectrophotometer 50, the input device 130, and the display device 140. The input device 130 is, for example, a keyboard and a mouse. The display device 140 is, for example, a liquid crystal display and an organic EL (Electro Luminescence) display. The CPU 150 executes the programs stored in the memory 110 to realize various functions.

[0013] Fig. 2 is a flowchart showing the flow of processing executed by color processing system 10. The processing shown in Fig. 2 is executed by CPU 150 of processing device 100. For example, when a user instructs the start of processing via input device 130, CPU 150 starts the processing shown in Fig. 2.

[0014] In step S100, the CPU 150 prepares evaluation result data of a color difference judgment experiment. First, the CPU 150 displays an image on the display device 140 to instruct a user to perform a color difference judgment experiment. In this embodiment, the user is an inspector who performs a color difference judgment experiment to evaluate color differences. The user is an inspector who is highly skilled in evaluating color differences.

[0015] In the color difference judgment experiment, the color difference of the paint color of the body of the car is evaluated. The user evaluates the difference in appearance of the paint color of the body of the car. In this color difference judgment experiment, a plate painted with a reference color and a plate painted with a sample color are used. The material of both plates is the same as the material of the body of the car. The sample color is a color that is slightly different in hue from the reference color. More specifically, the sample color is a color in which at least one of the lightness, hue, and saturation of the reference color is changed. For example, 20 pairs of reference colors and sample colors are used. Of the 20 pairs, all the reference colors do not need to be different colors. The same color may be set as the reference color among the 20 pairs. For example, when the same color is set as the reference color in two pairs, the respective sample colors are set to different colors. Standard illuminant D65 defined by the International Commission on Illumination (CIE) is used as the light source.

[0016] The user evaluates the difference in brightness, redness, and yellowness of the sample color against the reference color. Specifically, the user evaluates the difference in brightness by determining whether the sample color is "brighter," "slightly brighter," "no difference," "slightly darker," or "darker" than the reference color. The user evaluates the target sample color with one of the following values, for example: "2" representing "bright," ""1" representing "slightly brighter," ""0" representing "no difference," ""-1" representing "slightly darker," or ""-2" representing "dark."

[0017] The user evaluates the difference in redness by determining whether the sample color is "redder," "slightly redder," "no difference," "slightly less reddish," or "no reddish" compared to the reference color. The user evaluates the target sample color with one of the following values, for example: "2" representing "redder," "1" representing "slightly reddish," "0" representing "no difference," "-1" representing "slightly less reddish," or "-2" representing "no reddish."

[0018] The user evaluates the difference in yellowness by determining whether the sample color is "yellower", "slightly yellower", "no difference", "slightly less yellowish", or "no yellowish" compared to the reference color. The user evaluates the target sample color with one of the following values, for example: "2" representing "yellower", "1" representing "slightly yellowish", "0" representing "no difference", "-1" representing "slightly less yellowish", or "-2" representing "no yellowish".

[0019] The user evaluates the differences in brightness, redness, and yellowness, that is, color difference, for each of the 20 pairs. The user inputs the evaluation result via the input device 130. The CPU 150 stores the evaluation result data D1 representing the input evaluation result in the memory 110. In this manner, the evaluation result data D1 is prepared. The same user evaluates the differences in brightness, redness, and yellowness for each of the 20 pairs two or more times. For example, the user evaluates the differences in brightness, redness, and yellowness for each of the 20 pairs in the morning. Furthermore, the user evaluates the differences in brightness, redness, and yellowness for each of the same 20 pairs in the afternoon. In this case, the average value of the morning evaluation value and the afternoon evaluation value for the same sample color becomes the final evaluation value.

[0020] FIG. 3 is a diagram showing an example of evaluation result data D1 stored in memory 110. Evaluation result data D1 is also called result data. Sample represents an identification number that identifies each of 20 pairs of reference color and sample color. Each pair is assigned one of sample identification numbers T1, T2, . . . , T20. The evaluation values ​​for brightness, redness, and yellowness are the average values ​​of the evaluation values ​​by users.

[0021] In step S101 shown in FIG. 2, CPU 150 prepares measurement data of the measured values ​​obtained by measuring the spectral reflectance of each of 20 pairs of reference colors and sample colors for each wavelength. The measurement range is from 360 nm to 740 nm in wavelength, and the measurement pitch is 10 nm. CPU 150 displays an image on display device 140 to instruct the user to perform measurement. The user uses spectrophotometer 50 to measure the spectral reflectance of the reference color and the spectral reflectance of the sample color for each of the 20 pairs. The user inputs the measured values ​​of the spectral reflectance of the reference color and the spectral reflectance of the sample color for each of the 20 pairs via input device 130. CPU 150 stores measurement data D2 representing the input measurement values ​​in memory 110. In this manner, measurement data D2 is prepared.

[0022] 4 is a diagram showing an example of the measurement data D2 stored in the memory 110. The measurement data D2 includes the measured values ​​of the spectral reflectance of the reference color and the spectral reflectance of the sample color for each of the 20 pairs in the set wavelength range.

[0023] In step S102 shown in FIG. 2, the CPU 150 uses the measurement values ​​obtained by the spectrophotometer 50 to obtain an index representing each of the reference color and the sample color for each of the 20 pairs.

[0024] First, CPU 150 calculates tristimulus values ​​X, Y, Z for the reference colors and Y', which is a first value for the reference colors, using the spectral reflectance of the reference colors and the second color matching functions. X, Y, Z, and Y' for the reference colors are calculated by formulas (M1) to (M4). Here, the calculated Y' becomes the first value. x_bar(λ), y_bar(λ), z_bar(λ), and y'_bar(λ) are the second color matching functions.

[0025]

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[0029] 5 is a diagram showing data D3 including parameters that define the second color matching function. In this specification, the 10-degree observer color matching function adopted by CIE in 1964 is used as the initial values ​​of x_bar(λ), y_bar(λ), and z_bar(λ).<URL:https: / / cie.co.at / datatable / cie-1964-colour-matching-functions-10-degree-observer> ) where y'_bar(λ) is a function newly introduced in this embodiment. When the first value is calculated for the first time, all values ​​of y'_bar(λ) are set to the initial value "0". Data D3 is stored in memory 110 in advance.

[0030] In formulas (M1) to (M4), Km is the maximum luminous effect. The value of Km is 683 [lm / W]. P(λ) represents the spectral distribution. The value defined for each wavelength in the appendix of JIS Z8720:2012 is used as the value of the spectral distribution P(λ). The light source used for the measurements is standard illuminant D65. The measured value shown in Figure 4 is used as ρ(λ).

[0031] The CPU 150 stores in the memory 110 the calculated X, Y, Z, and Y′ for each of the 20 pairs of reference colors.

[0032] Next, CPU 150 calculates X, Y, Z, and Y' for the sample colors using the spectral reflectances of the sample colors and the second color matching functions according to formulas (M1) to (M4). Here, the calculated Y' becomes the second value. CPU 150 stores the calculated X, Y, Z, and Y' for each of the 20 pairs of sample colors in memory 110.

[0033] Next, the indices a*, b*, and L* are calculated using X, Y, Z, and Y' calculated for the reference color and the sample color, respectively.

[0034] The CPU 150 calculates the index a* of the reference color and the index a* of the sample color. The index a* is an index that represents the saturation and hue of a color in the L*a*b* color space adopted by the CIE in 1976.

[0035] The index a* is calculated using the following formula (M5): The following formula (M5) is the same as the formula for calculating the index a* defined by the CIE. a*=500[f(X / X n )-f(Y / Y n )]···(M5)

[0036] To calculate the index a* of the reference color, X, and Y calculated for the reference color in the manner described above are used. To calculate the index a* of the sample color, X, and Y calculated for the sample color in the manner described above are used.n ,Y n , and Z, which will be described later. n are the tristimulus values ​​of a perfect diffuse reflecting surface. The function f(X / X n ), f(Y / Y n ), and f(Z / Z n ) are as follows: f(X / X n )=(X / X n ) 1 / 3 (X / X n >(24 / 116) 3 When f(Y / Y n )=(Y / Y n ) 1 / 3 (Y / Y n >(24 / 116) 3 When f(Z / Z n )=(Z / Z n ) 1 / 3 (Z / Z n >(24 / 116) 3 When In other cases, the function f(X / X n ), f(X / X n ), f(X / X n ) are as follows: f(X / X n )=(841 / 108)(X / X n )+16 / 116(X / X n <=(24 / 116) 3 When f(Y / Y n )=(841 / 108)(Y / Y n )+16 / 116(Y / Y n <=(24 / 116) 3 When f(Z / Z n )=(841 / 108)(Z / Z n )+16 / 116(Z / Z n <=(24 / 116) 3 When

[0037] The spectral reflectance ρ(λ) is set to 100% for the entire wavelength range from 360 nm to 740 nm. The spectral distribution P(λ) is set to the values ​​defined for each wavelength in the appendix of JIS Z8720:2012.

[0038] Furthermore, the CPU 150 calculates Δa*, which is the difference between the exponent a* of the reference color and the exponent a* of the sample color. The CPU 150 calculates Δa* for each of the 20 pairs, and stores the calculated Δa* in the memory 110.

[0039] The CPU 150 calculates the index b* of the reference color and the index b* of the sample color. The index b* is an index that represents the saturation and hue of a color in the L*a*b* color space.

[0040] The index b* is calculated using the following formula (M6). The following formula (M6) is the same as the formula for calculating the index b* defined by CIE. To calculate the index b* of the reference color, Y, and Z calculated for the reference color by the method described above are used. To calculate the index b* of the sample color, Y, and Z calculated for the sample color by the method described above are used. b*=200[f(Y / Y n )-f(Z / Z n )]···(M6)

[0041] Furthermore, the CPU 150 calculates Δb*, which is the difference between the exponent b* of the reference color and the exponent b* of the sample color. The CPU 150 calculates Δb* for each of the 20 pairs, and stores the calculated Δb* in the memory 110.

[0042] Next, the CPU 150 calculates the index L* of the reference color and the index L* of the sample color. The index L* is an index that represents lightness.

[0043] The formula for calculating the index L* defined by the CIE is as follows: L*=116f(Y / Y n )-16····(M7)

[0044] However, in this embodiment, the exponent L* is calculated using the following equation (M8) instead of equation (M7). To calculate the exponent L* of the reference color, Y and Y' calculated for the reference color by the method described above are used. To calculate the exponent L* of the sample color, X, Y, Z, and Y' calculated for the sample color by the method described above are used. However, if all values ​​of y'_bar(λ) are set to "0", equation (M8) does not hold, and so the second term β·f(Y' / Y') in equation (M8) is n ) shall be set to "0". L*=α f(Y / Y n )-β f(Y' / Y' n )-γ (M8)

[0045] α, β, and γ are coefficients for adjusting formula (M8) according to the results of a color difference judgment experiment. For example, the initial values ​​of α and β are set to "116", and the initial value of γ is set to "16".

[0046] Also, Y' n is calculated using formula (M4). The value of the spectral reflectance ρ(λ) is set to 100% for the entire wavelength range from 360 nm to 740 nm. The value of the spectral distribution P(λ) is defined for each wavelength in the appendix of JIS Z8720:2012.

[0047] Furthermore, the CPU 150 calculates ΔL*, which is the difference between the index L* of the reference color and the index L* of the sample color. The CPU 150 calculates ΔL* for each of the 20 pairs, and stores the calculated ΔL* in the memory 110.

[0048] In step S103 shown in FIG. 2, the CPU 150 uses the indexes of the reference color and the sample color obtained in step S102 to obtain the color difference between the reference color and the sample color for each of the 20 pairs.

[0049] In step S104, the CPU 150 calculates a correlation coefficient between the color differences represented by ΔL*, Δa*, and Δb* and the color differences evaluated by the user represented by the evaluation result data D1 (see FIG. 3).

[0050] Specifically, the CPU 150 first uses 20 pairs of ΔL* and the evaluation result of the difference in brightness between the reference color and the sample color in the corresponding user's color difference judgment experiment (see FIG. 3) to calculate a correlation coefficient RsL* between ΔL* and the result of the color difference judgment experiment for brightness. For example, the correlation Spearman's rank correlation coefficient is calculated as the correlation coefficient RsL*, Rsa* and Rsb* described later.

[0051] Next, CPU 150 uses Δa* for the 20 pairs and the evaluation result values ​​for the difference in redness between the reference color and the sample color in the corresponding user's color difference judgment experiment (see Figure 3) to calculate the correlation coefficient Rsa* between Δa* and the results of the color difference judgment experiment for redness.

[0052] Next, CPU 150 uses Δb* of the 20 pairs and the evaluation result values ​​(see FIG. 3) of the difference in yellowness between the reference color and the sample color in the corresponding user's color difference judgment experiment to calculate the correlation coefficient Rsb* between Δb* and the results of the color difference judgment experiment for yellowness.

[0053] In step S105 shown in FIG. 2, CPU 150 judges whether each correlation coefficient satisfies a reference condition. In the embodiment, when each correlation coefficient is equal to or greater than a predetermined threshold, it is assumed that the reference condition is satisfied. The thresholds for the correlation coefficients RsL*, Rsa*, and Rsb* may not be the same. CPU 150 judges whether each correlation coefficient satisfies a predetermined criterion. If at least one of the correlation coefficients is less than the threshold, that is, if the reference condition is not satisfied (step S105; NO), CPU 150 executes the process of step S106. On the other hand, if the reference condition is satisfied (step S105; YES), CPU 150 stores the updated data D3 and α, β, and γ of formula (M8) in memory 110, and executes the process of step S107.

[0054] In step S106, CPU 150 updates a parameter (see FIG. 5) that defines one of the second color matching functions according to the correlation between the color difference represented by ΔL*, Δa*, and Δb* and the color difference evaluated by the user represented by the evaluation result data. At this time, it is not necessary to update the values ​​of x_bar(λ), y_bar(λ), z_bar(λ), and y'_bar(λ) in the entire range from 360 nm to 740 nm. For example, at least one value of x_bar(λ), y_bar(λ), z_bar(λ), and y'_bar(λ) is updated at a specific wavelength.

[0055] FIG. 6 is an explanatory diagram of a method for updating y'_bar(λ). For example, a correlation diagram is generated with ΔL* on the vertical axis and the user's evaluation value of the difference in brightness (see FIG. 3) on the horizontal axis. In the correlation diagram shown in FIG. 6, ΔL* and the evaluation value of the difference in brightness are plotted for each sample color. Here, on the assumption that there is a positive correlation between ΔL* and the user's evaluation value of the difference in brightness, outliers are extracted from the correlation diagram. The outliers are detected using a well-known algorithm for outlier detection. For example, a plot point for sample T5 is selected as an outlier.

[0056] For sample T5, the user's evaluation value of the difference in brightness indicates that there is almost no difference in the difference in brightness, whereas ΔL* for the outlier indicates that the difference in brightness is large.

[0057] FIG. 7 is a diagram showing the spectral reflectance curves of the sample color and the reference color for sample T5. The solid line shows the spectral reflectance curve of the reference color. The dashed line shows the spectral reflectance curve of the sample color. For outliers, the spectral reflectance curve of the sample color and the spectral reflectance curve of the reference color corresponding to that sample color are used to identify wavelength bands where there is a difference between the two spectral reflectance curves. A difference means that the difference in spectral reflectance is equal to or greater than a predetermined threshold. The value of y'_bar(λ) in the identified wavelength band is changed.

[0058] For example, suppose that the range of 530 nm to 580 nm is identified as a wavelength band with a difference. In order to reduce the difference in this wavelength band, the value of y'_bar(λ) in the range of 530 nm to 580 nm (see FIG. 5) is changed. For example, the value of y'_bar(λ) in the range of 530 nm to 580 nm is changed to "1." The changed value is determined according to the magnitude of the difference between the two spectral reflectance curves.

[0059] When the value of y'_bar(λ) is the initial value "0", in the formula for calculating L* shown in formula (M8), f(Y' / Y' n ) is "0." When the value of y'_bar(λ) is changed to "1," the value of L* can be reduced in the formula for finding L* shown in formula (M8).

[0060] Also, suppose that the range of 460 nm to 520 nm is identified as a wavelength band with a difference. In order to reduce the difference in this wavelength band, the value of y'_bar(λ) in the range of 460 nm to 520 nm (see FIG. 5) is changed. For example, the value of y'_bar(λ) in the range of 460 nm to 520 nm is changed to "0.1". The changed value is determined according to the magnitude of the difference between the two spectral reflectance curves.

[0061] Also, suppose that the range of 640nm to 670nm is identified as a wavelength band with a difference. In order to reduce the difference in this wavelength band, the value of y'_bar(λ) in the range of 640nm to 670nm (see FIG. 5) is changed. In the range of 640nm to 670nm, the spectral reflectance of the sample color is lower than the spectral reflectance of the reference color. Therefore, for example, the value of y'_bar(λ) in the range of 640nm to 670nm is changed to "-0.1".

[0062] Further, in the correlation diagram shown in FIG. 6, other outliers are extracted and the same process as described above is performed. For example, the same process as described above is performed for sample T1. Note that an upper limit on the number of outliers to be extracted may be determined each time step S106 is executed. For example, the upper limit is "2".

[0063] Also, y_bar(λ) can be changed using the correlation diagram for ΔL* shown in FIG.

[0064] For Δa*, a correlation diagram is generated with Δa* on the vertical axis and the user's evaluation value of the difference in redness (see FIG. 3) on the horizontal axis. This correlation diagram can be used to change x_bar(λ) or y_bar(λ). For Δb*, a correlation diagram is generated with Δb* on the vertical axis and the user's evaluation value of the difference in yellowness (see FIG. 3) on the horizontal axis. This correlation diagram can be used to change z_bar(λ) or y_bar(λ).

[0065] 2, after the process of step S106, CPU 150 executes the processes from step S102 onwards again. If at least one of x_bar(λ), y_bar(λ), z_bar(λ), and y'_bar(λ) is changed in step S106, in the second or subsequent steps of step S102, the indices L*, a*, and b* representing the colors are calculated using these changed second color matching functions.

[0066] The processes from step S102 to step S106 are repeated until the correlation coefficients RsL*, Rsa*, and Rsb* satisfy the reference condition.

[0067] If it is determined in step S105 that the reference condition is satisfied, step S107 is executed. In step S107, CPU 150 updates the equation for calculating the index representing the color using updated data D3 and α, β, and γ in equation (M8). Furthermore, CPU 150 updates spectrophotometer 50 using updated data D3 and α, β, and γ in equation (M8). Then, CPU 150 ends the process shown in FIG. 2.

[0068] As described above, in the color processing method according to the embodiment, the second color matching function using the color matching function as an initial value is updated by executing an update process according to the evaluation result of the color difference judgment experiment by the user. Furthermore, the second color matching function is adjusted based on the measurement value by the spectrophotometer 50 until the correlation coefficient between the color difference calculated by the processing device 100 and the color difference evaluated by the user becomes equal to or greater than a threshold value. This makes it possible to obtain a formula for calculating the indexes L*, a*, and b* according to the color difference actually perceived by a person. Therefore, it is possible to obtain the indexes L*, a*, and b* corrected according to the color difference actually perceived by a person using the second color matching function according to the evaluation result of the color difference by the user.

[0069] Furthermore, the spectrophotometer 50 is calibrated using the updated x_bar(λ), y_bar(λ), z_bar(λ), and y'_bar(λ), the calculation formula for the index representing the color, and the parameters of the calculation formula. Therefore, the spectrophotometer 50 can be calibrated according to the color difference that a person actually perceives. This increases the possibility that visual inspection will not be necessary.

[0070] B. Other embodiments: B1. Alternative embodiment 1: In a color difference judgment experiment by a user, in addition to the difference between redness and yellowness, the difference between blueness and greenness may also be evaluated. In this case, the user evaluates the target sample color with one of the values, for example, the value "2" representing "has a blueness", the value "1" representing "has a slight blueness", the value "0" representing "no difference", the value "-1" representing "has a slight lack of blueness", and the value "-2" representing "has no blueness". The evaluated value is added to the evaluation value of yellowness in the evaluation item for yellowness (see FIG. 3). In addition, the user evaluates the target sample color with one of the values, for example, the value "2" representing "has a greenness", the value "1" representing "has a slight greenness", the value "0" representing "no difference", the value "-1" representing "has a slight lack of greenness", and the value "-2" representing "has no greenness". The evaluated value is added to the evaluation value of redness in the evaluation item for redness (see FIG. 3).

[0071] B2. Alternative embodiment 2: In the embodiment, an example has been described in which the Spearman rank correlation coefficient is calculated as the correlation coefficient, but the Pearson product moment correlation coefficient or the Kendall rank correlation coefficient may be used as the correlation coefficient. Also, in the embodiment, an example has been described in which the tristimulus values ​​and Y' are calculated in the wavelength range of 360 nm to 740 nm as shown in the formulas (M1) to (M4), but these can be changed according to the measurable wavelength range of the spectrophotometer 50. For example, when the measurable wavelength range of the spectrophotometer 50 is 380 nm to 780 nm, the interval of integration in the formulas (M1) to (M4) can be set to 380 nm to 780 nm.

[0072] B3. Alternative embodiment 3: Also, in step S106 shown in FIG. 2, the value of y'_bar(λ) in a predetermined wavelength range may be changed from the initial value. As the predetermined wavelength range, for example, a certain range around 560 nm where the sensitivity of each of the L cones, M cones, and S cones of the photoreceptor cells is at its peak, a certain range around 530 nm, and a certain range around 430 nm may be selected. As the changed value of y'_bar(λ), for example, any value between 0 and 1 may be selected. Also, in step S106, the values ​​of α, β, and γ in formula (M8) may be changed.

[0073] The present disclosure is not limited to the above-mentioned embodiment, and can be realized in various configurations without departing from the spirit of the present disclosure. For example, the technical features in the embodiments corresponding to the technical features in each aspect described in the Summary of the Invention column can be appropriately replaced or combined to solve some or all of the above-mentioned problems or to achieve some or all of the above-mentioned effects. Furthermore, if the technical feature is not described as essential in this specification, it can be appropriately deleted. [Explanation of symbols]

[0074] D1...evaluation result data, D2...measurement data, D3...data, 10...color processing system, 50...spectrophotometer, 100...processing device, 110...memory, 120...interface unit, 130...input device, 140...display device, 150...CPU

Claims

1. 1. A color processing method comprising: a second color matching function using the color matching function as an initial value, the second color matching function being updated by executing an update process according to an evaluation result of a color difference judgment experiment by a user, and correcting an index representing a color using the second color matching function; Color processing method.

2. 2. The color processing method according to claim 1, (A) preparing result data of a color difference determination experiment in which a user evaluates the color difference between a reference color and a sample color obtained by changing at least one of the lightness, hue, and saturation of the reference color, using a pair of the reference color and the sample color; (B) preparing measurement data by measuring the spectral reflectance of each of the reference color and the sample color using a measuring instrument; (C) calculating tristimulus values ​​and a first value for the reference color using the spectral reflectance of the reference color and the second color matching function; (D) calculating the tristimulus values ​​and second values ​​for the sample colors using the spectral reflectances of the sample colors and the second color matching functions; (E) calculating ΔL*, which is a difference between an index L* representing the lightness of the reference color and the index L* of the sample color, using the tristimulus values ​​of the reference color, the tristimulus values ​​of the sample color, the first value, and the second value; (F) calculating a difference Δa* between an index a* representing the saturation and hue of the reference color and the index a* of the sample color using the tristimulus values ​​of the reference color and the tristimulus values ​​of the sample color; (G) calculating Δb*, which is a difference between an index b* representing the saturation and hue of the reference color and the index b* of the sample color, by using the tristimulus values ​​of the reference color and the tristimulus values ​​of the sample color; (H) calculating a correlation coefficient between the color difference represented by the calculated ΔL*, Δa*, and Δb*, and the color difference evaluated by the user represented by the result data; (I) changing the second color matching functions according to a correlation between the color difference represented by the ΔL*, the Δa*, and the Δb*, and the color difference evaluated by the user represented by the result data; (J) repeating steps (C) to (I) until the correlation coefficient satisfies a predetermined reference condition; (K) calibrating the measuring device using a formula for calculating the index L*, the index a*, and the index b* that represent colors including the finally changed second color matching functions, and parameters of the formula; Further comprising: Color processing method.

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