Color evaluation system, color evaluation method, pigment production method, and color evaluation program

Abstract

A color evaluation system includes an acquisition unit that acquires reference color information related to a reference color, a subject model that indicates recognition of the color by a subject, and a reference model that indicates recognition of the color by a reference color observer; a calculation unit that calculates a subject response indicating recognition of the reference color by the subject based on the reference color information and the subject model, and calculates a reference response indicating recognition of the reference color by the reference color observer based on the reference color information and the reference model; and a comparison unit that calculates a response difference as a difference between the subject response and the reference response.

Description

Technical Field

[0001] One aspect of the present invention relates to a color evaluation system, a color evaluation method, a method for manufacturing pigments, and a color evaluation procedure. Background Technology

[0002] Information processing related to color vision is known from the past. For example, the color processing apparatus described in Patent Document 1 acquires the color value that is the object of color processing, calculates a color vision degree coefficient representing the degree of color vision impairment based on the population distribution of the examination results obtained by conducting color vision examinations on a pre-set group, calculates a color conversion coefficient for converting the acquired color value based on the correspondence between the color vision degree coefficient and the sensitivity characteristics of L-cone, M-cone and S-cone, and performs color conversion processing on the color value using the color conversion coefficient.

[0003] Previous technical documents Patent documents Patent Document 1: Japanese Patent No. 5924289 Summary of the Invention

[0004] The technical problem to be solved by the invention The goal is to develop a mechanism for quantitatively assessing color from the perspective of human color vision.

[0005] means for solving technical problems A color assessment system according to one aspect of the present invention comprises: an acquisition unit that acquires reference color information related to a reference color, a subject model representing a subject's recognition of a color, and a reference model representing a reference colorimeter's recognition of a color; a calculation unit that calculates a subject response representing the subject's recognition of the reference color based on the reference color information and the subject model, and calculates a reference response representing a reference colorimeter's recognition of the reference color based on the reference color information and the reference model; and a comparison unit that calculates the difference between the subject response and the reference response as a response difference.

[0006] One aspect of the present invention relates to a color evaluation method executed by a color evaluation system having at least one processor. The color evaluation method includes the following steps: acquiring reference color information related to a reference color, a subject model representing a subject's recognition of the color, and a reference model representing a reference colorimeter's recognition of the color; calculating a subject response representing the subject's recognition of the reference color based on the reference color information and the subject model, and calculating a reference response representing the reference colorimeter's recognition of the reference color based on the reference color information and the reference model; and calculating the difference between the subject response and the reference response as a response difference.

[0007] The color evaluation procedure of one aspect of the present invention enables a computer to perform the following steps: acquiring reference color information related to a reference color, a subject model representing the subject's recognition of the color, and a reference model representing the reference colorist's recognition of the color; calculating a subject response representing the subject's recognition of the reference color based on the reference color information and the subject model, and calculating a reference response representing the reference colorist's recognition of the reference color based on the reference color information and the reference model; and calculating the difference between the subject response and the reference response as the response difference.

[0008] In this approach, the subject's recognition of a reference color is calculated as the subject's response, and the reference colorimeter's recognition of the reference color is calculated as the reference response. The difference between these two responses is then calculated as the response difference. This response difference quantitatively represents the deviation in the subject's recognition of the reference color from that of the reference color. Therefore, by using this response difference, color (the reference color) can be quantitatively evaluated from a color vision perspective.

[0009] Invention Effects According to one aspect of the present invention, color can be quantitatively evaluated from the perspective of human color vision. Attached Figure Description

[0010] Figure 1 This is a diagram representing the functional structure of a color evaluation system.

[0011] Figure 2 This is a flowchart illustrating an example of how a color evaluation system operates.

[0012] Figure 3 This is a chart showing examples of the sensitivity characteristics of L-cones, M-cones, and S-cones.

[0013] Figure 4 This is a flowchart illustrating an example of how a color evaluation system operates. Detailed Implementation

[0014] Hereinafter, various examples of the present invention will be described in detail with reference to the accompanying drawings. In the description of the drawings, the same or equivalent elements are labeled with the same symbols and repeated descriptions are omitted.

[0015] [System Overview] The color evaluation system involved in this invention is a computer system for quantitatively evaluating colors from the perspective of human color vision. The color evaluation system can also be referred to as a computer system that evaluates colors considering color vision diversity. Color vision diversity refers to the different states of color perception or recognition among different groups of people. The color evaluation system can be used to facilitate shared perceptions of colors among groups with different color visions, or to promote mutual understanding related to color perception or recognition.

[0016] As for types of color vision, there are five types: C (Common: normal color vision), P (Protanope: red-blind), D (Deuteranope: green-blind), T (Tritanope: blue-blind), and A (Acromatic: total color blindness).

[0017] Type C is a group of photoreceptor cells containing three types of cones: L-cones, M-cones, and S-cones. L-cones are photoreceptor cells that respond with high sensitivity to long-wavelength light, such as red. M-cones are photoreceptor cells that respond with high sensitivity to medium-wavelength light, such as green. S-cones are photoreceptor cells that respond with high sensitivity to short-wavelength light, such as blue. In the case of Japanese people, 95% of men and 99% of women belong to Type C. Type C individuals are also known as normal color vision individuals.

[0018] On the other hand, people with P-type, D-type, T-type, and A-type color blindness are also referred to as colorblind individuals. P-type is composed of P-type intensity (Protanopia: red-blindness) lacking the L-cone and P-type weakness (Protanomaly: red-weakness) with a sensitivity deviation of the L-cone but similar sensitivity to the M-cone. D-type is composed of D-type intensity (Deuteranopia: green-blindness) lacking the M-cone and D-type weakness (Deuteranomaly: green-weakness) with a sensitivity deviation of the M-cone but similar sensitivity to the L-cone. T-type is the group lacking the S-cone. A-type is the group possessing only one type of cone or none at all. Among colorblind individuals, P-type and D-type are the majority, while T-type and A-type are extremely rare.

[0019] As an example of fostering shared understanding or mutual comprehension, a color assessment system can be used to evaluate the differences in color recognition between individuals with color vision deficiencies and those with normal color vision. Alternatively, a color assessment system can be used to assess whether a particular color causes a discrepancy in recognition between individuals with color vision deficiencies and those with normal color vision. Or, a color assessment system can be used to adjust the spectrum of a reference color, i.e., the reference spectrum, to provide colors that are equally recognized by both individuals with normal color vision and those with color vision deficiencies. A reference spectrum is an example of reference color information associated with a reference color. A color assessment system can be used to promote shared understanding or mutual comprehension of colors among individuals with color vision deficiencies (Type C, Type P, Type D, Type T, and Type A).

[0020] [System Structure] A color evaluation system consists of one or more computers. When using multiple computers, these computers are connected via communication networks such as the Internet or intranets to form a logically unified color evaluation system.

[0021] A computer constituting a color evaluation system typically includes a processor, memory, communication interface, input devices, and output devices. Examples of processors include CPUs and GPUs. Memory can consist of flash memory, hard disks, etc. The communication interface can consist of a network card or a wireless communication module. Examples of input devices include keyboards, pointing devices, touch panels, microphones, sensors, and cameras. Examples of output devices include displays, touch panels, head-mounted displays (HMDs), and speakers.

[0022] A color evaluation program used to enable a computer to function as a color evaluation system includes program code for implementing the various functional modules of the color evaluation system. This color evaluation program can be provided, for example, non-transitory, recorded on a tangible recording medium such as a CD-ROM, DVD-ROM, or semiconductor memory. Alternatively, the color evaluation program can be provided via a communication network as a data signal superimposed on a carrier wave. The provided color evaluation program is, for example, recorded in memory.

[0023] refer to Figure 1 The structure of a color evaluation system 10 involved in an example will be described. Figure 1 This is a diagram showing the functional structure of the color evaluation system 10.

[0024] The color evaluation system 10 includes a processor 101 and a memory 102. In one example, the processor 101 functions as an acquisition unit 11, a calculation unit 12, a comparison unit 13, an evaluation unit 14, and a generation unit 15. The memory 102 pre-stores color spectra as spectral data of color, a subject model representing the subject's recognition of color, and a reference model representing the color recognition of a reference color visioner. The memory 102 can store more than one color spectrum, more than one subject model, and more than one reference model. Both the subject model and the reference model are color vision models that represent the human color vision system through algorithms or data. The color vision system is composed of the eyeball, optic nerve, visual cortex, etc.

[0025] The subject is a hypothetical person assumed to be color, while the reference color visioner is a hypothetical person assumed to be color-deficient for comparison with the subject. The subject can be a person with normal color vision or a specific type of color weakness, such as type P or type D. For example, when a specific type of color weakness is chosen as the subject, a person with normal color vision is assumed to be the reference color visioner; conversely, when a person with normal color vision is chosen as the subject, a person with a specific type of color weakness is assumed to be the reference color visioner. Reference models can be prepared separately for both people with normal color vision and those with color weakness. When a person with color weakness is assumed to be the reference color visioner, the reference model can be prepared according to the type of color vision, such as a type P reference model, a type D reference model, a type T reference model, and a type A reference model. Additionally, reference models that take into account differences in the intensity or severity of color weakness can be prepared.

[0026] The acquisition unit 11 is a functional module that acquires the reference spectrum, the subject model, and the reference model. The calculation unit 12 is a functional module that calculates the subject response representing the subject's recognition of the reference color based on the reference spectrum and the subject model, and calculates the reference response representing the reference colorimeter's recognition of the reference color based on the reference spectrum and the reference model. The comparison unit 13 is a functional module that calculates the difference between the subject response and the reference response as the response difference. The evaluation unit 14 is a functional module that evaluates the reference color based on the response difference. The generation unit 15 is a functional module that generates the spectrum of a color with a response difference smaller than the reference spectrum, i.e., an adjustment spectrum, based on the reference spectrum and the response difference. The color with the adjustment spectrum is a color that is equally recognized by both the subject and the reference colorimeter; that is, a color that is equally recognized by both normal colorimeters and color-weak individuals.

[0027] [System Operation] refer to Figure 2 The color evaluation method performed by the color evaluation system 10 is described. Figure 2 This is a flowchart illustrating an example of the operation of the color evaluation system 10 as a processing flow S1.

[0028] In step S11, the acquisition unit 11 acquires the reference spectrum, the subject model, and the reference model. In one example, the user of the color evaluation system 10 inputs a reference color, the subject's color vision type, and the reference color vision type. The acquisition unit 11 accepts this input and reads from the memory 102 the reference spectrum (which is the spectrum of the reference color), the subject model corresponding to the subject's color vision type, and the reference model corresponding to the reference color vision type. Alternatively, the acquisition unit 11 may directly accept the reference spectrum, subject model, and reference model as input. Alternatively, the acquisition unit 11 may receive the reference spectrum, subject model, and reference model from another computer.

[0029] In one example, the reference spectrum is composed of the brightness (cd / m²) at various wavelengths (nm) in the visible light region.2 The reference spectrum can be represented by a spectral function that expresses the relationship between wavelength and brightness.

[0030] In one example, both the subject model and the reference model represent the sensitivity characteristics of the L-cone, M-cone, and S-cone, respectively. Sensitivity characteristics are also known as spectral characteristics. Figure 3 This is a graph showing examples of the sensitivity characteristics of each cone for individuals with normal color vision and those with color weakness. The horizontal and vertical axes of the graph represent wavelength (nm) and relative sensitivity, respectively. Figure 3 The color-deficient individual shown corresponds to type P color weakness. Compared to individuals with normal color vision (type C), this color-deficient individual exhibits a greater overlap in the sensitivity characteristics of their M and L cones. This difference in sensitivity characteristics leads to differences in color perception or recognition. Figure 3 The color vision models shown can be represented by sensitivity functions that express the relationship between wavelength and relative sensitivity. These sensitivity functions can be prepared separately for each of the L-cone, M-cone, and S-cone. In this invention, such sensitivity functions are also referred to as "cone sensitivity functions." The subject model includes sensitivity functions representing the sensitivity characteristics of each of the subject's L-cone, M-cone, and S-cone. The reference model includes sensitivity functions representing the sensitivity characteristics of each of the reference color visioner's L-cone, M-cone, and S-cone. These color vision models can be generated based on measured values ​​obtained from determining an individual's sensitivity to the L-cone, M-cone, and S-cone, or based on representative values ​​such as C-type, P-type, and D-type weakness.

[0031] Return to Figure 2 In step S12, the calculation unit 12 calculates the subject response representing the subject's recognition of the reference color based on the reference spectrum and the subject model.

[0032] In one example, the calculation unit 12 calculates the subject response representing the response of each of the subject's L-cone, M-cone, and S-cone. In this example, the subject response defined by the response α from the L-cone, the response β from the M-cone, and the response γ from the S-cone is represented as (α, β, γ). The calculation unit 12 calculates the response of the cone that perceives the reference color for the subject's L-cone, M-cone, and S-cone as follows: The calculation unit 12 calculates the product of the relative sensitivity of the cone at each wavelength in the visible light region and the brightness shown in the reference spectrum. This product can be said to represent the degree of excitation. The calculation unit 12 calculates the sum of the products at each wavelength as the response from the cone.

[0033] Let the wavelength be represented by x, and the sensitivity functions of the L-cone, M-cone, and S-cone be represented by f, respectively. L (x), f M (x), f S(x) represents the spectral function of the reference color as g(x). At this time, the responses α, β, and γ can be expressed as equations (1) to (3), respectively.

[0034] α=∫f L (x)g(x)dx……(1) β=∫f M (x)g(x)dx……(2) γ=∫f S (x)g(x)dx……(3) That is, the calculation unit 12 calculates the integral of the product of the sensitivity function and the spectral function in each cone of the subject as the subject's response (α, β, γ).

[0035] In step S13, the calculation unit 12 calculates a reference response representing the reference colorimeter's recognition of a reference color based on the reference spectrum and the reference model. In one example, the calculation unit 12 calculates reference responses representing the responses of the L-cone, M-cone, and S-cone from the reference colorimeter. In this example, the reference response defined by the response α´ from the L-cone, the response β´ from the M-cone, and the response γ´ from the S-cone is expressed as (α´, β´, γ´). Similar to the case of calculating the subject's response, the calculation unit 12 calculates the response of each of the L-cone, M-cone, and S-cone of the reference colorimeter to the perceived reference color using the above equations (1) to (3), and obtains the reference response (α´, β´, γ´). That is, the calculation unit 12 calculates the integral of the product of the sensitivity function and the spectral function in each cone of the reference colorimeter as the reference response (α´, β´, γ´).

[0036] In step S14, the comparison unit 13 calculates the difference between the subject's response and the reference response as the response difference. When the subject's response and the reference response represent responses from the L-cone, M-cone, and S-cone respectively, the comparison unit 13 calculates the difference between the subject's response and the reference response for each of the L-cone, M-cone, and S-cone, and calculates the response difference based on a combination of the differences in the L-cone, M-cone, and S-cone. For example, the comparison unit 13 obtains the response difference (α-α´, β-β´, γ-γ´). Alternatively, the comparison unit 13 can calculate the response difference based on a ratio relative to the reference response; in this case, the response difference can be expressed as {(α-α´) / α´, (β-β´) / β´, (γ-γ´) / γ´}.

[0037] In step S15, the evaluation unit 14 evaluates the reference color based on the response difference. For example, if the response difference is above a predetermined threshold Th, the evaluation unit 14 determines that the reference color is a color that is recognized differently by the subject and the reference color visioner. On the other hand, if the response difference is below its threshold Th, the evaluation unit 14 determines that the reference color is a color that is equally recognized by the subject and the reference color visioner. The threshold Th is set to determine a color that is equally recognized by the subject and the reference color visioner. The evaluation unit 14 outputs the determination result. The evaluation unit 14 can display the determination result on a display device, store the determination result in a predetermined storage device such as the memory 102, or send the determination result to other computer systems.

[0038] When using the response of a cone, the evaluation unit 14 can set the threshold Th for the L-cone. L Threshold Th for M cone M and Th for S-cone S The combination of values ​​is used as the threshold Th. In this case, the evaluation unit 14 compares the difference in each of the L-cone, M-cone, and S-cone with the threshold in that cone. If the difference in at least one of the three cones is above the threshold, the evaluation unit 14 determines that the reference color is a color that is perceived differently by the subject and the reference color visioner. On the other hand, if the differences in all three cones are less than the threshold, the evaluation unit 14 determines that the reference color is a color that is perceived equally by the subject and the reference color visioner.

[0039] In step S16, the generation unit 15 determines whether to generate an adjustment spectrum. In one example, if the evaluation unit 14 determines that the reference color is a color that is perceived differently by the subject and the reference color visioner, the generation unit 15 determines to generate an adjustment spectrum ("Yes" in step S16). In this case, the process proceeds to step S17. On the other hand, if the evaluation unit 14 determines that the reference color is a color that is perceived equally by the subject and the reference color visioner, the generation unit 15 determines not to generate an adjustment spectrum ("No" in step S16). In this case, step S17 is not executed, and the processing flow S1 ends.

[0040] In step S17, the generation unit 15 generates an adjustment spectrum for a color whose response difference is smaller than the reference spectrum, based on the reference spectrum and the response difference. In one example, the generation unit 15 generates a candidate spectrum by changing at least a portion of the reference spectrum. Then, the generation unit 15 calculates the above equations (1) to (3) based on the spectral function of the candidate spectrum and the sensitivity functions of the subject's L-cone, M-cone, and S-cone, and calculates the subject's response (α, β, γ) corresponding to the candidate spectrum. Furthermore, the generation unit 15 calculates the above equations (1) to (3) based on the spectral function of the candidate spectrum and the sensitivity functions of the reference colorimeter's L-cone, M-cone, and S-cone, and calculates the reference response (α´, β´, γ´) corresponding to the candidate spectrum. Next, the generation unit 15 calculates the response difference between the subject's response (α, β, γ) and the reference response (α´, β´, γ´). While modifying at least a portion of the reference spectrum, the generation unit 15 repeatedly calculates the subject's response (α, β, γ) and the reference response (α', β', γ'), and calculates the response difference. Then, the generation unit 15 selects a candidate spectrum with a response difference smaller than that in the reference spectrum as an adjustment spectrum. For example, the generation unit 15 selects a candidate spectrum with a response difference below the aforementioned threshold Th as the adjustment spectrum. In other words, the adjustment spectrum selected based on the threshold Th is a color spectrum in which the subject's response is consistent with or approximately the reference response.

[0041] The generation unit 15 outputs an adjustment spectrum. The generation unit 15 can display the adjustment spectrum on a display device, store the adjustment spectrum in a specified storage device such as memory 102, or send the adjustment spectrum to other computer systems. Alternatively, the generation unit 15 can output the adjustment spectrum to a pigment manufacturing apparatus or a manufacturing apparatus as a group of apparatuses. A pigment is a substance that imparts color to an object. A pigment can be a dye or a pigment pigment. The manufacturing apparatus can be a component of the color evaluation system 10 or can be located outside the color evaluation system 10. The manufacturing apparatus refers to the adjustment spectrum, selects one or more pigment materials from a variety of pre-prepared pigment materials, and determines a mixing ratio for each selected pigment material. Then, the manufacturing apparatus mixes the selected one or more pigment materials according to the determined mixing ratio to manufacture the pigment. The manufacturing apparatus can generate pigment according to one or more pigment materials and mixing ratios selected by the operator. In summary, the manufacturing apparatus manufactures pigment based on the adjustment spectrum. This pigment has an adjustment spectrum and can help to represent colors considering the diversity of color perception.

[0042] [Variation Example] The technology involved in this invention has been described in detail above based on various examples. However, this invention is not limited to the above examples. Various modifications can be made to the technology involved in this invention without departing from its spirit.

[0043] (Example 1) In the above example, both the subject model and the reference model include a sensitivity function for the cone. As another example, both the subject model and the reference model may include a sensitivity function representing the relationship between the color spectrum and brain waves. In this invention, this sensitivity function is also referred to as a "sensitivity function of brain waves." The sensitivity function of brain waves can be a sensitivity function representing the relationship between the color spectrum and steady-state visual evoked potentials (SSVEPs) obtained by processing brain waves through frequency analysis such as Fourier transform. SSVEP is represented as the power spectral density obtained by performing a Fourier transform on the frequency components of the observer's brain wave signal when the observer observes a periodically changing color. The subject model is generated based on the subject's brain waves, and the reference model is generated based on the brain waves of a reference color visioner.

[0044] When using a sensitivity function of brainwaves, the calculation unit 12 inputs a reference spectrum to the sensitivity function of the subject and calculates the subject response based on the subject's brainwaves. Furthermore, the calculation unit 12 inputs a reference spectrum to the sensitivity function of a reference color visioner and calculates a reference response based on the reference color visioner's brainwaves. The comparison unit 13 calculates the difference between these two responses as the response difference. When using a sensitivity function of brainwaves, similar to the case of using a cone sensitivity function, the evaluation unit 14 can evaluate a reference color based on the response difference, the generation unit 15 can generate an adjustment spectrum based on the reference spectrum and the response difference, and the manufacturing apparatus can manufacture pigment based on the adjustment spectrum.

[0045] (Second variation) The sensitivity function of brainwaves can be a sensitivity function f representing the ratio of the subject's SSVEP (first SSVEP) when visually recognizing a change from a specified first gray to a specified second gray to the subject's SSVEP (second SSVEP) when visually recognizing a change from a base color to another specified color. B That is, f B = (Size of the 2nd SSVEP) / (Size of the 1st SSVEP). The concentrations of the 1st and 2nd grays are different. Another color can be pre-defined based on a reference color; for example, it could be a color that is symmetrical to the reference color in a color coordinate system with the difference in cone responses as the axis. This coordinate system is also known as the MB-DKL space. Alternatively, the other color could be a color that makes it difficult for the object to distinguish from the reference color, or it could be a color located on the same confusion line as the reference color. Based on the sensitivity function f BThe proportion obtained in response is a value greater than 0. SSVEP corresponds to the brainwave frequency components of subjects who visually recognize periodic changes in color. These brainwaves are measured at one or more locations, for example, four or more locations, corresponding to the visual cortex in the posterior part of the brain. If the subject cannot perceive the difference between the reference color and another color, the brainwave component decreases, as indicated by the sensitivity function f. B The obtained response becomes a value close to 0. On the other hand, if the subject can perceive the difference between the reference color and another color, the brainwave component increases, as seen in the sensitivity function f. B The obtained response becomes a large value.

[0046] The subject model includes a sensitivity function f for the subject. B The reference model includes a sensitivity function f for a reference color visioner. B Using these sensitivity functions, the acquisition unit acquires the first and second SSVEPs of the subject who visually identifies the reference color, and the first and second SSVEPs of the reference colorimeter who visually identifies the reference color. Furthermore, the acquisition unit acquires a subject model and a reference model. The calculation unit calculates the sensitivity function f for the subject by substituting the subject's first and second SSVEPs. B The obtained proportion is taken as the subject's response. Similarly, the calculation unit calculates the first SSVEP and the second SSVEP of the reference color visioner and substitutes them into the sensitivity function f for the reference color visioner. B The obtained proportion is used as a reference response. The comparison unit calculates the difference between the subject's response and the reference response as the response difference. Even when using the sensitivity function f... B In this case, the evaluation department can also evaluate the reference color based on the response difference, the generation department can also generate an adjustment spectrum based on the reference spectrum and the response difference, and the manufacturing apparatus can also manufacture pigments based on the adjustment spectrum.

[0047] (3rd variation) As another example of brainwave-based processing, both the subject model and the reference model can include a database showing the relationship between sample colors and the SSVEPs of subjects who visually recognize those sample colors for multiple sample colors. Each record in the database includes a sample color and the SSVEP of the subject who visually recognizes that sample color. This database can be prepared for each subject, and therefore, separately for the subject and reference color visioners. Alternatively, the database can be a general database prepared by collecting the correspondences for multiple sample colors from multiple subjects and statistically processing the correspondences for each sample color. The general database is prepared by calculating a statistical value (e.g., an average) of the SSVEPs of multiple subjects for each of the multiple sample colors and generating a correspondence between the sample color and the statistical value. The general database is used as both the subject model and the reference model.

[0048] When using a database, the acquisition unit acquires the SSVEP of the subject who visually recognizes the reference color and the SSVEP of the reference colorimeter who visually recognizes the reference color. Furthermore, the acquisition unit acquires a subject model and a reference model. Accessing the database is one example of acquiring the subject model and the reference model. The calculation unit refers to the database corresponding to the subject and determines the sample color corresponding to the acquired subject's SSVEP as the subject response. The calculation unit can select two or more SSVEPs close to the acquired SSVEP from the database, linearly combine these two or more SSVEPs to generate a single SSVEP, and determine the sample color corresponding to the generated SSVEP as the subject response. The calculation unit refers to the database corresponding to the reference colorimeter and determines the sample color corresponding to the acquired reference colorimeter's SSVEP as the reference response. Similar to the subject response case, the calculation unit can perform linear combination to determine the reference response. The comparison unit calculates the distance between the sample color shown as the subject response and the sample color shown as the reference response in the color space (e.g., HSV color space) as the response difference. Even when using a database that represents the correspondence between sample colors and SSVEPs, the evaluation unit can evaluate the reference color based on the response difference, the generation unit can generate an adjustment spectrum based on the reference spectrum and the response difference, and the manufacturing apparatus can manufacture the pigment based on the adjustment spectrum.

[0049] (4th variation) Both the subject model and the reference model can include a learned model that takes input from SSVEP and infers the sample color. This learned model is generated through machine learning, a method that autonomously discovers patterns or rules by repeatedly learning from provided information. In this machine learning, training data representing the correspondence between sample colors and the SSVEPs of subjects who visually recognize those sample colors is used, as in the database described above. In this machine learning, the sample color is used as the ground truth. The generation of the learned model can be performed by the learning unit of the color evaluation system. Alternatively, a learned model generated by a computer system different from the color evaluation system can be ported to the color evaluation system. Since the learned model infers the sample color based on the SSVEP, it can, similarly to the database described above, represent the relationship between the sample color and the steady-state visual evoked potentials of subjects who visually recognize those sample colors. The learned model can be prepared for each subject, and therefore can be prepared separately for the subject and the reference colorimeter. Alternatively, the learned model can be generated in a manner that allows both the subject and the reference colorimeter to use it.

[0050] When using a fully learned model, the acquisition unit acquires the SSVEP of the subject who visually recognizes the reference color and the SSVEP of the reference colorimeter who visually recognizes the reference color. Furthermore, the acquisition unit acquires a subject model and a reference model. Accessing the fully learned model is one example of acquiring the subject model and the reference model. The calculation unit inputs the acquired subject's SSVEP into the fully learned model corresponding to the subject and determines the sample color output from the fully learned model as the subject's response. The calculation unit can select two or more SSVEPs close to the acquired SSVEP from the aforementioned database, linearly combine these two or more SSVEPs to generate a single SSVEP, and input the generated SSVEP into the fully learned model. The calculation unit inputs the acquired reference colorimeter's SSVEP into the fully learned model corresponding to the reference colorimeter and determines the sample color output from the fully learned model as the reference response. Similar to the subject response case, the calculation unit can perform linear combination. The comparison unit calculates the distance between the sample color represented by the subject's response and the sample color represented by the reference response in a color space (e.g., HSV color space) as the response difference. When using a learned model that accepts SSVEP input and outputs sample colors, the evaluation unit can also evaluate the reference color based on the response difference, the generation unit can also generate an adjustment spectrum based on the reference spectrum and the response difference, and the manufacturing apparatus can also manufacture the pigment based on the adjustment spectrum.

[0051] Figure 4The flowchart of processing flow S2 is shown as an example of the operation of the color evaluation system corresponding to the 2nd to 4th variations.

[0052] In step S21, the acquisition unit acquires the SSVEP of the subject who visually recognizes the reference color, the SSVEP of the reference colorimeter who visually recognizes the reference color, the subject model, and the reference model. The SSVEP of the subject who visually recognizes the reference color and the SSVEP of the reference colorimeter are examples of reference color information. In step S22, the calculation unit calculates the subject response representing the subject's recognition of the reference color based on the subject's SSVEP and the subject model. In step S23, the calculation unit calculates the reference response representing the reference colorimeter's recognition of the reference color based on the reference colorimeter's SSVEP and the reference model. The processing in steps S24 to S27 is the same as that in steps S14 to S17 of the processing flow S1.

[0053] (5th variation) As another example different from pyramidal vertigo and brainwaves, a sensitivity function reflecting changes in pupil size can also be considered. The pupil size changes based on how much attention the subject receives from a particular color. If the subject does not perceive a difference between one color Cx and another color Cd, the predicted change in pupil size is minimal (i.e., the change in pupil size is less than a predetermined threshold). Conversely, if the subject can perceive a difference between color Cx and another color Cd, the predicted change in pupil size is above a predetermined threshold.

[0054] The pupil sensitivity function can be a sensitivity function that represents the relationship between the color spectrum and the change in pupil size. When using the pupil sensitivity function, the calculation unit 12 inputs a reference spectrum into the subject's sensitivity function and calculates the subject's response based on the change in pupil size. Furthermore, the calculation unit 12 inputs the reference spectrum into the sensitivity function of a reference color visioner and calculates a reference response based on the change in pupil size of the reference color visioner. The comparison unit 13 calculates the difference between these two responses as the response difference. When using the pupil sensitivity function, similarly to when using the cone sensitivity function, the evaluation unit 14 can evaluate the reference color based on the response difference, the generation unit 15 can generate an adjustment spectrum based on the reference spectrum and the response difference, and the manufacturing apparatus can manufacture the pigment based on the adjustment spectrum.

[0055] (Sixth variation) As another example, a sensitivity function reflecting changes in cerebral blood flow could be constructed. This function would be based on how much attention a subject receives from a particular color, and how much this affects cerebral blood flow. If the subject does not perceive a difference between color Cx and another color Cd, the predicted change in cerebral blood flow is minimal (i.e., the change is less than a predetermined threshold). Conversely, if the subject can perceive a difference between color Cx and another color Cd, the predicted change in cerebral blood flow is above a predetermined threshold.

[0056] The sensitivity function for cerebral blood flow can be a sensitivity function that represents the relationship between the color spectrum and the change in cerebral blood flow. When using the sensitivity function for cerebral blood flow, the calculation unit 12 inputs a reference spectrum into the subject's sensitivity function and calculates the subject's response based on the change in the subject's cerebral blood flow. Furthermore, the calculation unit 12 inputs the reference spectrum into the sensitivity function of a reference color visioner and calculates a reference response based on the change in the reference color visioner's cerebral blood flow. The comparison unit 13 calculates the difference between these two responses as the response difference. When using the sensitivity function for cerebral blood flow, similarly to when using the cone sensitivity function, the evaluation unit 14 can evaluate the reference color based on the response difference, the generation unit 15 can generate an adjustment spectrum based on the reference spectrum and the response difference, and the manufacturing apparatus can manufacture the pigment based on the adjustment spectrum.

[0057] (Seventh variation) As another example, a sensitivity function reflecting changes in amylase concentration could be constructed. The change in amylase concentration is determined by how much attention a subject receives from a particular color. If the subject does not perceive a difference between one color Cx and another color Cd, the predicted change in amylase concentration is small (i.e., the change in amylase concentration is less than a predetermined threshold). Conversely, if the subject can perceive a difference between color Cx and another color Cd, the predicted change in amylase concentration is large (i.e., the change in amylase concentration is above a predetermined threshold).

[0058] The sensitivity function for amylase concentration can be a sensitivity function that represents the relationship between the color spectrum and the change in amylase concentration. When using the amylase concentration sensitivity function, the calculation unit 12 inputs a reference spectrum into the subject's sensitivity function and calculates the subject's response based on the change in the subject's amylase concentration. Furthermore, the calculation unit 12 inputs the reference spectrum into a reference colorimeter's sensitivity function and calculates a reference response based on the change in the reference colorimeter's amylase concentration. The comparison unit 13 calculates the difference between these two responses as the response difference. When using the amylase concentration sensitivity function, similarly to when using the cone sensitivity function, the evaluation unit 14 can evaluate the reference color based on the response difference, the generation unit 15 can generate an adjustment spectrum based on the reference spectrum and the response difference, and the manufacturing apparatus can manufacture the pigment based on the adjustment spectrum.

[0059] (Example 8) As another example, a sensitivity function reflecting changes in gaze could be constructed. This function measures how much attention a subject pays to a particular color, thus affecting the prediction of a change in gaze. If the subject does not perceive a difference between color Cx and color Cd, the prediction is that the change in gaze is small (i.e., the change in gaze is less than a predetermined threshold). Conversely, if the subject can perceive a difference between color Cx and color Cd, the prediction is that the change in gaze is significant (i.e., the change in gaze is above a predetermined threshold).

[0060] The gaze sensitivity function can be a sensitivity function that represents the relationship between the color spectrum and the change in gaze. When using the gaze sensitivity function, the calculation unit 12 inputs a reference spectrum into the subject's sensitivity function and calculates the subject's response based on the change in gaze. Furthermore, the calculation unit 12 inputs the reference spectrum into a reference colorimeter's sensitivity function and calculates a reference response based on the change in gaze. The comparison unit 13 calculates the difference between these two responses as the response difference. When using the gaze sensitivity function, similarly to when using a cone sensitivity function, the evaluation unit 14 can evaluate a reference color based on the response difference, the generation unit 15 can generate an adjustment spectrum based on the reference spectrum and the response difference, and the manufacturing apparatus can manufacture pigments based on the adjustment spectrum.

[0061] (9th variation) As another example, a sensitivity function reflecting changes in skin potential could be constructed. This function is based on how much attention a subject receives from a particular color, and how much that color affects the change in skin potential. If the subject does not perceive a difference between color Cx and color Cd, a small change in skin potential is predicted (i.e., the change in skin potential is less than a predetermined threshold). Conversely, if the subject can perceive a difference between color Cx and color Cd, a change in skin potential is predicted (i.e., the change in skin potential is above a predetermined threshold).

[0062] The skin potential sensitivity function can be a sensitivity function that represents the relationship between the color spectrum and the change in skin potential. When using the skin potential sensitivity function, the calculation unit 12 inputs a reference spectrum into the subject's sensitivity function and calculates the subject's response based on the change in the subject's skin potential. Furthermore, the calculation unit 12 inputs the reference spectrum into a reference colorimeter's sensitivity function and calculates a reference response based on the change in the reference colorimeter's skin potential. The comparison unit 13 calculates the difference between these two responses as the response difference. When using the skin potential sensitivity function, similarly to when using a cone sensitivity function, the evaluation unit 14 can evaluate a reference color based on the response difference, the generation unit 15 can generate an adjustment spectrum based on the reference spectrum and the response difference, and the manufacturing apparatus can manufacture pigment based on the adjustment spectrum.

[0063] The color evaluation system may not perform at least one of the evaluation of a reference color based on response difference and the generation of an adjusted spectrum. Therefore, the color evaluation system may not have a functional module equivalent to at least one of the evaluation unit 14 and the generation unit 15.

[0064] The processing order of a method executed by at least one processor is not limited to the example above. For example, some of the steps described above may be omitted, or the steps may be executed in a different order. Furthermore, any two or more of the steps described above may be combined, or some of the steps may be modified or deleted. Alternatively, other steps may be executed in addition to those described above.

[0065] In comparing the magnitude of two values ​​in this invention, either "above" or "greater than" can be used, or either "below" or "less than" can be used.

[0066] In this invention, the statement "at least one processor executes the first process, executes the second process, ... executes the nth process" or its corresponding statement represents the concept of a situation where the processor, which is the executing entity of the n processes from the first process to the nth process, changes midway. That is, this statement represents the concept of two situations: the situation where all n processes are executed by the same processor, and the situation where the processor changes in any way among the n processes.

[0067] [Postscript] As can be understood from the various examples above, the present invention includes the following embodiments.

[0068] (Note 1) A color evaluation system, which has: The acquisition department acquires reference color information related to the reference color, a subject model representing the subject's color recognition, and a reference model representing the reference colorimeter's color recognition. The calculation unit calculates, based on the reference color information and the subject model, a subject response representing the subject's recognition of the reference color, and calculates, based on the reference color information and the reference model, a reference response representing the reference colorimeter's recognition of the reference color; and The comparison unit calculates the difference between the subject's response and the reference response as the response difference.

[0069] (Note 2) According to the color evaluation system described in Appendix 1, it also has the following features: The evaluation department evaluates the reference color based on the response difference.

[0070] (Note 3) According to the color evaluation system described in Appendix 1 or 2, it also has the following features: The generation unit generates an adjustment spectrum for a color whose response difference is smaller than the reference spectrum, based on a reference spectrum that serves as the reference color and the response difference.

[0071] (Note 4) According to any one of Annexes 1 to 3, the color evaluation system wherein, The reference color information is the reference spectrum that serves as the spectrum of the reference color. The calculation unit calculates the subject response based on the reference spectrum and the subject model, and calculates the reference response based on the reference spectrum and the reference model.

[0072] (Note 5) According to the color evaluation system described in Appendix 4, wherein... The subject model includes sensitivity functions representing the sensitivity characteristics of the subject's L-cone, M-cone, and S-cone, respectively. The reference model includes sensitivity functions representing the sensitivity characteristics of the L-cone, M-cone, and S-cone of the reference color visioner. The calculation unit performs the following: Based on the reference spectrum and the subject's sensitivity function, calculate the subject response, representing the responses from the L-cone, M-cone, and S-cone of the subject, respectively; and Based on the reference spectrum and the sensitivity function of the reference colorimeter, the reference response, representing the respective responses of the L-cone, M-cone, and S-cone from the reference colorimeter, is calculated. The comparison unit performs the following: For each of the L-cone, the M-cone, and the S-cone, calculate the difference between the subject's response and the reference response; and The response difference is calculated based on the combination of the differences in the L cone, the M cone, and the S cone.

[0073] (Note 6) According to the color evaluation system described in Appendix 4, wherein... The subject model includes a sensitivity function representing the relationship between the color spectrum and the subject's brain waves. The reference model includes a sensitivity function representing the relationship between the color spectrum and the brainwaves of the reference color visioner. The calculation unit performs the following: Based on the reference spectrum and the subject's sensitivity function, the subject's response based on the subject's brainwaves is calculated; and The reference response based on the brainwaves of the reference color visioner is calculated based on the reference spectrum and the sensitivity function of the reference color visioner.

[0074] (Note 7) According to any one of Annexes 1 to 3, the color evaluation system wherein, The acquisition unit acquires the steady-state visual evoked potentials of the subject who visually identifies the reference color and the steady-state visual evoked potentials of the reference colorist who visually identifies the reference color as the reference color information. The calculation unit calculates the subject response based on the subject's steady-state visual evoked potential and the subject model, and calculates the reference response based on the reference color visioner's steady-state visual evoked potential and the reference model.

[0075] (Note 8) According to the color evaluation system described in Appendix 7, wherein... The subject model includes a sensitivity function representing the magnitude of the subject's steady-state visual evoked potential relative to the magnitude of the subject's steady-state visual evoked potential when the subject visually recognizes a change from a predetermined first gray to a predetermined second gray, and the magnitude of the subject's steady-state visual evoked potential when the subject visually recognizes a change from a reference color to another predetermined color. The reference model includes a sensitivity function representing the magnitude of the steady-state visual evoked potential of the reference colorimeter relative to the magnitude of the steady-state visual evoked potential of the reference colorimeter when the reference colorimeter visually recognizes a change from the first gray to the second gray, and the magnitude of the steady-state visual evoked potential of the reference colorimeter when the reference colorimeter visually recognizes a change from the reference color to the other color. The calculation unit performs the following: Based on the subject's steady-state visual evoked potentials and the subject's sensitivity function, the proportion of the subject's response is calculated; and The reference colorimeter's proportion is calculated as the reference response based on the steady-state visual evoked potential and the reference colorimeter's sensitivity function.

[0076] (Note 9) According to the color evaluation system described in Appendix 7, wherein... The subject model represents the relationship between sample color and the steady-state visual evoked potentials of the subject who visually recognizes that sample color. The reference model represents the relationship between the sample color and the steady-state visual evoked potential of the reference colorimeter who visually identifies the sample color. The calculation unit performs the following: Using the subject model, the sample color corresponding to the steady-state visual evoked potential of the subject acquired by the acquisition unit is calculated as the subject's response; and Using the reference model, the sample color corresponding to the steady-state visual evoked potential of the reference color visioner acquired by the acquisition unit is calculated as the reference response.

[0077] (Postscript 10) According to the color evaluation system described in Appendix 9, wherein... The subject model includes a database representing the relationship between the sample color and the subject's steady-state visual evoked potentials. The reference model includes a database representing the relationship between the sample color and the steady-state visual evoked potential of the reference color visioner.

[0078] (Postscript 11) According to the color evaluation system described in Appendix 9, wherein... The subject model includes a learned model that receives the subject's steady-state visual evoked potentials as input and outputs the sample color. The reference model includes a learned model that receives input from the steady-state visual evoked potentials of the reference colorimeter and outputs the sample colors.

[0079] (Postscript 12) According to the color evaluation system described in Appendix 4, wherein... The subject model includes a sensitivity function representing the relationship between the color spectrum and the amount of change in the subject's pupil size. The reference model includes a sensitivity function representing the relationship between the color spectrum and the change in pupil size of the reference color visioner. The calculation unit performs the following: Based on the baseline spectrum and the subject's sensitivity function, calculate the subject response based on the change in the subject's pupil size; and The reference response, based on the reference spectrum and the sensitivity function of the reference color visioner, is calculated based on the change in pupil size of the reference color visioner.

[0080] (Postscript 13) According to the color evaluation system described in Appendix 4, wherein... The subject model includes a sensitivity function representing the relationship between the color spectrum and changes in the subject's cerebral blood flow. The reference model includes a sensitivity function representing the relationship between the color spectrum and changes in cerebral blood flow in the reference color visioner. The calculation unit performs the following: Based on the baseline spectrum and the subject's sensitivity function, the subject response is calculated based on the change in cerebral blood flow in the subject; and The reference response, based on the change in cerebral blood flow of the reference color visioner, is calculated according to the reference spectrum and the sensitivity function of the reference color visioner.

[0081] (Postscript 14) According to the color evaluation system described in Appendix 4, wherein... The subject model includes a sensitivity function representing the relationship between the color spectrum and the change in the subject's amylase concentration. The reference model includes a sensitivity function representing the relationship between the color spectrum and the change in amylase concentration of the reference colorimeter. The calculation unit performs the following: Based on the baseline spectrum and the subject's sensitivity function, the subject's response based on the change in the subject's amylase concentration is calculated; and The reference response, based on the change in amylase concentration of the reference colorimeter, is calculated according to the reference spectrum and the sensitivity function of the reference colorimeter.

[0082] (Postscript 15) According to the color evaluation system described in Appendix 4, wherein... The subject model includes a sensitivity function representing the relationship between the color spectrum and the amount of change in the subject's gaze. The reference model includes a sensitivity function representing the relationship between the color spectrum and the amount of change in the line of sight of the reference color visioner. The calculation unit performs the following: Based on the baseline spectrum and the subject's sensitivity function, calculate the subject response based on the change in the subject's gaze; and The reference response is calculated based on the reference spectrum and the sensitivity function of the reference color visioner, taking into account the change in the reference color visioner's line of sight.

[0083] (Postscript 16) According to the color evaluation system described in Appendix 4, wherein... The subject model includes a sensitivity function representing the relationship between the color spectrum and the change in the subject's skin potential. The reference model includes a sensitivity function representing the relationship between the color spectrum and the change in skin potential of the reference color visioner. The calculation unit performs the following: Based on the baseline spectrum and the subject's sensitivity function, the subject response is calculated based on the change in the subject's skin potential; and The reference response, based on the change in skin potential of the reference color visioner, is calculated according to the reference spectrum and the sensitivity function of the reference color visioner.

[0084] (Postscript 17) A color evaluation method, performed by a color evaluation system having at least one processor, the color evaluation method comprising the following steps: Acquire baseline color information related to the baseline color, a subject model representing the subject's color recognition, and a reference model representing the reference colorimeter's color recognition; Based on the reference color information and the subject model, a subject response representing the subject's recognition of the reference color is calculated; and based on the reference color information and the reference model, a reference response representing the reference colorimeter's recognition of the reference color is calculated; and The difference between the subject's response and the reference response is calculated as the response difference.

[0085] (Postscript 18) A method for manufacturing a pigment, comprising the following steps: Acquire baseline color information related to the baseline color, a subject model representing the subject's color recognition, and a reference model representing the reference colorimeter's color recognition; Based on the reference color information and the subject model, a subject response representing the subject's recognition of the reference color is calculated, and based on the reference color information and the reference model, a reference response representing the reference colorimeter's recognition of the reference color is calculated. The difference between the subject's response and the reference response is calculated as the response difference; Based on a reference spectrum, which serves as the reference color, and the response difference, an adjusted spectrum is generated, which represents the spectrum of a color whose response difference is smaller than the reference spectrum; and Pigments are manufactured by adjusting the spectrum as described.

[0086] (Postscript 19) A color evaluation program that causes a computer to perform the following steps: Acquire baseline color information related to the baseline color, a subject model representing the subject's color recognition, and a reference model representing the reference colorimeter's color recognition; Based on the reference color information and the subject model, a subject response representing the subject's recognition of the reference color is calculated; and based on the reference color information and the reference model, a reference response representing the reference colorimeter's recognition of the reference color is calculated; and The difference between the subject's response and the reference response is calculated as the response difference.

[0087] According to notes 1, 17, and 19, the subject's recognition of the reference color is calculated as the subject's response, and the reference colorimeter's recognition of the reference color is calculated as the reference response. The difference between these two responses is calculated as the response difference. This response difference quantitatively represents the deviation in the subject's recognition of the reference color between the subject and the reference colorimeter. Therefore, by using this response difference, color (reference color) can be quantitatively evaluated from a color vision perspective. In one example, through this mechanism, a person with color weakness who has difficulty resonating with or enjoying colors with others in terms of color recognition can enjoy colors with others. Furthermore, it is possible to develop colors with visual effects close to those of color-weak individuals and those with normal color vision. As a result, indicators can be obtained when developing color materials such as dyes and paints, or products using such color materials (e.g., clothing products, writing instruments). Furthermore, it is possible to construct a world where color-weak individuals and those with normal color vision can enjoy products simultaneously by resonating with hues.

[0088] According to Note 2, color is automatically evaluated based on response difference, so users can easily know how color is evaluated from the perspective of human color vision.

[0089] According to Appendix 3, an adjusted spectrum with a response difference smaller than the reference spectrum is generated. By using this adjusted spectrum, colors can be provided that take into account color vision diversity. For example, colors that are equally recognizable between people with normal color vision and those with color weakness can be generated and provided.

[0090] According to Appendix 4, the subject's recognition of the reference color is calculated as the subject's response, and the reference colorimeter's recognition of the reference color is calculated as the reference response. The difference between these two responses is calculated as the response difference. This response difference quantitatively represents the deviation between the subject's and the reference colorimeter's recognition of the reference color. Therefore, by using this response difference, color (the reference color) can be quantitatively evaluated from a color vision perspective.

[0091] According to Appendix 5, a subject model and a reference model simulating the actual human color vision system were used, thus enabling more accurate calculation of the subject's response and the reference response. Therefore, a more accurate response difference can be obtained.

[0092] According to Note 6, a subject model and a reference model simulating actual human brainwaves were used, thus enabling more accurate calculation of the subject's response and the reference response. Therefore, a more accurate response difference can be obtained.

[0093] According to Appendix 7, the subject's response and reference response are calculated based on visual responses in the human brain. This method allows for the acquisition of subject and reference responses that more directly represent human recognition of a reference color, and thus can be expected to yield a more accurate response difference that represents the actual recognition bias.

[0094] According to Note 8, a sensitivity function that takes into account visual responses in the human brain is used as both the subject model and the reference model, thus enabling more accurate calculation of the subject's response and the reference response. Therefore, a more accurate response difference can be obtained.

[0095] According to Note 9, considering visual responses in the human brain, the relationship between color (sample color) and steady-state visual evoked potentials (SSVEP) is used as the subject model and reference model, thus enabling more accurate calculation of the subject's response and the reference response. Therefore, a more accurate response difference can be obtained. Furthermore, since the subject's response and the reference response are represented by color (sample color), these responses can be easily or intuitively obtained.

[0096] According to Note 10, the relationship between color (sample color) and steady-state visual evoked potential (SSVEP) is prepared through a database. Therefore, by referring to this database, the subject response and reference response can be easily obtained through this simple procedure.

[0097] According to Note 11, by learning the relationship between the prepared color (sample color) and the steady-state visual evoked potential (SSVEP), it is possible to expect to obtain high-precision subject responses and reference responses.

[0098] According to Note 12, a subject model and a reference model simulating the actual size of a human pupil were used, thus enabling a more accurate calculation of the subject's response and the reference response. Therefore, a more accurate response difference can be obtained.

[0099] According to Note 13, a subject model and a reference model simulating actual human cerebral blood flow were used, thus enabling more accurate calculation of the subject's response and the reference response. Therefore, a more accurate response difference can be obtained.

[0100] According to Appendix 14, a subject model and a reference model simulating actual human amylase concentration were used, thus enabling more accurate calculation of the subject response and reference response. Therefore, a more accurate response difference can be obtained.

[0101] According to Note 15, a subject model and a reference model simulating actual changes in human gaze were used, thus enabling more accurate calculation of the subject's response and the reference response. Therefore, a more accurate response difference can be obtained.

[0102] According to Note 16, a subject model and a reference model simulating actual changes in human skin potential were used, thus enabling a more accurate calculation of the subject's response and the reference response. Therefore, a more accurate response difference can be obtained.

[0103] According to Appendix 18, the subject's recognition of the reference color is calculated as the subject's response, and the reference colorimeter's recognition of the reference color is calculated as the reference response. The difference between these two responses is then calculated as the response difference. An adjusted spectrum with a response difference smaller than the reference spectrum is then generated, and a pigment is manufactured based on this adjusted spectrum. By using this pigment, which both the subject and the reference colorimeter expect to recognize equally, color diversity can be taken into account when providing colors.

[0104] Symbol Explanation 10-Color evaluation system, 11-Acquisition unit, 12-Calculation unit, 13-Comparison unit, 14-Evaluation unit, 15-Generation unit.

Claims

1. A color evaluation system, comprising: The acquisition department acquires reference color information related to the reference color, a subject model representing the subject's color recognition, and a reference model representing the reference colorimeter's color recognition. The calculation unit calculates, based on the reference color information and the subject model, a subject response representing the subject's recognition of the reference color, and calculates, based on the reference color information and the reference model, a reference response representing the reference colorimeter's recognition of the reference color; and The comparison unit calculates the difference between the subject's response and the reference response as the response difference.

2. The color evaluation system according to claim 1, further comprising: The evaluation department evaluates the reference color based on the response difference.

3. The color evaluation system according to claim 1, further comprising: The generation unit generates an adjustment spectrum for a color whose response difference is smaller than the reference spectrum, based on a reference spectrum that serves as the reference color and the response difference.

4. The color evaluation system according to any one of claims 1 to 3, wherein, The reference color information is the reference spectrum that serves as the spectrum of the reference color. The calculation unit calculates the subject response based on the reference spectrum and the subject model, and calculates the reference response based on the reference spectrum and the reference model.

5. The color evaluation system according to claim 4, wherein, The subject model includes sensitivity functions representing the sensitivity characteristics of the subject's L-cone, M-cone, and S-cone, respectively. The reference model includes sensitivity functions representing the sensitivity characteristics of the L-cone, M-cone, and S-cone of the reference color visioner. The calculation unit performs the following: Based on the reference spectrum and the subject's sensitivity function, the subject response, representing the respective responses of the L-cone, M-cone, and S-cone from the subject, is calculated; and Based on the reference spectrum and the sensitivity function of the reference colorimeter, the reference response, representing the respective responses of the L-cone, M-cone, and S-cone from the reference colorimeter, is calculated. The comparison unit performs the following: For each of the L-cone, the M-cone, and the S-cone, the difference between the subject's response and the reference response is calculated; and The response difference is calculated based on the combination of the differences in the L cone, the M cone, and the S cone.

6. The color evaluation system according to claim 4, wherein, The subject model includes a sensitivity function representing the relationship between the color spectrum and the subject's brain waves. The reference model includes a sensitivity function representing the relationship between the color spectrum and the brainwaves of the reference color visioner. The calculation unit performs the following: The subject response based on the subject's brainwaves is calculated based on the reference spectrum and the subject's sensitivity function. and The reference response based on the brainwaves of the reference color visioner is calculated based on the reference spectrum and the sensitivity function of the reference color visioner.

7. The color evaluation system according to any one of claims 1 to 3, wherein, The acquisition unit acquires the steady-state visual evoked potentials of the subject who visually identifies the reference color and the steady-state visual evoked potentials of the reference colorist who visually identifies the reference color as the reference color information. The calculation unit calculates the subject response based on the subject's steady-state visual evoked potential and the subject model, and calculates the reference response based on the reference color visioner's steady-state visual evoked potential and the reference model.

8. The color evaluation system according to claim 7, wherein, The subject model includes a sensitivity function representing the magnitude of the subject's steady-state visual evoked potential relative to the magnitude of the subject's steady-state visual evoked potential when the subject visually recognizes a change from a predetermined first gray to a predetermined second gray, and the magnitude of the subject's steady-state visual evoked potential when the subject visually recognizes a change from a reference color to another predetermined color. The reference model includes a sensitivity function representing the magnitude of the steady-state visual evoked potential of the reference colorimeter relative to the magnitude of the steady-state visual evoked potential of the reference colorimeter when the reference colorimeter visually recognizes a change from the first gray to the second gray, and the magnitude of the steady-state visual evoked potential of the reference colorimeter when the reference colorimeter visually recognizes a change from the reference color to the other color. The calculation unit performs the following: The proportion of the subject is calculated as the subject's response based on the subject's steady-state visual evoked potential and the subject's sensitivity function; and The reference colorimeter's proportion is calculated as the reference response based on the steady-state visual evoked potential and the reference colorimeter's sensitivity function.

9. The color evaluation system according to claim 7, wherein, The subject model represents the relationship between sample color and the steady-state visual evoked potentials of the subject who visually recognizes that sample color. The reference model represents the relationship between the sample color and the steady-state visual evoked potential of the reference colorimeter who visually identifies the sample color. The calculation unit performs the following: Using the subject model, the sample color corresponding to the steady-state visual evoked potential of the subject acquired by the acquisition unit is calculated as the subject response; and Using the reference model, the sample color corresponding to the steady-state visual evoked potential of the reference color visioner acquired by the acquisition unit is calculated as the reference response.

10. The color evaluation system according to claim 9, wherein, The subject model includes a database representing the relationship between the sample color and the subject's steady-state visual evoked potentials. The reference model includes a database representing the relationship between the sample color and the steady-state visual evoked potential of the reference color visioner.

11. The color evaluation system according to claim 9, wherein, The subject model includes a learned model that receives the subject's steady-state visual evoked potentials as input and outputs the sample color. The reference model includes a learned model that receives input from the steady-state visual evoked potentials of the reference colorimeter and outputs the sample colors.

12. The color evaluation system according to claim 4, wherein, The subject model includes a sensitivity function representing the relationship between the color spectrum and the amount of change in the subject's pupil size. The reference model includes a sensitivity function representing the relationship between the color spectrum and the change in pupil size of the reference color visioner. The calculation unit performs the following: The subject response, based on the reference spectrum and the subject's sensitivity function, is calculated based on the change in the subject's pupil size. and The reference response, based on the reference spectrum and the sensitivity function of the reference color visioner, is calculated based on the change in pupil size of the reference color visioner.

13. The color evaluation system according to claim 4, wherein, The subject model includes a sensitivity function representing the relationship between the color spectrum and changes in the subject's cerebral blood flow. The reference model includes a sensitivity function representing the relationship between the color spectrum and changes in cerebral blood flow in the reference color visioner. The calculation unit performs the following: The subject response, based on the change in cerebral blood flow in the subject, is calculated according to the reference spectrum and the subject's sensitivity function. and The reference response, based on the change in cerebral blood flow of the reference color visioner, is calculated according to the reference spectrum and the sensitivity function of the reference color visioner.

14. The color evaluation system according to claim 4, wherein, The subject model includes a sensitivity function representing the relationship between the color spectrum and the change in the subject's amylase concentration. The reference model includes a sensitivity function representing the relationship between the color spectrum and the change in amylase concentration of the reference colorimeter. The calculation unit performs the following: The subject response is calculated based on the baseline spectrum and the subject's sensitivity function, taking into account the change in the subject's amylase concentration. and The reference response, based on the change in amylase concentration of the reference colorimeter, is calculated according to the reference spectrum and the sensitivity function of the reference colorimeter.

15. The color evaluation system according to claim 4, wherein, The subject model includes a sensitivity function representing the relationship between the color spectrum and the amount of change in the subject's gaze. The reference model includes a sensitivity function representing the relationship between the color spectrum and the amount of change in the line of sight of the reference color visioner. The calculation unit performs the following: The subject response is calculated based on the reference spectrum and the subject's sensitivity function, taking into account the change in the subject's gaze. and The reference response is calculated based on the reference spectrum and the sensitivity function of the reference color visioner, taking into account the change in the reference color visioner's line of sight.

16. The color evaluation system according to claim 4, wherein, The subject model includes a sensitivity function representing the relationship between the color spectrum and the change in the subject's skin potential. The reference model includes a sensitivity function representing the relationship between the color spectrum and the change in skin potential of the reference color visioner. The calculation unit performs the following: The subject response, based on the change in the subject's skin potential, is calculated using the reference spectrum and the subject's sensitivity function. and The reference response, based on the change in skin potential of the reference color visioner, is calculated according to the reference spectrum and the sensitivity function of the reference color visioner.

17. A color evaluation method, performed by a color evaluation system having at least one processor, the color evaluation method comprising the following steps: Acquire baseline color information related to the baseline color, a subject model representing the subject's color recognition, and a reference model representing the reference colorimeter's color recognition; Based on the reference color information and the subject model, a subject response representing the subject's recognition of the reference color is calculated, and based on the reference color information and the reference model, a reference response representing the reference colorimeter's recognition of the reference color is calculated. and The difference between the subject's response and the reference response is calculated as the response difference.

18. A method for manufacturing a pigment, comprising the following steps: Acquire baseline color information related to the baseline color, a subject model representing the subject's color recognition, and a reference model representing the reference colorimeter's color recognition; Based on the reference color information and the subject model, a subject response representing the subject's recognition of the reference color is calculated, and based on the reference color information and the reference model, a reference response representing the reference colorimeter's recognition of the reference color is calculated. The difference between the subject's response and the reference response is calculated as the response difference; Based on the reference spectrum, which serves as the reference color, and the response difference, an adjusted spectrum is generated, which serves as the spectrum of a color whose response difference is smaller than the reference spectrum. and Pigments are manufactured by adjusting the spectrum as described.

19. A color evaluation program that causes a computer to perform the following steps: Acquire baseline color information related to the baseline color, a subject model representing the subject's color recognition, and a reference model representing the reference colorimeter's color recognition; Based on the reference color information and the subject model, a subject response representing the subject's recognition of the reference color is calculated; and based on the reference color information and the reference model, a reference response representing the reference colorimeter's recognition of the reference color is calculated; and The difference between the subject's response and the reference response is calculated as the response difference.

Images