Light source color preference degree evaluation method and device, equipment and storage medium

Abstract

The invention discloses a light source color preference degree evaluation method and device, equipment and a storage medium, and relates to the technical field of light source quality testing. The method comprises the following steps: acquiring a color temperature parameter of a test light source and spectral power distribution in a preset wavelength range, acquiring an associated reference light source based on the color temperature parameter, and calculating reference spectral power distribution; a color quality index is then calculated based on the plurality of test color samples according to the color temperature parameter, the spectral power distribution, and the reference spectral power distribution. Calculating a color gamut space area formed by the test color sample under the test light source according to the spectral power distribution, calculating a color resolution index according to the color gamut space area, and finally calculating a color preference index of the test light source according to the color quality index and the color resolution index. Therefore, the color preference index is calculated according to the color temperature parameter and the reference light source, the calculation process is associated with visual feelings such as visual discrimination, and a basis is provided for accurate evaluation or comparison of the color development quality of different color temperature test light sources through quantitative calculation of the color preference.

Description

Technical Field

[0001] The present invention relates to the technical field of light source quality testing, and in particular to a light source color preference evaluation method, device, equipment and storage medium. Background Art

[0002] With the rapid development of semiconductor lighting technology and the widespread adoption of the concept of human-centered lighting, the color rendering quality of light source products has become a core quality indicator. Whether it's dedicated lighting equipment or electronic device displays, consumers are placing increasingly high demands on the visual effects of light color rendering, expecting a more realistic, natural, and comfortable lighting experience.

[0003] Color preference is a key indicator for measuring the color rendering quality of light sources. Lighting color preference corresponds to the human eye's perception of the color appearance of illuminated objects. However, existing technology approaches for evaluating the color rendering quality of light sources primarily rely on reference illuminants at specific color temperatures. These methods fail to directly correlate with visual preferences and discrimination, making it difficult to accurately evaluate or compare the color rendering quality of light sources at different color temperatures. Further improvement and refinement are urgently needed. Summary of the Invention

[0004] In view of this, the present invention provides a light source color preference evaluation method, device, equipment and storage medium to solve the problem in the prior art that it is impossible to directly associate visual preferences and visual perceptions such as resolution, and it is difficult to accurately evaluate or compare the color rendering quality of light source devices with different color temperatures.

[0005] The technical solution adopted in the present invention is:

[0006] In a first aspect, the present invention provides a method for evaluating light source color preference, the method comprising:

[0007] Obtaining color temperature parameters of a test light source and spectral power distribution of the test light source within a preset wavelength range;

[0008] Acquire an associated reference light source based on the color temperature parameter, and calculate a reference spectral power distribution of the reference light source within the preset wavelength range;

[0009] Calculating a color quality index of the test light source based on a plurality of test color samples according to the color temperature parameter, the spectral power distribution, and the reference spectral power distribution;

[0010] Calculating the color gamut space area formed by a plurality of test color samples under the test light source according to the spectral power distribution, and calculating the color resolution index of the test light source according to the color gamut space area;

[0011] A color preference index of the test light source is calculated according to the color quality index and the color discrimination index.

[0012] In some embodiments, calculating the color quality index of the test light source based on a plurality of test color samples according to the color temperature parameter, the spectral power distribution, and the reference spectral power distribution includes:

[0013] Calculating the white point tristimulus values of the test light source and the tristimulus values of the test color sample under the test light source based on the spectral power distribution;

[0014] Calculating reference white point tristimulus values of the reference light source and reference tristimulus values of the test color sample under the reference light source based on the reference spectral power distribution;

[0015] Calculating the chromaticity coordinates of the test color sample under the test light source according to the tristimulus values and the white point tristimulus values, and calculating the reference chromaticity coordinates of the test color sample under the reference light source according to the reference tristimulus values and the reference white point tristimulus values;

[0016] Calculating a chromaticity difference and a coordinate difference of the test color sample according to the chromaticity coordinates and the reference chromaticity coordinates, and calculating a saturation factor of the test color sample based on the chromaticity difference and the coordinate difference;

[0017] A color temperature factor is calculated according to the color temperature parameter, and a color quality index of the test light source is calculated according to the color temperature factor and the saturation factor.

[0018] In some embodiments, before calculating the chromaticity coordinates of the test color sample under the test light source according to the tristimulus values and the white point tristimulus values, the method further includes:

[0019] Performing chromatic adaptation conversion on the tristimulus values to obtain a first conversion value, performing chromatic adaptation conversion on the white point tristimulus values to obtain a second conversion value, and performing chromatic adaptation conversion on the reference white point tristimulus values to obtain a third conversion value;

[0020] calculating a color value of the test color sample according to the first conversion value, the second conversion value, and the third conversion value;

[0021] The color values are converted according to a preset inverse conversion matrix to obtain new tristimulus values of the test color sample under the test light source.

[0022] In some embodiments, calculating the color gamut space area formed by the plurality of test color samples under the test light source according to the spectral power distribution, and calculating the color resolution index of the test light source according to the color gamut space area includes:

[0023] Calculating the test tristimulus values of 8 test color samples under the test light source according to the spectral power distribution;

[0024] Calculating the color space coordinates of the corresponding test color sample according to the test tristimulus values;

[0025] Obtaining the color gamut space area based on an octagon formed by the eight color space coordinates;

[0026] The color resolution index of the test light source is calculated according to the color gamut space area.

[0027] In some embodiments, after calculating the color preference index of the test light source according to the color quality index and the color discrimination index, the method further includes:

[0028] Calculating a reference color preference index of the reference light source based on the reference spectral power distribution;

[0029] The relative color preference index of the test light source is calculated according to the color preference index and the reference color preference index.

[0030] In some embodiments, acquiring an associated reference light source based on the color temperature parameter and calculating a reference spectral power distribution of the reference light source within the preset wavelength range includes:

[0031] If the color temperature parameter is less than a first color temperature threshold, the reference light source obtained is a Planckian illuminant, and a reference spectral power distribution of the Planckian illuminant is calculated according to the color temperature parameter and the preset wavelength range;

[0032] If the color temperature parameter is greater than or equal to the first color temperature threshold, the acquired reference light source is a daylight illuminator, and a reference spectral power distribution of the daylight illuminator is calculated according to the color temperature parameter and the preset wavelength range.

[0033] In some embodiments, calculating the reference spectral power distribution of the daylight-illuminated object according to the color temperature parameter and the preset wavelength range includes:

[0034] If the color temperature parameter is greater than the first color temperature threshold and the color temperature parameter is less than or equal to the second color temperature threshold, calculating the coordinate parameters of the daylight-illuminated object according to a first preset formula; wherein the second color temperature threshold is greater than the first color temperature threshold;

[0035] If the color temperature parameter is greater than the second color temperature threshold, calculating the coordinate parameters of the daylight-illuminated object according to a second preset formula;

[0036] A reference spectral power distribution of the daylight illumination object within the preset wavelength range is calculated according to the coordinate parameters.

[0037] In a second aspect, the present invention provides a light source color preference evaluation device, the device comprising:

[0038] A test light source acquisition module is used to obtain the color temperature parameters of the test light source and the spectral power distribution of the test light source within a preset wavelength range;

[0039] A reference light source acquisition module, configured to acquire an associated reference light source based on the color temperature parameter, and calculate a reference spectral power distribution of the reference light source within the preset wavelength range;

[0040] A color quality index module is configured to calculate the color quality index of the test light source based on a plurality of test color samples according to the color temperature parameter, the spectral power distribution, and the reference spectral power distribution;

[0041] A color resolution index module is configured to calculate the color gamut space area formed by a plurality of test color samples under the test light source according to the spectral power distribution, and calculate the color resolution index of the test light source according to the color gamut space area;

[0042] The color preference calculation module is configured to calculate the color preference index of the test light source according to the color quality index and the color resolution index.

[0043] In a third aspect, an embodiment of the present invention further provides an electronic device comprising: at least one processor, at least one memory, and computer program instructions stored in the memory, which, when executed by the processor, implement the method of the first aspect of the above-mentioned embodiment.

[0044] In a fourth aspect, an embodiment of the present invention further provides a storage medium having computer program instructions stored thereon, which implements the method of the first aspect of the above-mentioned embodiment when the computer program instructions are executed by a processor.

[0045] In summary, the beneficial effects of the present invention are as follows:

[0046] The present invention provides a method, apparatus, device, and storage medium for evaluating light source color preference. The method first obtains the color temperature parameters of a test light source and its spectral power distribution within a preset wavelength range. The standardized wavelength range effectively avoids large errors in the quantitative results of the test light source. Based on the color temperature parameters, an associated reference light source is obtained, and the spectral power distribution of the reference light source within the preset wavelength range is calculated. Reference light sources at different color temperatures can accurately quantify the color rendering quality of the test light source. Then, based on the color temperature parameters, spectral power distribution, and reference spectral power distribution, a color quality index of the test light source is calculated based on multiple test color samples. The color gamut space area formed by the multiple test color samples under the test light source is then calculated based on the spectral power distribution. The color resolution index of the test light source is then calculated based on the color gamut space area. Finally, a color preference index of the test light source is calculated based on the color quality index and the color resolution index. Thus, by obtaining the color temperature parameters of the test light source and its spectral power distribution within a preset range, the color preference index is calculated based on the color temperature parameters and the associated reference light source. This calculation process integrates visual perception, such as visual resolution, and provides a basis for accurately evaluating or comparing the color rendering quality of test light sources with different color temperatures through the quantitative calculation of color preference. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work, and these are all within the scope of protection of the present invention.

[0048] Figure 1 Schematic diagram of the process of the light source color preference evaluation method in Example 1 of the present invention;

[0049] Figure 2 yes Figure 1 Flow diagram of step S102;

[0050] Figure 3 yes Figure 1 Flow diagram of step S103;

[0051] Figure 4 yes Figure 1 Flow chart of step S104;

[0052] Figure 5 yes Figure 1 Flowchart after step S105;

[0053] Figure 6 This is a structural block diagram of a light source color preference evaluation device in Example 2 of the present invention;

[0054] Figure 7This is a schematic diagram of the structure of an electronic device in Example 3 of the present invention. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. It should be noted that, in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. In the description of the present invention, it should be understood that the orientation or position relationship indicated by the terms "center", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like is based on the orientation or position relationship shown in the drawings, and is only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as limiting the present invention. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further limitations, elements defined by the phrase "comprising..." do not preclude the presence of additional identical elements in the process, method, article, or apparatus comprising the elements. The embodiments of the present invention and the features thereof may be combined with each other if there is no conflict, and all are within the scope of protection of the present invention.

[0056] According to official documents from the International Commission on Illumination (CIE), color rendering and color rendition quality are two different concepts. In the CIE's International Lighting Vocabulary (ILV, 2nd Edition, CIE S 017 / E:2020), color rendering is defined as "the effect of an illuminant on the perceived color of an object, produced by conscious or subconscious comparison with the perceived color of the object under a reference illuminant." However, the CIE also defines "color rendition quality" in its technical report CIE 224:2017 as "the effect of an illuminant on the perceived color of an object." Compared to color rendering, the definition of color rendition quality is independent of the reference illuminant, and fewer restrictions mean a broader meaning.

[0057] With the rapid development of semiconductor lighting technology and the widespread adoption of the concept of human-centered lighting, the color rendering quality of light source products has become one of the core indicators for measuring their quality. Whether it's dedicated lighting equipment or displays for electronic devices, consumers are placing increasingly high demands on the visual effects of light color rendering from light source products, hoping to obtain a more realistic, natural, and comfortable lighting visual experience. Among them, color preference is one of the key indicators for measuring the color rendering quality of light source equipment. Light color preference corresponds to the human eye's perception of the color appearance of the illuminated object. However, in the existing technical system, the evaluation method for the color rendering quality of light source equipment mainly relies on a reference illuminant at a specific color temperature. It cannot directly correlate with visual preferences and discrimination, making it difficult to accurately evaluate or compare the color rendering quality of light source equipment with different color temperatures. Further improvement and perfection is urgently needed.

[0058] Based on this, an embodiment of the present invention provides a method, device, electronic device and storage medium for evaluating the color preference of a light source. By obtaining the color temperature parameters of a test light source and the spectral power distribution within a preset range, a color preference index is calculated based on the color temperature parameters and the associated reference light source. The calculation process is associated with visual perceptions such as visual resolution. By quantifying the color preference, a basis is provided for accurate evaluation or comparison of the color rendering quality of test light sources with different color temperatures, which is specifically illustrated by the following embodiments.

[0059] Example 1

[0060] See Figure 1 , Figure 1This is an optional flow chart of the light source color preference evaluation method provided by an embodiment of the present invention. Figure 1 The method may include but is not limited to steps S101 to S105. Figure 1 The order of step S101 to step S105 is not specifically limited, and the order of steps can be adjusted or some steps can be reduced or added according to actual needs.

[0061] Step S101 : obtaining the color temperature parameters of a test light source and the spectral power distribution of the test light source within a preset wavelength range.

[0062] Color temperature is a key parameter in testing light sources, used to describe the perceived color of the light emitted by the source. In the GB / T7922 standard, color temperature is typically measured using the correlated color temperature (CCT), measured in Kelvin (K). In some embodiments, the CCT of the test light source can be calculated using the method described in GB / T 7922 as the color temperature parameter. Lower CCT values indicate warmer light, while higher CCT values indicate cooler light.

[0063] In some embodiments, during the calculation of the color quality index, the spectral power distribution S of the test light source within the preset wavelength range is obtained. t The preset wavelength range can be set to 380nm to 780nm, and a sufficiently fine sampling interval can be set for spectral sampling, for example, 5nm or less, to ensure comprehensive and accurate calculations. If the spectral power distribution (SPD) data of the test light source is missing within the preset wavelength range, for example, the data only covers 400nm to 700nm, the SPD values corresponding to the missing wavelengths are supplemented to zero. This ensures that subsequent calculations can be performed for the preset wavelength range, avoiding calculation deviations caused by incomplete data.

[0064] In some embodiments, during the calculation of the color discrimination index based on the absolute color gamut, the preset wavelength range of the test light source can be set to 400 nm to 700 nm, and a sampling interval of 10 nm or less can be set to obtain the corresponding spectral power distribution. It will be appreciated that the preset wavelength range and / or sampling interval for calculating different indices can be set by those skilled in the art based on actual circumstances, and this embodiment does not impose any limitations thereto.

[0065] Step S102 : acquiring an associated reference light source based on the color temperature parameter, and calculating a reference spectral power distribution of the reference light source within a preset wavelength range.

[0066] In some embodiments, the reference light source can be a Planckian radiator, a daylight source, or a combination of the two. Test light sources with different color temperature parameters may correspond to different reference light sources. This helps achieve desired lighting effects in practical applications, such as simulating natural light during a specific time period or creating a specific atmosphere. Based on the color temperature parameters of the test light source, an associated reference light source is obtained, and the reference spectral power distribution of this reference light source within the same preset wavelength range is calculated, providing a benchmark for subsequent quantitative evaluation calculations.

[0067] Step S103 : calculating the color quality index of the test light source based on a plurality of test color samples according to the color temperature parameter, the spectral power distribution and the reference spectral power distribution.

[0068] The General Colour Quality Scale (Qa) is a comprehensive measure of the degree to which the color appearance of a test light source (such as a lamp or electronic device display) when illuminating a specified test color sample is consistent with the color appearance of the same test color sample illuminated by a reference light source. The color quality index comprehensively reflects the color accuracy, saturation, and other performance of the test light source when presenting these test color samples. Therefore, the color quality index can not only be used to evaluate the color quality of different test light sources, but also provide a basis for lighting design, product development, and the formulation of lighting standards.

[0069] In some embodiments, the plurality of test color samples are 15 Munsell samples. For example, the 15 Munsell samples (hue, lightness, and chroma) are: 7.5P 4 / 10, 10PB 4 / 10, 5PB 4 / 12, 7.5B 5 / 10, 10BG 6 / 8, 2.5BG 6 / 10, 2.5G 6 / 12, 7.5GY 7 / 10, 2.5GY 8 / 10, 5Y 8.5 / 12, 10YR 7 / 12, 5YR 7 / 12, 10R 6 / 12, 5R 4 / 14, and 7.5RP 4 / 12. The color quality index of the test light source is calculated based on the 15 Munsell samples according to the color temperature parameter, the spectral power distribution, and the reference spectral power distribution.

[0070] Step S104 , calculating the color gamut space area formed by the plurality of test color samples under the test light source according to the spectral power distribution, and calculating the color resolution index of the test light source according to the color gamut space area.

[0071] In some embodiments, the color discrimination index (CDI) is a parameter that characterizes the color resolution under the illumination of a test light source, and can be calculated from the color gamut space area constructed by 8 test color samples under the test light source and the reference light source. Specifically, the color gamut space area formed by multiple test color samples under the test light source is calculated based on the spectral power distribution, and the color discrimination index of the test light source is calculated based on the color gamut space area. It can be understood that the higher the color discrimination index, the stronger the color discrimination ability of the test light source under illumination, that is, it can better present and distinguish different colors. Therefore, the color discrimination index has a wide range of applications in lighting design, product development, color science and other fields, and can help designers and engineers optimize the color performance of lighting products to meet the needs of specific application scenarios.

[0072] Step S105 : calculating the color preference index of the test light source according to the color quality index and the color discrimination index.

[0073] In some embodiments, a color preference index of a test light source is calculated based on the color quality index and color resolution index to evaluate the overall color rendering performance of the test light source. The color preference index is calculated based on the color temperature parameters and the associated reference light source. This calculation incorporates visual perceptions such as color resolution. This quantitative calculation of color preference provides a basis for accurate evaluation or comparison of the color rendering quality of test light sources with different color temperatures.

[0074] Reference Figure 2 As shown, in some embodiments of the present application, the above step S102: obtaining an associated reference light source based on the color temperature parameter and calculating the reference spectral power distribution of the reference light source within a preset wavelength range may also include but is not limited to the following steps S201 to S202.

[0075] In step S201 , if the color temperature parameter is less than a first color temperature threshold, the reference light source obtained is a Planckian illuminant, and a reference spectral power distribution of the Planckian illuminant is calculated according to the color temperature parameter and a preset wavelength range.

[0076] In some embodiments, the first color temperature threshold is set to 5000K. When the color temperature parameter T is less than 5000K, the reference light source obtained is a Planck illuminator, also known as a Planck radiator, which is an idealized light source model based on Planck's radiation law. Planck's radiation law describes the spectral distribution of radiation emitted by a blackbody (an object that completely absorbs incident radiation of any wavelength and has maximum emissivity) at different temperatures.

[0077] Furthermore, the reference spectral power distribution SPD of the Planckian illuminant is calculated according to the preset wavelength range and color temperature parameters with reference to formulas (1)-(2), denoted as SrP :

[0078]

[0079] Among them, λ is the wavelength (unit: nm), T is the color temperature parameter. Formulas (1) and (2) are based on Planck's law and are used to calculate the spectral power distribution of blackbody radiation, which are basic formulas in physics. The SPD processing normalized to 560 nm conforms to conventional colorimetry applications.

[0080] Step S202, if the color temperature parameter is greater than or equal to the first color temperature threshold, the reference light source obtained is a daylight illuminant, and the reference spectral power distribution of the daylight illuminant is calculated according to the color temperature parameter and the preset wavelength range.

[0081] In some embodiments, when the color temperature parameter T > 5000K, the reference light source obtained is a daylight illuminant. A daylight illuminant refers to an illuminant having the same or approximately the same relative spectral power distribution as daylight at a certain time phase, and is a light source model that simulates natural daylight. Specifically, calculating the reference spectral power distribution of the daylight illuminant within the preset wavelength range may include, but is not limited to, the following steps S301 to S303.

[0082] Step S301, if the color temperature parameter is greater than the first color temperature threshold and less than or equal to the second color temperature threshold, calculate the coordinate parameters of the daylight illuminant according to the first preset formula.

[0083] Coordinate parameters refer to representing a color using two coordinate values (x and y), and these two coordinate values represent the position of the color in the visible spectrum. In some embodiments, the second color temperature threshold is greater than the first color temperature threshold. Assuming the third color temperature threshold is 7000K, when the color temperature parameter is greater than the first color temperature threshold and less than or equal to the second color temperature threshold, that is, 5000K < T <= 7000K, calculate the coordinate parameters (x D , y D ) of the daylight illuminant according to the following first preset formula:

[0084]

[0085] Among them, T is the color temperature parameter.

[0086] Step S302, if the color temperature parameter is greater than the second color temperature threshold, calculate the coordinate parameters of the daylight illuminant according to the second preset formula.

[0087] In some embodiments, when the color temperature parameter is greater than the second color temperature threshold, that is, T > 7000K, calculate the coordinate parameters (x D , y D ) of the daylight illuminant according to the following second preset formula:

[0088]

[0089] Among them, T is the color temperature parameter.

[0090] Step S303 : calculating the reference spectral power distribution of the daylight illumination object within a preset wavelength range according to the coordinate parameters.

[0091] In some embodiments, referring to formulas (3)-(5), according to the preset wavelength range and chromaticity coordinates (x D ,y D ) Calculate the reference spectral power distribution SPD of the daylight illumination object in the preset wavelength range, denoted as S rD :

[0092] S rD (λ)=S0(λ)+M1S1(λ)+M2S2(λ)(3)

[0093]

[0094] Where λ is the wavelength (in nm), S0(λ), S1(λ), and S2(λ) are basis functions with a wavelength interval of 5 nm, which is the CIE standard method.

[0095] Reference Figure 3 As shown, in some embodiments of the present application, the above step S103: calculating the color quality index of the test light source based on multiple test color samples according to the color temperature parameters, the spectral power distribution and the reference spectral power distribution, may also include but is not limited to the following steps S401 to S405.

[0096] Step S401 : calculating the white point tristimulus values of the test light source and the tristimulus values of the test color sample under the test light source based on the spectral power distribution.

[0097] Tristimulus values are an important concept in color science, used to quantitatively describe color. They represent the degree of stimulation of the three primary colors that causes the human retina to perceive a certain color. In the XYZ color space specified by the CIE (International Commission on Illumination), tristimulus values are represented by X, Y, and Z, corresponding to the stimulation of the three primary colors red, green, and blue, respectively. From this, the tristimulus values can be calculated from the spectral power distribution to represent the color perception properties of the test color sample under the test light source.

[0098] In some embodiments, referring to formula (6), the white point tristimulus value X of the test light source is calculated based on the spectral power distribution by using the CIE standard color matching function. w,t , Y w,t , Z w,t :

[0099]

[0100] Among them, S t (λ) is the spectral power distribution of the test light source, is the CIE1931 color matching function.

[0101] In some embodiments, the spectral reflectance factor M of a plurality of test color samples (eg, 15 Munsell samples) is obtained. i (λ) (i corresponds to an integer from 1 to 15). Specifically, referring to formula (7), the tristimulus value X of each test color sample under the test light source (subscript t) is calculated based on the CIE color matching function, spectral reflectance factor, preset wavelength range and spectral power distribution. i,t , Y i,t , Z i,t :

[0102]

[0103] Where i represents the i-th test color sample, S t (λ) is the spectral power distribution of the test light source, is the CIE1931 color matching function.

[0104] Step S402 : calculating reference white point tristimulus values of the reference light source and reference tristimulus values of the test color sample under the reference light source based on the reference spectral power distribution.

[0105] In some embodiments, referring to formula (8), the reference white point tristimulus value X of the reference light source is calculated based on the reference spectral power distribution. w,r , Y w,r , Z w,r :

[0106]

[0107] Among them, S r (λ) is the reference spectral power distribution of the reference light source, is the CIE1931 color matching function.

[0108] In some embodiments, referring to formula (9), the reference tristimulus value X of each test color sample under the reference light source (subscript r) is calculated based on the CIE color matching function, the spectral reflectance factor, the preset wavelength range and the reference spectral power distribution. i,r , Y i,r , Z i,r :

[0109]

[0110] Among them, i represents the i-th test color sample, S r(λ) is the reference spectral power distribution of the reference light source, is the CIE1931 color matching function.

[0111] In some embodiments of the present application, before step S403, the following steps may also be included but not limited to steps S501 to S503.

[0112] Step S501 , performing chromatic adaptation conversion on the tristimulus values to obtain first conversion values, performing chromatic adaptation conversion on the white point tristimulus values to obtain second conversion values, and performing chromatic adaptation conversion on the reference white point tristimulus values to obtain third conversion values.

[0113] In some embodiments, referring to formulas (10)-(11), the tristimulus values (X i,t , Y i,t , Z i,t ), white point tristimulus values (X w,t , Y w,t , Z w,t ), reference white point tristimulus values (X w,r , Y w,r , Z w,r ) are respectively subjected to color adaptation conversion (the subscript is omitted in the formula), and the corresponding first conversion value (R i,t , G i,t , B i,t ), the second conversion value (R w,t , G w,t , B w,t ) and the third conversion value (R w,r , G w,r , B w,r ):

[0114]

[0115] Where M is the chromatic adaptation transformation matrix.

[0116] Step S502 : Calculate the color value of the test color sample according to the first conversion value, the second conversion value, and the third conversion value.

[0117] In some embodiments, referring to formulas (12)-(15), the corresponding color value (R i,t,c , G i,t,c , B i,t,c ):

[0118]

[0119] Where i represents the i-th test color sample.

[0120] Step S503 : converting the color value according to a preset inverse conversion matrix to obtain new tristimulus values of the test color sample under the test light source.

[0121] In some embodiments, referring to formulas (16)-(17), the color values are converted according to a preset inverse conversion matrix to obtain new tristimulus values (X i,t,c , Y i,t,c , Z i,t,c ):

[0122]

[0123] Where i represents the i-th test color sample.

[0124] Therefore, through chromatic adaptation conversion and the introduction of a preset inverse conversion matrix, it helps to eliminate the differences in observers' color perception under different lighting conditions, thereby improving the accuracy of color evaluation, which is particularly important for application scenarios that require high-precision color matching.

[0125] Step S403 , calculating the chromaticity coordinates of the test color sample under the test light source according to the tristimulus values and the white point tristimulus values, and calculating the reference chromaticity coordinates of the test color sample under the reference light source according to the reference tristimulus values and the reference white point tristimulus values.

[0126] In some embodiments, the chromaticity coordinates are CIE1976 L*a*b* coordinates. Specifically, referring to formulas (18)-(20), the chromaticity coordinates of the test color sample under the test light source are calculated based on the tristimulus values and the white point tristimulus values.

[0127]

[0128] Similarly, referring to formulas (21)-(23), the reference chromaticity coordinates of the test color sample under the reference light source are calculated based on the reference tristimulus values and the reference white point tristimulus values.

[0129]

[0130] Step S404 : calculating the chromaticity difference and the coordinate difference of the test color sample according to the chromaticity coordinates and the reference chromaticity coordinates, and calculating the saturation factor of the test color sample based on the chromaticity difference and the coordinate difference.

[0131] Referring to formula (24), calculate the chromaticity of the i-th test color sample under the test light source according to the chromaticity coordinates:

[0132]

[0133] Referring to formula (25), calculate the reference chromaticity of the i-th test color sample under the reference light source according to the reference chromaticity coordinates:

[0134]

[0135] Referring to formula (26), calculate the chromaticity difference of the i-th test color sample based on the chromaticity and the reference chromaticity

[0136]

[0137] Referring to formula (27), the coordinate difference of the i-th test color sample is calculated based on the chromaticity coordinates and the reference chromaticity coordinates:

[0138]

[0139] Referring to formulas (28)-(29), the saturation factor of the i-th test color sample is calculated based on the chromaticity difference and coordinate difference

[0140]

[0141] In some embodiments, step S404 further includes:

[0142] S4041. Obtaining the hue angle of each test color sample according to the chromaticity coordinates;

[0143] Specifically, the hue angle is a parameter that describes the color orientation using polar coordinates. It is often calculated in CIELab space using the formula H = atan2(b*, a*) (the result is converted to 0–360°); if working in u′v′ space, the method is similar. For example, a dark blue sample with a* = 0 and b* = -40 calculates to H ≈ 270°, while an orange-red sample with a* = 25 and b* = 25 is approximately 45°. The purpose of this step is to quantify color tonality at a single angle, establishing coordinate keys for subsequent table lookups to obtain human eye sensitivity weights; only by first projecting the two-dimensional chromaticity onto a one-dimensional angle can the color region be quickly located.

[0144] During implementation, the system first converts XYZ to the selected color space, then calls the inverse tangent function to obtain H. If H < 0, a 360° normalization is applied. The hue angle compresses complex color representations into a scalar that is easily partitioned. Subsequent algorithms require only a single integer division to determine the color bin index, significantly reducing the computational load for embedded or MCU implementations.

[0145] S4042. Determine a color region index of the test color sample in a preset color region group according to the hue angle, wherein the preset color region group includes a preset number of color regions, and different color regions have different color region indexes;

[0146] Specifically, the color zone index R is the discretized number of the 0–360° color wheel. For example, using the CIE 16 zones, each 22.5° zone is divided into segments: 0°–22.5° is R1, 22.5°–45° is R2, and so on up to R16. Custom 8 zones can also be created based on application requirements, or a skin-sensitive zone can be introduced. This step aims to map continuous angles to a finite set, enabling subsequent steps to directly look up visual weights for different color categories, leveraging the inherent uneven distribution of hue sensitivity. After zone indexing, the algorithm develops a scalable classification framework. The hardware requires only simple arithmetic operations and a small lookup table structure to dynamically support any number of zones from 8 to 32, ensuring flexible software upgrades without hardware changes.

[0147] In one embodiment, the preset color areas are divided by the following method:

[0148] The CIELab color wheel is divided into different widths based on the human eye's subjective perceptible differences. The MacAdam ellipse represents the human eye's perception of color differences at different hues and brightnesses in the CIE1931 xy plane (or the converted u′v′ / Lab plane). Larger ellipses indicate a more tolerant color region; smaller ellipses indicate a more sensitive region.

[0149] Specifically, the MacAdam ellipse major and minor axis data are superimposed on the 360° hue angle, and the just noticeable difference corresponding to each 1° of color is calculated. The entire hue circle is then divided into N segments using an integral method, ensuring that the cumulative just noticeable difference within each segment is approximately the same. This scheme exploits the fact that the subjective just noticeable difference at each hue angle is not equal to the average. It divides the 0–360° color wheel into segments of variable width, ensuring that the cumulative just noticeable difference within each segment is essentially the same. The sensitive areas (skin tone, yellow-green) are divided more finely, while the tolerance areas (blue-violet) are merged more widely, thus aligning the granularity of the segments with human perception.

[0150] In one specific embodiment, the skin-tone and yellow-green areas are subdivided into narrow angles of 10–15°, while the blue-violet area is merged into a wider angle of 30–40°. This resulting index table allows for higher resolution in subsequent weighted lookup tables for areas where the eye is particularly sensitive. This avoids the overly coarse assessment of sensitive areas by simple conformal methods, while significantly improving the consistency of color difference evaluation with real-world perception without requiring additional hardware.

[0151] S4043. Obtaining a human eye perception sensitivity weight for each test color sample according to the color region index and a preset human eye viewing function;

[0152] The human eye's perceptual sensitivity weight, W(R), describes the subjectively perceptible difference in the same physical color difference between different hues. For example, statistical experiments show that a ΔE=3 in the blue-violet region is often unnoticeable, so W=0.8 can be used; whereas a ΔE=3 in skin tone or grass green is highly perceptible, W=1.3–1.5 can be used. The weight is typically fitted based on a Just-Noticeable-Difference (JND) curve from 80–100 observers. Its purpose is to quantify the perceptible difference threshold of the human eye's "eccentricity" into a numerical coefficient, thereby amplifying or converging the error contribution during the color difference scoring stage and making the final index more consistent with subjective perception. By incorporating the perceptual sensitivity weight, the evaluation system automatically mitigates overly strict scoring caused by a high tolerance for blue areas and emphasizes the importance of skin tone distortion in display lighting, significantly improving the correlation between the CQI and real-world user preferences.

[0153] S4044: performing a weighted operation based on the visual deviation tolerance weight and the chromaticity difference to obtain a weighted chromaticity difference;

[0154] Specifically, the original chromaticity difference only reflects the objective color difference; this step multiplies it by a weight. When the visual deviation tolerance weight is less than 1, the weighted chromaticity difference is compressed; when the visual deviation tolerance weight is greater than 1, the weighted chromaticity difference is amplified. The goal is to truly inject the "human eye tolerance / sensitivity coefficient" calculated in the previous step into the color difference calculation process and convert it into a measurable perceptual color difference. Compared with a one-size-fits-all threshold clipping, the weighted multiplication process maintains continuity and differentiability, avoiding boundary jumps; and it does not increase additional storage, only a single floating-point multiplication, making it suitable for real-time evaluation scenarios.

[0155] S4045. Calculate the saturation factor of the test color sample according to the weighted chromaticity difference and the coordinate difference.

[0156] The purpose of this step is to convert the color error of the corrected visual sensitivity into an intuitive and easy-to-compare score, in preparation for the subsequent summary and generation of the "color quality index". When implementing, exponential decay can be used: first select the empirical coefficient k (usually around 0.09) to control the sensitivity, and then execute the above calculation formula for each test color sample; if you want to consider the coordinate difference at the same time, you can linearly combine the two items according to the weights and then enter them into the formula. The saturation factors of all test color samples are averaged or weighted averaged to obtain the color quality index of the light source. This approach injects the subjective perception information of the human eye into the scoring system without changing the existing evaluation framework. It can significantly improve the consistency between the evaluation results and the actual perception, and keep the algorithm continuous and smooth, and easy to embed.

[0157] In some embodiments, referring to formula (30), the root mean square ΔE is further calculated based on the saturation factors of all test samples (e.g., 15 Munsell samples). rms :

[0158]

[0159] Step S405 : calculating a color temperature factor according to the color temperature parameter, and calculating a color quality index of the test light source according to the color temperature factor and the saturation factor.

[0160] In some embodiments, referring to formulas (31)-(32), the color temperature factor M is calculated according to the color temperature parameter CCT :

[0161] M CCT =T 3 (9.2672×10 -11 )-T 2 (8.3959×10 -7 )+T(0.00255)-1.612(T<3500K)(31)

[0162] M CCT =1(T≥3500K) (32)

[0163] In some embodiments, referring to formula (33), the root mean square of the saturation factor is proportionally converted to obtain the intermediate color quality index Q a,rms :

[0164] Q a,rms =100-3.1×ΔE rms (33)

[0165] Furthermore, referring to formula (34), the original color quality index Q is obtained based on the intermediate color quality index a,0-100 :

[0166]

[0167] Finally, referring to formula (35), the color quality index Q of the test light source is calculated based on the color temperature factor and the original color quality index. a :

[0168] Q a =M CCT Q a,0-100 (35)

[0169] Based on the color temperature parameters, spectral power distribution, and reference spectral power distribution, the color quality index of the test light source is calculated using multiple test color samples. This includes calculating the tristimulus values, chromaticity coordinates, chromaticity difference, coordinate difference, saturation factor, and color temperature factor of the test color samples under the test light source and reference light source, and ultimately integrating this information to derive the color quality index. This allows for a more accurate assessment of the color quality of the test light source. This refined assessment helps identify potential problems with the light source in specific color regions, thereby guiding the improvement and optimization of the light source. Furthermore, using the reference light source and reference spectral power distribution as a benchmark allows the color quality of the test light source to be compared with a standard or ideal state, helping to ensure the consistency and reliability of the light source and meet the needs of different application scenarios. In the embodiments of the present application, the calculation of the color quality index not only takes into account the color temperature parameters, but also combines multiple factors such as spectral power distribution, chromaticity coordinates, and chromaticity difference, thereby enabling a more comprehensive and objective assessment of the color quality of the light source. Furthermore, the concepts of chromatic adaptation transformation and a preset inverse transformation matrix are further introduced to more accurately evaluate color performance under the test light source.

[0170] Reference Figure 4 As shown, in some embodiments of the present application, the above-mentioned step S104: calculating the color gamut space area formed by multiple test color samples under the test light source according to the spectral power distribution, and calculating the color resolution index of the test light source according to the color gamut space area, may also include but is not limited to the following steps S601 to S604.

[0171] Step S601 : calculating the test tristimulus values of 8 test color samples under the test light source according to the spectral power distribution.

[0172] In some embodiments, the spectral reflectance factors R of 8 test color samples are obtained. i (λ) (i is 1 to 8), refer to formula (36), calculate the test tristimulus value (X) of the i-th test color sample under the test light source according to the spectral power distribution and spectral reflectance factor i , Y i , Z i ):

[0173]

[0174] Among them, S t (λ) is the spectral power distribution, is the CIE1931 color matching function.

[0175] Step S602: Calculate the color space coordinates of the corresponding test color sample according to the test tristimulus values.

[0176] In some embodiments, the color space coordinates are coordinates in the CIE1976u'v' color space. Specifically, referring to formulas (37)-(38), the color space coordinates (u', v') of the corresponding test color sample are calculated according to the test tristimulus values:

[0177]

[0178] Where i represents the i-th test color sample.

[0179] Step S603 : obtaining the color gamut space area based on the octagon formed by the 8 color space coordinates.

[0180] In some embodiments, referring to formula (39), the color gamut space area GA is obtained based on the octagon composed of 8 color space coordinates. t :

[0181]

[0182] Where i represents the i-th color space coordinate.

[0183] In one embodiment, based on the visual deviation tolerance weight obtained in step S4043, step S603 further includes:

[0184] S6031. Perform radial scaling on the corresponding color space coordinates according to the visual deviation tolerance weight of each test color sample;

[0185] Specifically, radial scaling means first taking the white point coordinates of the light source as the origin, calculating the vector from the current color sample to the white point, and then lengthening or shortening the vector in proportion to the weight. This step is to convert the physical color difference into a perceptual color difference that is more consistent with the subjective resolution of the human eye before constructing the color gamut area. When W>1, the vector is amplified, indicating that the human eye is more likely to perceive color changes in this color area; when W<1, the opposite is true, and the vector is reduced to reduce the contribution of the insensitive area to the area. This scaling stretches the "colors that are easy for the human eye to distinguish" outward and compresses the "colors that are difficult to distinguish" inward, so that the subsequent octagonal area more realistically represents the perceptible color gamut, avoiding the problem of simple physical coordinates causing the blue-purple area to be inflated and the contribution of the skin color area to be underestimated.

[0186] S6032. Obtain the color gamut space area according to the octagon formed by the scaled color space coordinates;

[0187] Specifically, this step aims to further integrate the information of the visual weight that has been embedded in the previous link into a single area indicator, providing a basis for the subsequent conversion of the color resolution index. The larger the area, the wider the spacing between the reference color samples and the richer the layers on the scale discernible to the human eye. Keep the original octagonal lines and processing flow unchanged, only replace the vertex coordinates, the calculation complexity remains unchanged, and no additional matrix operations are required. The area after the introduction of visual weights directly reflects the perceived color gamut, so that the color resolution index not only considers the physical color difference, but also simultaneously reflects the difference in the human eye's ability to resolve different colors, providing a more reliable and more subjective evaluation basis for the color optimization of lamps or displays. Step S604, calculate the color resolution index of the test light source based on the area of the color gamut space.

[0188] In some embodiments, referring to formula (40), the color discrimination index CDI of the test light source is calculated according to the color gamut space area:

[0189]

[0190] Wherein, C is the correction index, C=0.005.

[0191] In some embodiments, referring to formula (41), the color preference index MCPI of the test light source is calculated based on the color quality index and the color resolution index:

[0192] MCPI=0.62Q a +0.38CDI (41)

[0193] By calculating the color gamut area, the color resolution capability of the test light source can be quantitatively assessed. This method is independent of specific light source type or color standard, making it widely applicable. Finally, the color preference index is calculated by combining the color quality index and the color resolution index. This calculation process incorporates visual experiences such as color resolution. The quantitative calculation of color preference provides a basis for accurate evaluation or comparison of the color rendering quality of test light sources with different color temperatures.

[0194] Reference Figure 5 As shown, in some embodiments of the present application, after the above step S105: after calculating the color preference index of the test light source according to the color quality index and the color resolution index, the following steps may also be included but not limited to steps S701 to S702.

[0195] Step S701 : calculating a reference color preference index of a reference light source based on a reference spectral power distribution.

[0196] In some embodiments, similarly, referring to steps S401 to S405, steps S501 to S503, and steps S601 to S604 above, the reference color preference index MCPI of the reference light source is calculated based on the reference spectral power distribution. ref .

[0197] Step S702 : Calculate the relative color preference index of the test light source according to the color preference index and the reference color preference index.

[0198] In some embodiments, referring to formula (42), the relative color preference index MCPI of the test light source is calculated based on the color preference index and the reference color preference index: r :

[0199]

[0200] In some embodiments, depending on the use scenario of the test light source and the comparison object, it is advisable to select the relative color preference index or the absolute color preference index in this document to evaluate the color rendering quality of the light source. Specifically, for comparison across color temperatures, it is advisable to use the absolute index, namely the absolute color preference index MCPI; for comparison within the same color temperature, it is advisable to use the relative index, namely the relative color preference index MCPI. r .

[0201] In some embodiments, the relative color preference index (MCPI) can be used to grade the test light sources under the same color temperature conditions. Specifically, in the color temperature range [2700, 6500] (unit K), the relative color preference index (MCPI) is r If the value is greater than or equal to 100%, the light source of the test light source is judged to be Class A; the relative color preference index MCPI r If the value is greater than or equal to 97% but less than 100%, the light source of the test light source is judged to be Class B; the relative color preference index MCPI r If the value is greater than or equal to 85% but less than 97%, the light source of the test light source is judged to be Class C; the relative color preference index MCPI r If the value is less than 85%, the light source classification of the test light source is determined to be D.

[0202] Therefore, by obtaining the color temperature parameters of the test light source to be evaluated and the spectral power distribution within a preset range, the color preference index is calculated based on the color temperature parameters and the associated reference light source. The calculation process is associated with visual perceptions such as visual resolution. The quantitative calculation of color preference provides a basis for accurate evaluation or comparison of the color rendering quality of test light sources with different color temperatures.

[0203] Example 2

[0204] See Figure 6 Embodiment 2 of the present invention further provides a light source color preference evaluation device, the device comprising:

[0205] A test light source acquisition module is used to obtain the color temperature parameters of the test light source and the spectral power distribution of the test light source within a preset wavelength range;

[0206] A reference light source acquisition module is used to acquire an associated reference light source based on a color temperature parameter and calculate a reference spectral power distribution of the reference light source within a preset wavelength range;

[0207] A color quality index module is used to calculate the color quality index of the test light source based on multiple test color samples according to color temperature parameters, spectral power distribution and reference spectral power distribution;

[0208] A color resolution index module is used to calculate the color gamut space area formed by multiple test color samples under the test light source based on the spectral power distribution, and calculate the color resolution index of the test light source based on the color gamut space area;

[0209] The color preference calculation module is used to calculate the color preference index of the test light source according to the color quality index and the color resolution index.

[0210] The specific implementation of the light source color preference evaluation device of this embodiment is basically the same as the specific implementation of the light source color preference evaluation method described above, and will not be described in detail here.

[0211] Example 3

[0212] In addition, combined Figure 1 The light source color preference evaluation method of the first embodiment of the present invention can be implemented by an electronic device. Figure 7 A schematic diagram of the hardware structure of an electronic device provided in Example 3 of the present invention is shown.

[0213] An electronic device may include a processor and a memory storing computer program instructions.

[0214] Specifically, the processor may include a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits for implementing the embodiments of the present invention.

[0215] The memory may include a large capacity memory for data or instructions. By way of example and not limitation, the memory may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory may include a removable or non-removable (or fixed) medium. Where appropriate, the memory may be inside or outside the data processing device. In a specific embodiment, the memory is a non-volatile solid-state memory. In a specific embodiment, the memory includes a read-only memory (ROM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM), or a flash memory, or a combination of two or more of these.

[0216] The processor implements any one of the light source color preference evaluation methods in the above embodiments by reading and executing computer program instructions stored in the memory.

[0217] In one example, the electronic device may further include a communication interface and a bus. Figure 7 As shown, the processor, memory, and communication interface are connected via a bus and communicate with each other.

[0218] The communication interface is mainly used to implement communication between the modules, devices, units and / or equipment in the embodiments of the present invention.

[0219] Bus comprises hardware, software or both, couples the parts of described equipment together.For example, and not limitation, bus can comprise accelerated graphics port (AGP) or other graphics bus, enhanced industry standard architecture (EISA) bus, front side bus (FSB), hypertransport (HT) interconnection, industry standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations.In suitable cases, bus can comprise one or more buses.Although the embodiment of the present invention describes and shows specific bus, the present invention considers any suitable bus or interconnection.

[0220] Example 4

[0221] In addition, in conjunction with the light source color preference evaluation method in the first embodiment, the fourth embodiment of the present invention may also be implemented by providing a computer-readable storage medium. The computer-readable storage medium stores computer program instructions; when executed by a processor, the computer program instructions implement any of the light source color preference evaluation methods in the above embodiments.

[0222] In summary, the embodiments of the present invention provide a method, apparatus, device, and storage medium for evaluating light source color preference.

[0223] It should be understood that the present invention is not limited to the specific configurations and processes described above and illustrated in the figures. For the sake of brevity, a detailed description of known methods is omitted. In the above embodiments, several specific steps are described and illustrated as examples. However, the method of the present invention is not limited to the specific steps described and illustrated. Those skilled in the art may make various changes, modifications, and additions, or change the order of the steps after understanding the spirit of the present invention.

[0224] The functional blocks shown in the above-described block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in unit, a function card or the like. When implemented in software, the elements of the present invention are programs or code segments that are used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROMs, flash memories, erasable ROMs (EROMs), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0225] It should also be noted that the exemplary embodiments described herein describe methods or systems based on a series of steps or devices. However, the present invention is not limited to the order of the steps described above. In other words, the steps may be performed in the order described in the embodiments, or in a different order, or several steps may be performed simultaneously.

[0226] The above description is only a specific embodiment of the present invention. Those skilled in the art will clearly understand that for the convenience and brevity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the protection scope of the present invention is not limited to this. Any person skilled in the art can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention.

Claims

1. A method for evaluating light source color preference, characterized in that: The method comprises: Obtaining color temperature parameters of a test light source and spectral power distribution of the test light source within a preset wavelength range; Acquire an associated reference light source based on the color temperature parameter, and calculate a reference spectral power distribution of the reference light source within the preset wavelength range; Calculating a color quality index of the test light source based on a plurality of test color samples according to the color temperature parameter, the spectral power distribution, and the reference spectral power distribution; Calculating the color gamut space area formed by a plurality of test color samples under the test light source according to the spectral power distribution, and calculating the color resolution index of the test light source according to the color gamut space area; A color preference index of the test light source is calculated according to the color quality index and the color discrimination index.

2. The light source color preference evaluation method according to claim 1, characterized in that: Calculating the color quality index of the test light source based on a plurality of test color samples according to the color temperature parameter, the spectral power distribution, and the reference spectral power distribution includes: Calculating the white point tristimulus values of the test light source and the tristimulus values of the test color sample under the test light source based on the spectral power distribution; Calculating reference white point tristimulus values of the reference light source and reference tristimulus values of the test color sample under the reference light source based on the reference spectral power distribution; Calculating the chromaticity coordinates of the test color sample under the test light source according to the tristimulus values and the white point tristimulus values, and calculating the reference chromaticity coordinates of the test color sample under the reference light source according to the reference tristimulus values and the reference white point tristimulus values; Calculating a chromaticity difference and a coordinate difference of the test color sample according to the chromaticity coordinates and the reference chromaticity coordinates, and calculating a saturation factor of the test color sample based on the chromaticity difference and the coordinate difference; A color temperature factor is calculated according to the color temperature parameter, and a color quality index of the test light source is calculated according to the color temperature factor and the saturation factor.

3. The light source color preference evaluation method according to claim 2, characterized in that: Before calculating the chromaticity coordinates of the test color sample under the test light source according to the tristimulus values and the white point tristimulus values, the method further includes: Performing chromatic adaptation conversion on the tristimulus values to obtain a first conversion value, performing chromatic adaptation conversion on the white point tristimulus values to obtain a second conversion value, and performing chromatic adaptation conversion on the reference white point tristimulus values to obtain a third conversion value; calculating a color value of the test color sample according to the first conversion value, the second conversion value, and the third conversion value; The color values are converted according to a preset inverse conversion matrix to obtain new tristimulus values of the test color sample under the test light source.

4. The light source color preference evaluation method according to claim 1, characterized in that: The step of calculating the color gamut space area formed by a plurality of test color samples under the test light source according to the spectral power distribution, and calculating the color resolution index of the test light source according to the color gamut space area includes: Calculating the test tristimulus values of 8 test color samples under the test light source according to the spectral power distribution; Calculating the color space coordinates of the corresponding test color sample according to the test tristimulus values; Obtaining the color gamut space area based on an octagon formed by the eight color space coordinates; The color resolution index of the test light source is calculated according to the color gamut space area.

5. The light source color preference evaluation method according to any one of claims 1 to 4, characterized in that: After calculating the color preference index of the test light source according to the color quality index and the color discrimination index, the method further includes: Calculating a reference color preference index of the reference light source based on the reference spectral power distribution; The relative color preference index of the test light source is calculated according to the color preference index and the reference color preference index.

6. The light source color preference evaluation method according to claim 1, characterized in that: The acquiring of an associated reference light source based on the color temperature parameter and calculating a reference spectral power distribution of the reference light source within the preset wavelength range includes: If the color temperature parameter is less than a first color temperature threshold, the reference light source obtained is a Planckian illuminant, and a reference spectral power distribution of the Planckian illuminant is calculated according to the color temperature parameter and the preset wavelength range; If the color temperature parameter is greater than or equal to the first color temperature threshold, the obtained reference light source is a daylight illuminator, and a reference spectral power distribution of the daylight illuminator is calculated according to the color temperature parameter and the preset wavelength range.

7. The light source color preference evaluation method according to claim 6, characterized in that: The calculating the reference spectral power distribution of the daylight-illuminated object according to the color temperature parameter and the preset wavelength range includes: If the color temperature parameter is greater than the first color temperature threshold and the color temperature parameter is less than or equal to the second color temperature threshold, calculating the coordinate parameters of the daylight-illuminated object according to a first preset formula; wherein the second color temperature threshold is greater than the first color temperature threshold; If the color temperature parameter is greater than the second color temperature threshold, calculating the coordinate parameters of the daylight-illuminated object according to a second preset formula; A reference spectral power distribution of the daylight illumination object within the preset wavelength range is calculated according to the coordinate parameters.

8. A light source color preference evaluation device, characterized in that: The device comprises: A test light source acquisition module is used to obtain the color temperature parameters of the test light source and the spectral power distribution of the test light source within a preset wavelength range; A reference light source acquisition module, configured to acquire an associated reference light source based on the color temperature parameter, and calculate a reference spectral power distribution of the reference light source within the preset wavelength range; A color quality index module is configured to calculate the color quality index of the test light source based on a plurality of test color samples according to the color temperature parameter, the spectral power distribution, and the reference spectral power distribution; A color resolution index module is configured to calculate the color gamut space area formed by a plurality of test color samples under the test light source according to the spectral power distribution, and calculate the color resolution index of the test light source according to the color gamut space area; The color preference calculation module is configured to calculate the color preference index of the test light source according to the color quality index and the color resolution index.

9. An electronic device, characterized in that: include: At least one processor, at least one memory, and computer program instructions stored in the memory, which implement the method according to any one of claims 1 to 7 when the computer program instructions are executed by the processor.

10. A storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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