CN111324986A - Optimized color sample set obtaining method and device and color gamut index obtaining method and device - Google Patents

Abstract

The application provides an optimized color sample set obtaining method and device, a color gamut index obtaining method and device, and the optimized color sample set obtaining method comprises the following steps: acquiring color appearance parameters of various color samples in a large sample set; matching the lightness of each color sample with different lightness layers to obtain color samples of different lightness layers; matching the color samples of all lightness layers with different hue areas to obtain color samples positioned in the different hue areas; selecting a color sample with the maximum saturation in each hue region to form an initial high-saturation color sample set; clustering the color samples in the initial high-saturation color sample set to obtain a color sample cluster set of a plurality of color sample categories; and taking the respective clustering centers of the color sample clustering sets corresponding to the color sample categories as representative color samples of the color sample clustering sets of the color sample categories to obtain an optimized color sample set.

Description

Method and device for obtaining optimized color sample set, and method and device for obtaining color gamut index

technical field

The present application relates to the technical field of light source evaluation, and in particular, to a method for obtaining an optimized color sample set, a device for obtaining an optimized color sample set, a method for obtaining a color gamut index, a device for obtaining a color gamut index, an electronic device and a non-volatile readable storage medium.

Background technique

The color gamut index of the light source can be used to evaluate the ability of the light source to achieve a higher degree of color vividness of the illuminated object. Some light sources, especially LED light sources with a peaked spectrum, can accurately reproduce low-saturation colors, but perform poorly on color samples with high saturation. Therefore, the ability of the light source to reproduce the color of the sample with high saturation needs to be evaluated. The color gamut index of the light source is used to characterize the color reproduction ability of the light source to the sample with high saturation, and is an important index for evaluating the color rendering of the light source. When the existing methods use the light source color gamut index to evaluate the color rendering of the light source, the screening of the color samples required for the evaluation is mostly completed in the plane color gamut, which makes the applicability of the screened color samples limited. The selected color samples have different degrees of loss when evaluating the saturation of color samples with different lightness (that is, the color rendering of the light source), which affects the accuracy of the evaluation.

SUMMARY OF THE INVENTION

In view of this, the purpose of this application is to provide a method for obtaining an optimized color sample set, an optimized color sample obtaining and device, a method for obtaining a color gamut index, a device for obtaining a color gamut index, an electronic device and a non-volatile readable storage medium, It is used to improve the applicability of the screened color samples, thereby improving the accuracy of evaluating the color rendering of light sources by using the screened color samples.

The present application provides a method for obtaining an optimized color sample set, including: obtaining color appearance parameters of each color sample in a large sample set, where the color appearance parameters include lightness; The color samples of the lightness layer, wherein the different lightness layers are obtained by dividing the lightness at equal intervals; the color samples of each lightness layer are matched with the hue angle range of the different hue areas to obtain the color samples located in the different hue areas, wherein the different hues are The area is obtained by dividing the hue angle according to the interval of equal hue angle; selecting the color sample with the maximum saturation in each hue area to form an initial high-saturation color sample set; clustering the color samples in the initial high-saturation color sample set, Obtain the color swatch clustering sets of multiple color swatch categories; take the respective cluster centers of the color swatch clustering sets corresponding to each color swatch category as the representative color swatches of the color swatch clustering sets of the color swatch category, and obtain the optimized color swatch set .

In the method for obtaining an optimized color sample set provided by the present application, the color samples of different lightness layers are obtained by matching the lightness of each color sample with the lightness range of different lightness layers; Perform matching to obtain color samples located in different hue regions; select the color samples with the highest saturation in each hue region to form an initial high-saturation color sample set; cluster the color samples in the initial high-saturation color sample set to obtain A color sample cluster set of multiple color sample categories; and the respective cluster centers of the color sample cluster sets corresponding to each color sample category are used as the representative color samples of the color sample cluster set of the color sample category, and an optimized color sample set is obtained. , so that the finally obtained optimized color sample set can be suitable for evaluating the saturation of color samples with different lightness (that is, the color rendering of light source), thereby improving the evaluation accuracy to a certain extent.

Further, the acquiring the color appearance parameters of each color sample in the initial color sample set includes: acquiring the spectral data of each color sample in the large sample set; and obtaining the color appearance model based on the spectral data of each color sample in the large sample set. Color appearance parameters for each color sample in a large sample set.

The large sample set of this application can ensure the variety of color samples in the final screened optimized color sample set, and to a certain extent ensure that the final screened optimized color sample set can be applied to the color sample saturation of different lightness (that is, the light source Color rendering) was evaluated, which improved the applicability of the optimized color swatch set.

Further, performing the clustering on the color samples in the initial high-saturation color sample set to obtain a color sample clustering set of multiple color sample categories includes: spectral data of each color sample in the initial high-saturation color sample set Dimensionality reduction processing to obtain the main spectral data whose total contribution rate reaches the threshold; the main spectral data and the color appearance parameter are fused to form fusion data; based on the fusion data, the color samples in the initial high saturation color sample set are processed Clustering to obtain a color sample cluster set of the plurality of color sample categories.

The present application obtains the main spectral data whose total contribution rate reaches a threshold by reducing the dimension of the spectral data of each color sample in the initial high-saturation color sample set; fuses the main spectral data and the color appearance parameter to form fusion data; and Based on the fusion data, the color samples in the initial high-saturation color sample set are clustered to obtain the color sample cluster sets of the multiple color sample categories, so that the color appearance parameters and the spectral data are taken into account during the clustering, Avoid the phenomenon of metamerism, further improve the applicability of the final optimized color sample set and further improve the evaluation effect when using the final optimized color sample set to evaluate the color rendering of the light source; in addition, the dimensionality reduction of the spectral data can simplify the calculation. process to reduce the amount of computation.

Further, using the respective cluster centers of the color sample cluster sets corresponding to each color sample category as the representative color samples of the color sample cluster sets of the color sample category includes: merging the fusion data of all color samples of each color sample category. Calculate the mean value respectively; take the color sample with the smallest sum of absolute errors with the mean value as the representative color sample.

In this application, by calculating the average value of the fusion data of all the color samples of each color sample category, and taking the color sample with the smallest sum of absolute errors with the average value as the representative color sample, it can better solve the problem that the cluster center or the cluster center cannot be directly obtained. A case where the cluster center is not a point that actually exists in the corresponding color sample cluster set.

The present application also provides a method for obtaining a color gamut index, including: obtaining color appearance parameters of each color sample in a large sample set, where the color appearance parameters include lightness; The color samples of the lightness layer, wherein, the different lightness layers are obtained by dividing the lightness at equal intervals; the color samples of each lightness layer are matched with the hue angle range of the different tone areas to obtain the color samples located in the different tone areas, wherein the different tone areas are Obtained by dividing the hue angle according to the interval of equal hue angle; selecting the color sample with the highest saturation in each hue area to form an initial high-saturation color sample set; clustering the color samples in the initial high-saturation color sample set to obtain Color sample cluster sets of multiple color sample categories; take the respective cluster centers of the color sample cluster sets corresponding to each color sample category as the representative color samples of the color sample cluster sets of the color sample category, and obtain an optimized color sample set; and obtaining the color gamut index of the light source according to the optimized color sample set.

The color gamut index acquisition method provided by the present application obtains color samples of different lightness layers by matching the lightness of each color sample with the lightness range of different lightness layers; Match to obtain color samples located in different hue regions; select the color samples with the highest saturation in each hue region to form an initial high-saturation color sample set; cluster the color samples in the initial high-saturation color sample set to obtain multiple color swatch clustering sets of each color swatch category; and using the respective cluster centers of the color swatch clustering sets corresponding to each color swatch category as the representative color swatches of the color swatch clustering sets of the color swatch category, to obtain an optimized color swatch set, The final optimized color sample set is suitable for evaluating the saturation of color samples of different lightness (that is, the color rendering of the light source), and the color gamut index of the light source is obtained according to the optimized color sample set, and then the color gamut index is obtained according to the obtained color gamut index. The evaluation of the color rendering of the light source can improve the evaluation accuracy to a certain extent, that is, improve the evaluation accuracy.

The present application also provides a device for obtaining an optimized color sample set, comprising: an obtaining module for obtaining color appearance parameters of each color sample in a large sample set, the color appearance parameters including lightness; a matching module for comparing the lightness of each color sample with the lightness of each color sample The lightness ranges of different lightness layers are matched to obtain color samples of different lightness layers, wherein the different lightness layers are obtained by dividing the lightness at equal intervals, and the color samples of each lightness layer are matched with the hue angle range of the different tonal regions to obtain Color samples located in different hue areas, where the different hue areas are obtained by dividing the hue angles according to equal hue angle intervals; the selection module is used to select the color samples with the highest saturation in each hue area to form the initial high-saturation color samples The clustering module is used to cluster the color samples in the initial high-saturation color sample set to obtain color sample cluster sets of multiple color sample categories; the selection module is also used to The respective cluster centers of the color sample cluster sets are used as the representative color samples of the color sample cluster sets of the color sample category, and the optimized color sample set is obtained.

Further, the acquisition module is used to acquire the spectral data of each color sample in the large sample set, and based on the spectral data of each color sample in the large sample set, obtain the color appearance parameters of each color sample in the large sample set through a color appearance model. .

Further, the clustering module is used for dimensionality reduction processing of the spectral data of each color sample in the initial high-saturation color sample set, to obtain the main spectral data whose total contribution rate reaches a threshold value, and the main spectral data is compared with the color appearance. The parameters are fused to form fusion data, and based on the fusion data, the color samples in the initial high-saturation color sample set are clustered to obtain a color sample cluster set of the plurality of color sample categories.

Further, the selection module is further configured to calculate the average value of the fusion data of all the color samples of each color sample category, and use the color sample with the smallest sum of absolute errors with the mean value as the representative color sample.

The present application also provides a color gamut index acquisition device, comprising: an acquisition module for acquiring color appearance parameters of each color sample in a large sample set, the color appearance parameters including lightness; a matching module for comparing the lightness of each color sample with the difference The lightness ranges of the lightness layers are matched to obtain color samples of different lightness layers, wherein the different lightness layers are obtained by dividing the lightness at equal intervals, and the color samples of each lightness layer are matched with the hue angle ranges of the different tonal regions to obtain the Color samples of different hue areas, wherein the different hue areas are obtained by dividing the hue angle according to the interval of equal hue angles; the selection module is used to select the color samples with the highest saturation in each hue area to form an initial high-saturation color sample set; The clustering module is used for clustering the color samples in the initial high-saturation color sample set to obtain color sample cluster sets of multiple color sample categories; the selection module is also used for color samples corresponding to each color sample category The respective cluster centers of the cluster sets are used as the representative color samples of the color sample cluster sets of the color sample category to obtain an optimized color sample set; and a calculation module is used for obtaining the light source color gamut index according to the optimized color sample set.

The present application also provides an electronic device, including a processor and a memory, where the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the processor executes the above-mentioned optimized color sample set The acquisition method or the above-mentioned gamut index acquisition method.

The present application further provides a non-volatile readable storage medium storing computer-readable instructions, the computer-readable instructions, when executed by a processor, cause the processor to execute the above-mentioned method for obtaining an optimized color sample set or the above-mentioned color swatch set acquisition method. Domain index get method.

Other features and advantages of the present application will be set forth in the description which follows, and, in part, will be apparent from the description, or may be learned by practice of the embodiments of the present application. The objectives and other advantages of the application may be realized and attained by the structure particularly pointed out in the written description, claims, and drawings.

Description of drawings

In order to illustrate the technical solutions of the embodiments of the present application more clearly, the following drawings will briefly introduce the drawings that need to be used in the embodiments. It should be understood that the following drawings only show some embodiments of the present application, and therefore do not It should be regarded as a limitation of the scope, and for those of ordinary skill in the art, other related drawings can also be obtained according to these drawings without any creative effort.

FIG. 1 is a schematic flowchart of a method for obtaining an optimized color sample set provided by an embodiment of the present application.

FIG. 2 is a schematic flowchart of a method for obtaining a color gamut index according to an embodiment of the present application.

FIG. 3 is a structural block diagram of an apparatus for obtaining an optimized color sample set provided by an embodiment of the present application.

FIG. 4 is a structural block diagram of an apparatus for obtaining a color gamut index provided by an embodiment of the present application.

FIG. 5 is a structural block diagram of an electronic device provided by an embodiment of the present application.

Icon: Optimized color sample set acquisition device 10; acquisition module 11; matching module 12; selection module 13; clustering module 14; color gamut index acquisition device 20; calculation module 15; electronic equipment 100; Storage medium 102; Internal memory 103; Input device 104; Display screen 105; Scanning device 106; Network interface 107;

Detailed ways

The technical solutions in the embodiments of the present application will be described below with reference to the accompanying drawings in the embodiments of the present application.

It should be noted that like numerals and letters refer to like items in the following figures, so once an item is defined in one figure, it does not require further definition and explanation in subsequent figures. Meanwhile, in the description of the present application, the terms "first", "second", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

Referring to FIG. 1 , an embodiment of the present application provides a method for obtaining an optimized color sample set. The method includes the following steps.

Step S101, acquiring color appearance parameters of each color sample in the large sample set, where the color appearance parameters include lightness.

In this embodiment, the large sample set may include all or part of the color samples in the color sample data set actually measured in nature, and/or include all or part of the color sample spectral data in the artificially synthesized color sample spectral data set. Optionally, the large sample set includes more than 100,000 color samples. The large sample set can ensure the diversity of color samples in the final screened optimized color sample set, and to a certain extent ensure that the final screened optimized color sample set can be applied to the color sample saturation of different lightness (that is, the color rendering of the light source). performance), which improves the applicability of the optimized color swatch set.

In this embodiment, the color appearance parameters may include brightness J', red-green degree a', yellow-blue degree b', and the like.

In this embodiment, the spectral data of each color sample in the large sample set can be obtained; then, based on the spectral data of each color sample in the large sample set, the color appearance parameters of each color sample in the large sample set can be obtained through a color appearance model.

Specifically, the high-dimensional spectral reflectance of each color sample in the large sample set is obtained, and then the high-dimensional spectral reflectance of each color sample in the large sample set is obtained by referring to the spectral data of the light source and the standard observer function (2° field of view, or 10° field of view). field, etc.) into the color appearance model to calculate the color appearance parameters of each color sample in the large sample set. It can be understood that it is a known technology to use the color appearance model to calculate the color appearance parameters of the color sample, and the specific process thereof will not be described in detail here.

In this embodiment, the CAM16 color appearance model (with built-in CAT16 color adaptation and CAM16-UCS color space) can be selected as the color appearance model. It can be understood that, in other embodiments, the color appearance model can also be selected from other color appearance models, and the present application is not limited to this.

Step S102: Match the lightness of each color sample with the lightness range of different lightness layers to obtain color samples of different lightness layers, wherein the different lightness layers are obtained by dividing the lightness at equal intervals.

In this embodiment, the brightness (of brightness) is divided into a plurality of different brightness layers according to equal brightness intervals. Each brightness layer corresponds to a different brightness range. The selection of the brightness interval on which the division is based can be set as required.

After obtaining the color appearance parameters of each color sample in the large sample set through step S101, the lightness of each color sample is matched with the lightness range of different lightness layers, and when it is determined that the lightness of the color sample is within the lightness range of one lightness layer, it is considered that the lightness of each color sample is within the lightness range of one lightness layer. The color sample is the color sample of the lightness layer, so as to obtain color samples of different lightness layers. It can be understood that the number of color samples of each brightness layer may vary.

Step S103: Match the color samples of each lightness layer with the hue angle ranges of different hue regions to obtain color samples in different hue regions, wherein the different hue regions are obtained by dividing the hue angle according to equal hue angle intervals.

In this embodiment, after the brightness is pre-divided into multiple different brightness layers at equal brightness intervals, the hue (of hue angle) of each brightness layer is pre-divided into multiple different hue regions at equal hue angle intervals. Each hue region is for a different hue angle range. The selection of the hue angle interval on which the division is based can be set as required.

After obtaining the color samples of each lightness layer through step S102, the hue angle of each color sample is matched with the hue angle range of different hue regions, and when it is determined that the hue angle of the color sample is within the hue angle range of a hue region, it is considered that The color swatch is the color swatch of the hue area, thereby obtaining color swatches of different hue areas. The hue angle of each color sample can be obtained by the formula hue angle h=arctan (yellow-blue degree b'/red-green degree a') based on the red-green degree a' and the yellow-blue degree b' in the color appearance parameter. It can be understood that the number of color samples in each tone area may vary.

Step S104, selecting the color sample with the highest saturation in each hue area to form an initial high-saturation color sample set.

In this embodiment, the color sample with the highest saturation in each hue region can be selected by the formula: chroma C=(red and green degree a'^2+yellow and blue degree b'^2)^0.5. The most saturated color swatches in each hue region of each lightness layer together form an initial set of highly saturated color swatches.

Step S105: Cluster the color samples in the initial high-saturation color sample set to obtain color sample cluster sets of multiple color sample categories.

In this embodiment, the color swatches in the initial high-saturation color swatch set are clustered to obtain a color swatch clustering set of multiple color swatch categories in the following manner.

First, the spectral data of each color sample in the initial high-saturated color sample set is dimensionally reduced to obtain the main spectral data whose total contribution rate reaches the threshold; then, the main spectral data and the color appearance parameters are fused to form fusion data. Multi-dimensional data formed by spectral data and color appearance parameters; then, based on the fusion data, the color samples in the initial high-saturation color sample set are clustered to obtain a color sample cluster set of multiple color sample categories. In this embodiment, by reducing the dimensionality of the spectral data of each color sample in the initial high-saturation color sample set, the calculation process of clustering can be simplified and the amount of calculation can be reduced. Clustering can take into account the color appearance parameters and spectral data during clustering, avoid metamerism, further improve the applicability of the final optimized color sample set, and further improve the use of the final optimized color sample set for light source color rendering The effect of evaluation in sexual evaluation. It can be understood that, in other embodiments, the step of performing dimension reduction processing on the spectral data of each color sample in the initial high-saturation color sample set can also be omitted, and the spectral data of the color sample and the color appearance parameters can be directly fused to form fusion data.

In this embodiment, the principal component analysis spectral dimensionality reduction method can be used to reduce the dimensionality of the spectral data of each color sample in the initial high-saturation color sample set. Since the dimensionality reduction method can be realized by matlab code, it is a known technology, and it is not described here. The specific process is described in detail.

In this embodiment, the main spectral data whose total contribution rate reaches the threshold refers to the spectral data that are ranked in the front row after the dimensionality reduction process and whose total contribution rate reaches the threshold. The threshold can be set as required, and in this embodiment, the threshold is 95%.

In this embodiment, the color samples in the initial high-saturation color sample set can be clustered based on the fusion data, and K-means clustering algorithm, hierarchical clustering algorithm, neighbor propagation clustering algorithm, self-organizing mapping neural network clustering, etc. can be selected as required. Clustering Algorithm. When clustering, multi-dimensional data formed by three-dimensional color appearance parameters (lightness, red-green degree, yellow-blue degree) and main spectral data in the fusion data can be comprehensively considered for clustering. The specific process of clustering using the above-mentioned clustering algorithm is a known technology, and the specific process is not described in detail here. It can be understood that, in other embodiments, different index numbers may be assigned to different color samples according to the lightness, red-green degree, yellow-blue degree and main spectral data of the color sample. The yellow-blueness and the main spectral data form a corresponding relationship. After the clustering is completed, the corresponding color sample can be found and obtained through the corresponding relationship between the index number and the lightness, red-greenness, yellow-blueness and the main spectral data of the color sample. The lightness, red-green, yellow-blue and main spectral data of the color sample.

In step S106, the respective cluster centers of the color swatch cluster sets corresponding to each color swatch category are used as the representative color swatches of the color swatch cluster sets of the color swatch category to obtain an optimized color swatch set.

In this embodiment, in order to better solve the situation that the cluster center cannot be directly obtained or the cluster center is not the actual point in the corresponding color sample cluster set, the cluster center of each color sample category can be used as the representative color in the following way Sample. First, average the fusion data of all color samples of each color sample category; then, take the color sample with the smallest sum of absolute errors from the mean as the representative color sample.

The respective representative swatches of all swatch categories together form the optimized swatch set.

In the method for obtaining an optimized color sample set provided in this embodiment, the color samples of different lightness layers are obtained by matching the lightness of each color sample with the lightness range of different lightness layers; Match the range to obtain color samples located in different hue regions; select the color samples with the highest saturation in each hue region to form an initial high-saturation color sample set; cluster the color samples in the initial high-saturation color sample set, Obtaining a color sample clustering set of multiple color sample categories; and using the respective cluster centers of the color sample clustering sets corresponding to each color sample category as the representative color sample of the color sample clustering set of the color sample category, and obtaining an optimized color sample set, so that the finally obtained optimized color sample set can be suitable for evaluating the saturation of color samples with different lightness (that is, the color rendering of light source), thereby improving the evaluation accuracy to a certain extent.

Referring to FIG. 2 , based on the same inventive concept, an embodiment of the present application further provides a method for obtaining a color gamut index. On the basis of the foregoing method for obtaining the optimized color swatch set, the method for obtaining the color gamut index further includes step S107 after step S106: obtaining the color gamut index of the light source according to the optimized color sample set. In this embodiment, the color gamut index of the light source is obtained by using the color gamut index calculation formula according to the optimized color sample set. It can be understood that the specific process of obtaining the color gamut index of the light source through the color gamut index formula based on the color sample set is a known technology, and details are not described herein again.

The color gamut index acquisition method provided by the present application obtains color samples of different lightness layers by matching the lightness of each color sample with the lightness range of different lightness layers; Match to obtain color samples located in different hue regions; select the color samples with the highest saturation in each hue region to form an initial high-saturation color sample set; cluster the color samples in the initial high-saturation color sample set to obtain multiple color swatch clustering sets of each color swatch category; and using the respective cluster centers of the color swatch clustering sets corresponding to each color swatch category as the representative color swatches of the color swatch clustering sets of the color swatch category, to obtain an optimized color swatch set, The final optimized color sample set is suitable for evaluating the saturation of color samples of different lightness (that is, the color rendering of the light source), and the color gamut index of the light source is obtained according to the optimized color sample set, and then the color gamut index is obtained according to the obtained color gamut index. The evaluation of the color rendering of the light source can improve the evaluation accuracy to a certain extent, that is, improve the evaluation accuracy.

Next, the technical effect of using the color gamut index of the light source to obtain the color gamut index of the light source to evaluate the color rendering of the light source will be described with reference to a specific example.

In this example, the large sample set includes actually measured color sample data existing in nature and artificially synthesized spectral data, and the large sample set includes more than 100,000 color samples. The 10° standard observer of the CIE1964 standard chromaticity system is used as the standard observer function, the D65 light source is used as the reference light source to calculate the CAM16-UCS color space, and the color appearance parameters (lightness J' , red and green degrees a', and yellow and blue degrees b'). A plurality of lightness layers are obtained by dividing the lightness at equal intervals with a lightness interval of 1 in advance, and a plurality of hue regions are obtained by dividing the hue at equal intervals with a hue angle interval of 1°. The initial high-saturation color sample set Ωg is obtained in the manner described in the previous method. The initial high-saturation color sample set Ωg contains 24,843 high-saturation color samples. The 81-dimensional spectrum of 24,843 color samples (for light wavelengths 380nm-780nm, divided at equal intervals with an interval of 5nm to obtain an 81-dimensional spectrum) is subjected to principal component spectral dimension reduction, and the contribution rates are 80.58%, 12.15% and 4.68 %, and the total contribution rate exceeds 97% of the first 3-dimensional main spectral data. The main spectral data (three-dimensional data) and color appearance parameters (lightness, red-green and yellow-blue three-dimensional data) are fused to obtain 6-dimensional fusion data. Self-organizing map neural network clustering on fused data. The specific process of self-organizing mapping neural network clustering for fusion data is roughly as follows:

1) Initialization: each node (color sample) randomly initializes its own parameters (in this embodiment, the own parameters are the data of each dimension in the 6-dimensional fusion data formed by the color appearance parameters and the 3-dimensional main spectral data). The number of parameters for each node is the same as the dimension of the input.

2) For each input data, find the node that best matches it. Assuming that the input is D-dimensional data, that is, X={xi, i=1,2,...,D}, the most matching node is found for the Euclidean distance through the discriminant function:

Among them, j represents the jth node, and _wji represents the data of the ith dimension of the jth node.

随节点与激活节点I(x)之间的距离的增大而减小。更新权重可以通过下式表示。3) After finding the active node I(x), update its adjacent nodes. Let S _ij denote the distance between nodes i and j, and assign update weights to nodes adjacent to I(x). update weights

It decreases as the distance between the node and the active node I(x) increases. The update weight can be expressed by the following equation.

是I(x)相邻节点的更新权值，S_j,I(x)代表第j个节点和第I(x)个节点之间的距离，

其中，t是时间，σ₀是适当选取的常数。in,

is the updated weight of the adjacent nodes of I(x), S _{j, I(x)} represents the distance between the jth node and the I(x)th node,

where t is time and _σ0 is an appropriately chosen constant.

4) Update the parameters of the node according to the gradient descent method:

Δw _ji =η(t).T _{j, I(x)} (t).(x _i -w _ji )

其中，t为时间；x_i表示第i个维度的数据；w_ji表示第j个节点的第i个维度的数据。Among them, Δw _ji is the weight modifier; η(t) is the learning rate,

Among them, t is time; _xi represents the data of the ith dimension; w _ji represents the data of the ith dimension of the jth node.

Then, iterate until convergence or the maximum number of iterations is reached.

The fused data is clustered through the self-organizing mapping neural network, and finally the color swatch clustering sets of 16 color swatch categories are obtained, and the respective cluster centers of the color swatch clustering sets corresponding to each color swatch category are used as the color swatches of the color swatch category. The representative color samples of the sample cluster set are obtained, and the optimized color sample set Θg containing 16 high-saturation optimized color samples is obtained.

In this example, when evaluating the color rendering of the light source, the color gamut index Rg in the existing light source color gamut index is selected as the evaluation index. With the optimized color sample set Θg containing 16 high-saturation optimized color samples, the color gamut index Rg in the CES color sample (IES-TM-30 (IES system), using the current relatively uniform color space CAM02-UCS and 99 The standard color sample, recorded as CES color sample) and the original large sample set are used as test color samples respectively, and the actual calculation is carried out on more than 1,200 lighting sources such as LEDs, fluorescent lamps, and traditional light sources with different color temperatures, and the optimized color sample sets Θg and The difference between the Rg calculated by the CES color sample and the Rg calculated by the original large sample set, and the mean absolute error MAD is calculated. As shown in Table 1, the average absolute error MAD of the optimized color sample set Θg is small and smaller than the MAD of the CES color sample. higher; and the color gamut index Rg value calculated through the optimized color sample set Θg is closer to the color gamut index Rg calculated through the original large color sample set.

Table 1 Θg/CES and mean absolute error of Rg calculated with large sample set

Referring to FIG. 3 , based on the same inventive concept, an embodiment of the present application further provides a device 10 for obtaining an optimized color sample set. The device 10 for obtaining an optimized color sample includes an obtaining module 11 , a matching module 12 , a selection module 13 , and a clustering module 11 . module 14.

The obtaining module 11 is used to obtain the color appearance parameters of each color sample in the large sample set, and the color appearance parameters include lightness. In one embodiment, the acquisition module 11 is configured to acquire spectral data of each color sample in the large sample set, and obtain color appearance parameters of each color sample in the large sample set through a color appearance model based on the spectral data of each color sample in the large sample set.

The matching module 12 is used to match the lightness of each color sample with the lightness range of different lightness layers to obtain color samples of different lightness layers, wherein the different lightness layers are obtained by dividing the lightness at equal intervals, and the color samples of each lightness layer are divided. Matching with the hue angle ranges of different hue areas to obtain color samples located in different hue areas, wherein the different hue areas are obtained by dividing hue angles according to equal hue angle intervals;

The selection module 13 is used to select the color sample with the highest saturation in each hue area to form an initial high-saturation color sample set.

The clustering module 14 is used for clustering the color samples in the initial high-saturation color sample set to obtain color sample cluster sets of multiple color sample categories. In one embodiment, the clustering module 14 is used to reduce the dimension of the spectral data of each color sample in the initial high-saturation color sample set, obtain the main spectral data whose total contribution rate reaches a threshold, and fuse the main spectral data with the color appearance parameters to form fusion data , and clustering the color samples in the initial high-saturation color sample set based on the fusion data to obtain a color sample clustering set of multiple color sample categories.

In this embodiment, the selection module 13 is further configured to use the respective cluster centers of the color swatch cluster sets corresponding to each color swatch category as the representative color swatches of the color swatch cluster sets of the color swatch category to obtain an optimized color swatch set. In one embodiment, the selection module 13 is further configured to calculate the average value of the fusion data of all the color samples of each color sample category, and use the color sample with the smallest sum of absolute errors from the mean value as the representative color sample.

It can be understood that the optimized color sample set acquisition device provided by this application corresponds to the optimized color sample set acquisition method provided by this application. Repeat.

Referring to FIG. 4 , based on the same inventive concept, an embodiment of the present application further provides a color gamut index acquisition device 20 , the color gamut index acquisition device 20 further includes a calculation module on the basis of the aforementioned optimized color sample set acquisition device 10 15, used to obtain the color gamut index of the light source according to the optimized color sample set. In one embodiment, the calculation module 15 is configured to obtain the color gamut index of the light source through the color gamut index calculation formula according to the optimized color sample set.

It can be understood that the color gamut index obtaining device 20 provided by the present application is based on the aforementioned optimized color sample set obtaining device 10, and corresponds to the color gamut index obtaining method provided by the present application. The content of the color sample set obtaining apparatus 10 and the color gamut index obtaining method will not be repeated here.

Each module in the above-mentioned optimized color sample set acquisition device and/or color gamut index acquisition device may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in or independent of the processor in the server in the form of hardware, or may be stored in the memory in the server in the form of software, so that the processor can call and execute operations corresponding to the above modules. The processor may be a central processing unit (CPU), a microprocessor, a single-chip microcomputer, or the like.

The above-mentioned optimized color sample set acquisition method and/or optimized color sample set acquisition device and/or color gamut index acquisition method and/or color gamut index acquisition device can be implemented as a form of computer-readable instructions, and the computer-readable instructions can be in the form of run on the electronic device shown in Figure 5.

An electronic device further provided by an embodiment of the present application includes a memory, a processor, and a computer-readable instruction stored in the memory and executable on the processor. When the processor executes the program, the above-mentioned method for obtaining an optimized color sample set is implemented. And/or the above-mentioned color gamut index acquisition method.

FIG. 5 is a schematic diagram of an internal structure of an electronic device according to an embodiment of the present application, and the electronic device may be a mobile phone, a tablet computer, or the like. Referring to FIG. 5 , the electronic device 100 includes a processor 101 , a non-volatile storage medium 102 , an internal memory 103 , an input device 104 , a display screen 105 , a scanning device 106 and a network interface 107 connected through a system bus 108 . The non-volatile storage medium of the electronic device 100 can store an operating system and computer-readable instructions. When the computer-readable instructions are executed, the processor 101 can be made to execute an optimized color sample set according to the embodiments of the present application. The specific implementation process of the acquisition method and/or the color gamut index acquisition method, the specific implementation process of the optimized color sample set acquisition method can refer to the specific content of FIG. 1, and the specific implementation process of the color gamut index acquisition method can refer to the specific content of FIG. Repeat. The processor 101 of the electronic device 100 is used to provide computing and control capabilities to support the operation of the entire electronic device 100 . The internal memory 103 may store computer-readable instructions, and when executed by the processor 101, the computer-readable instructions may cause the processor 101 to execute an optimized color sample set acquisition method and/or a color gamut index acquisition method. The input device 104 of the electronic device 100 is used for inputting various parameters, the display screen 105 of the electronic device 100 is used for displaying, the scanning device 106 of the electronic device 100 is used for scanning graphic codes, and the network interface 107 of the electronic device 100 is used for network communication. Those skilled in the art can understand that the structure shown in FIG. 5 is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the electronic device to which the solution of the present application is applied. The specific electronic device may be Include more or fewer components than shown in the figures, or combine certain components, or have a different arrangement of components.

Based on the same inventive concept, a computer-readable storage medium provided by an embodiment of the present application stores computer-readable instructions thereon, and when the computer-readable instructions are executed by a processor, realizes the above-mentioned method for obtaining an optimized color sample set and/or The above-mentioned color gamut index acquisition method.

Any reference to a memory, storage, database or other medium as used herein may include non-volatile. Suitable nonvolatile memory may include read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory.

In the embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other manners. The apparatus embodiments described above are only illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined or Can be integrated into another system, or some features can be ignored, or not implemented. On the other hand, the shown or discussed mutual coupling or direct coupling or communication connection may be through some communication interfaces, indirect coupling or communication connection of devices or units, which may be in electrical, mechanical or other forms.

In addition, units described as separate components may or may not be physically separated, and components shown as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution in this embodiment.

Furthermore, each functional module in each embodiment of the present application may be integrated together to form an independent part, or each module may exist alone, or two or more modules may be integrated to form an independent part.

The above descriptions are merely examples of the present application, and are not intended to limit the protection scope of the present application. For those skilled in the art, various modifications and changes may be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this application shall be included within the protection scope of this application.

Claims (10)

1. an optimized color sample set acquisition method, is characterized in that, comprises: obtaining the color appearance parameters of each color sample in the large sample set, where the color appearance parameters include lightness; Match the lightness of each color sample with the lightness range of different lightness layers to obtain color samples of different lightness layers, wherein the different lightness layers are obtained by dividing the lightness at equal intervals; Matching the color samples of each lightness layer with the hue angle ranges of different hue areas to obtain color samples located in different hue areas, wherein the different hue areas are obtained by dividing hue angles according to equal hue angle intervals; Select the color sample with the highest saturation in each hue area to form the initial high-saturation color sample set; Clustering the color samples in the initial high-saturation color sample set to obtain a color sample clustering set of multiple color sample categories; The optimized color swatch set is obtained by taking the respective cluster centers of the color swatch clustering sets corresponding to each color swatch category as the representative color swatches of the color swatch clustering sets of the color swatch category. 2. The method for obtaining an optimized color sample set according to claim 1, wherein the obtaining the color appearance parameters of each color sample in the initial color sample set, comprising: acquiring spectral data of each color sample in the large sample set; Based on the spectral data of each color sample in the large sample set, the color appearance parameters of each color sample in the large sample set are obtained through a color appearance model. 3 . The method for obtaining an optimized color sample set according to claim 1 , wherein the color samples in the initial high-saturation color sample set are clustered to obtain a color sample clustering set of multiple color sample categories. 4 . ,include: Dimensionality reduction processing is performed on the spectral data of each color sample in the initial high-saturation color sample set to obtain the main spectral data whose total contribution rate reaches a threshold; fusing the main spectral data and the color appearance parameters to form fusion data; The color samples in the initial high-saturation color sample set are clustered based on the fusion data to obtain a color sample cluster set of the multiple color sample categories. 4. The method for obtaining an optimized color swatch set according to claim 1, wherein the respective cluster centers of the color swatch clustering sets corresponding to each color swatch category are used as the Representing color swatches, including: Calculate the average value of the fusion data of all color samples of each color sample category; The color sample with the smallest sum of absolute errors from the mean value is taken as the representative color sample. 5. a color gamut index acquisition method, is characterized in that, comprises: obtaining the color appearance parameters of each color sample in the large sample set, where the color appearance parameters include lightness; Match the lightness of each color sample with the lightness range of different lightness layers to obtain color samples of different lightness layers, wherein the different lightness layers are obtained by dividing the lightness at equal intervals; Matching the color samples of each lightness layer with the hue angle ranges of different hue regions to obtain color samples located in different hue regions, wherein the different hue regions are obtained by dividing hue angles according to equal hue angle intervals; Select the color sample with the highest saturation in each hue area to form the initial high-saturation color sample set; Clustering the color samples in the initial high-saturation color sample set to obtain a color sample clustering set of multiple color sample categories; Taking the respective cluster centers of the color sample cluster sets corresponding to each color sample category as the representative color samples of the color sample cluster sets of the color sample category, an optimized color sample set is obtained; and The color gamut index of the light source is obtained according to the optimized color sample set. 6. A device for obtaining an optimized color sample set, comprising: an acquisition module, configured to acquire color appearance parameters of each color sample in the large sample set, where the color appearance parameters include lightness; The matching module is used to match the brightness of each color sample with the brightness range of different brightness layers, and obtain the color samples of different brightness layers, wherein the different brightness layers are obtained by dividing the brightness at equal intervals, and the color samples of each brightness layer and The hue angle ranges of different hue regions are matched to obtain color samples located in different hue regions, wherein the different hue regions are obtained by dividing hue angles according to equal hue angle intervals; The selection module is used to select the color sample with the highest saturation in each hue area to form an initial high-saturation color sample set; a clustering module, configured to perform clustering on the color samples in the initial high-saturation color sample set to obtain color sample cluster sets of multiple color sample categories; The selection module is further configured to use the respective cluster centers of the color sample cluster sets corresponding to each color sample category as the representative color samples of the color sample cluster sets of the color sample category to obtain an optimized color sample set. 7. The device for obtaining an optimized color sample set according to claim 6, wherein the clustering module is used to perform dimension reduction processing on the spectral data of each color sample in the initial high-saturation color sample set, and obtain a total contribution rate of up to 100%. The main spectral data of the threshold value, the main spectral data and the color appearance parameter are fused to form fusion data, and the color samples in the initial high-saturation color sample set are clustered based on the fusion data to obtain the plurality of A set of swatch clusters for swatch categories. 8. A color gamut index acquisition device, characterized in that, comprising: an acquisition module, configured to acquire color appearance parameters of each color sample in the large sample set, where the color appearance parameters include lightness; The matching module is used to match the brightness of each color sample with the brightness range of different brightness layers, and obtain the color samples of different brightness layers, wherein the different brightness layers are obtained by dividing the brightness at equal intervals, and the color samples of each brightness layer and The hue angle ranges of different hue regions are matched to obtain color samples located in different hue regions, wherein the different hue regions are obtained by dividing hue angles according to equal hue angle intervals; The selection module is used to select the color sample with the highest saturation in each hue area to form an initial high-saturation color sample set; a clustering module, configured to cluster the color samples in the initial high-saturation color sample set to obtain color sample cluster sets of multiple color sample categories; The selection module is also used to use the respective cluster centers of the color sample cluster sets corresponding to each color sample category as the representative color samples of the color sample cluster sets of the color sample categories to obtain an optimized color sample set; and The calculation module is configured to obtain the color gamut index of the light source according to the optimized color sample set. 9. An electronic device, characterized in that it comprises a processor and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the processor is made to perform the execution as claimed in the claims The method for obtaining an optimized color sample set according to any one of 1 to 4 or the method for obtaining a color gamut index according to claim 5 . 10. A non-volatile readable storage medium storing computer readable instructions which, when executed by a processor, cause the processor to perform the execution of any one of claims 1 to 4 The optimized color sample set acquisition method or the color gamut index acquisition method described in claim 5.

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