A display device calibration method with built-in color management module

Abstract

The present invention discloses a display device correction method with a built-in color management module, including: obtaining the original color temperature value of the screen of the display device and the ambient color temperature value of the environment in which it is located, calculating the color temperature difference data of the original color temperature value of the screen and the ambient color temperature value of the environment in which it is located; obtaining the color feature information of the display device; querying a preset data table according to the color temperature difference data and the color feature information to determine the corresponding LAB data; performing data conversion on the LAB data to obtain the first XYZ data; obtaining the first color matrix according to the first XYZ data; determining the second XYZ data according to the color feature information; obtaining the second color matrix according to the second XYZ data; determining the color correction parameters, and performing correction processing on the display device. To avoid the problem of different colors being presented under different light sources, the influence of the ambient color temperature on the display content of the display device is considered to improve the accuracy of color correction.

Description

Technical Field

[0001] The present invention relates to the technical field of color correction, and in particular to a correction method for a display device with a built-in color management module. Background Art

[0002] With the development of new media technology, LCD monitors, as a commonly used digital image display device, often perform cross-media color reproduction. However, in the reproduction process, large color differences are prone to occur, resulting in image color distortion. Therefore, it is necessary to perform color calibration on the display screen on the display device. At present, color calibration of display devices only considers the color information and original color temperature information of the display device, and only considers the color gamut, resulting in low accuracy of color difference calibration, and it is easy to present different colors under different light sources. At the same time, the impact of ambient color temperature on the display content of the display device is not considered, resulting in poor viewing experience for users and inability to accurately perform color correction. Summary of the invention

[0003] The present invention aims to solve at least one of the technical problems in the above-mentioned technology to a certain extent. To this end, the purpose of the present invention is to propose a display device calibration method with a built-in color management module, so as to avoid the problem of different colors under different light sources, consider the influence of the ambient color temperature on the display content of the display device, and improve the accuracy of color calibration.

[0004] To achieve the above object, an embodiment of the present invention provides a display device calibration method with a built-in color management module, comprising:

[0005] Obtaining the original color temperature value of the screen of the display device and the ambient color temperature value of the environment in which the display device is located, and calculating the color temperature difference data between the original color temperature value of the screen and the ambient color temperature value of the environment in which the display device is located;

[0006] Obtain color characteristic information of the display device;

[0007] According to the color temperature difference data and color feature information, the preset data table is searched to determine the corresponding LAB data;

[0008] Performing data conversion on the LAB data to obtain first XYZ data; obtaining a first color matrix according to the first XYZ data;

[0009] Determine second XYZ data according to the color feature information; obtain a second color matrix according to the second XYZ data;

[0010] Color correction parameters are determined according to the first color matrix and the second color matrix, and correction processing is performed on the display device.

[0011] According to some embodiments of the present invention, the original color temperature value of the screen of the display device is determined based on color analysis software.

[0012] According to some embodiments of the present invention, obtaining an ambient color temperature value of an environment in which a display device is located includes:

[0013] Acquire an environmental image of the environment in which the display device is located;

[0014] Perform image segmentation processing on the environment image to obtain several local images; obtain the XYZ color value of each pixel in the local image; the XYZ color value includes the X channel value, the Y channel value and the Z channel value;

[0015] Calculate the average value of the X channel, the average value of the Y channel and the average value of the Z channel according to the XYZ color value of each pixel in the local image;

[0016] Calculate the ratio of the average value of the X channel to the average value of the Y channel as the first ratio; calculate the ratio of the average value of the Z channel to the average value of the Y channel as the second ratio; use the ratio of the average value of the X channel to the average value of the Z channel as the third ratio; generate three-dimensional coordinates according to the first ratio, the second ratio and the third ratio, search in a preset three-dimensional color temperature model according to the three-dimensional coordinates, determine the color temperature value corresponding to the coordinate point, and use it as the color temperature value of the local image;

[0017] Determine the ratio of the area of ​​the local image to the area of ​​the environment image as a weight coefficient corresponding to the local image;

[0018] A weighted calculation is performed based on the color temperature value of each local image and the corresponding weight coefficient to obtain the ambient color temperature value of the environment in which the display device is located.

[0019] According to some embodiments of the present invention, obtaining color feature information of a display device includes:

[0020] Collect the initial spectrum information of the XYZ channels of the display device;

[0021] Extract features from the initial spectral information and determine several color attribute features;

[0022] Based on the principal component analysis algorithm, several color attribute features are sorted according to their importance, and color attribute features with importance greater than a preset threshold are selected as target color attribute features;

[0023] The target color attribute features are input into the pre-built color neural model, and the color feature information of the display device is output.

[0024] According to some embodiments of the present invention, feature extraction is performed on the initial spectral information to determine a number of color attribute features, including:

[0025] Based on the Mahalanobis distance algorithm, the abnormal information of the initial spectral information is screened out, and noise reduction is performed to obtain noise-reduced data;

[0026] The spectral data of the corresponding band of visible light color is selected from the denoised data and mapped into a spectral curve, and several color attribute features in the spectral curve are extracted.

[0027] According to some embodiments of the present invention, the preset data table is a color temperature difference-color feature-LAB data comparison data table;

[0028] The method for obtaining the preset data table includes:

[0029] Obtain reference color information of display devices at different color temperature differences;

[0030] Create a preset data table based on the reference color information.

[0031] According to some embodiments of the present invention, after the display device is calibrated, the method further includes: testing the calibrated display device.

[0032] According to some embodiments of the present invention, before the display device is calibrated, it is also included to determine whether the display device is faulty, and when it is determined that the display device is faulty, a maintenance instruction is issued.

[0033] According to some embodiments of the present invention, determining whether a display device is faulty includes:

[0034] Determine whether the display device is faulty based on a database generated by fault record data, where P fault record data exist in the database, each fault record data contains the values ​​of N indicators monitored by the display device at different operating faults, and an analysis matrix X is formed based on the values ​​of the N indicators of the P fault record data, the analysis matrix X has P rows and N columns, and each fault record data corresponds to whether an operating fault occurs, and a state vector Y formed by the faulty part, and the vector Y contains D1 values;

[0035] Perform feature learning on the analysis matrix X;

[0036] W11=rand(N,D1) W12=rand(N)

[0037]

[0038] Wherein, W11=rand(N, D1) is to generate a random matrix W11 with N rows and D1 columns; W12=rand(N) is to generate a random vector W12 with N elements whose values ​​are 0 to 1; W1 is the result of normalizing the matrix W11; W2 is the result of normalizing the vector W12; sum is the sum, f(X i ) is used for X i The feature learning vector obtained by solving, X i is the value of the i-th row of the analysis matrix X; W2 eTo solve each element in vector W2 to the power of e, e is a natural constant, To vector All elements in are compared with 0 and the larger value is taken, i = 1, 2, 3...P;

[0039] Construct a i )’s fault characteristic value analysis parameters:

[0040]

[0041] Where SS is the fault characteristic value analysis parameter; j≠Y i For j, take the vector Y except the Yth i All the remaining values ​​of Y i is the position of the value equal to the i-th value of vector Y1 in the middle of vector Y; f(X i ) j is the vector f(X i ), is the vector f(X i ) i value, W2 t is the value of the tth element of vector W2, W1 t，s is the value of the tth row and Sth column of the matrix W1, max() is the maximum value in the brackets, i=1, 2, 3...P, t=1, 2, 3...N, S=1, 2, 3...D1;

[0042] The current detection data of the display device is obtained, including the values ​​of N indicators, and the average value is taken, and compared with the fault characteristic value analysis parameter. When it is determined that the average value is greater than the fault characteristic value analysis parameter, it indicates that the display device has a fault; otherwise, it indicates that the display device has not a fault.

[0043] According to some embodiments of the present invention, determining color correction parameters according to the first color matrix and the second color matrix, and performing correction processing on the display device includes:

[0044] The first color matrix is ​​compared with the second color matrix, a difference parameter is determined as a color correction parameter, and correction processing is performed on the display device based on the color correction parameter.

[0045] The present invention proposes a display device calibration method with a built-in color management module to avoid the problem of different colors under different light sources. The preset data table is queried based on color temperature difference data and color feature information to determine the corresponding LAB data, and then the first color matrix is ​​determined. The second XYZ data is determined according to the color feature information; the second color matrix is ​​obtained, and the color correction parameters are determined according to the first color matrix and the second color matrix, and the display device is calibrated. The influence of the ambient color temperature on the display content of the display device is considered to improve the accuracy of color correction.

[0046] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0047] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0049] Figure 1 is a flow chart of a display device calibration method with a built-in color management module according to an embodiment of the present invention;

[0050] Figure 2 is a flow chart of obtaining the ambient color temperature value of the environment in which the display device is located according to one embodiment of the present invention;

[0051] Figure 3 The flowchart of obtaining color characteristic information of a display device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0052] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0053] like Figure 1 As shown, the embodiment of the present invention proposes a display device calibration method with a built-in color management module, including steps S1-S6:

[0054] S1. Obtaining the original color temperature value of the screen of the display device and the ambient color temperature value of the environment in which the display device is located, and calculating the color temperature difference data between the original color temperature value of the screen and the ambient color temperature value of the environment in which the display device is located;

[0055] S2. Obtaining color feature information of a display device;

[0056] S3, querying a preset data table according to the color temperature difference data and color feature information to determine the corresponding LAB data;

[0057] S4, performing data conversion on the LAB data to obtain first XYZ data; and obtaining a first color matrix;

[0058] S5, determining second XYZ data according to the color feature information; obtaining a second color matrix according to the second XYZ data;

[0059] S6. Determine color correction parameters according to the first color matrix and the second color matrix, and perform correction processing on the display device.

[0060] The working principle of the above technical solution: The original color temperature value of the screen refers to the color temperature value of the screen when it is not affected by any external light. This value can be measured by professional calibration equipment and software. For example, color management software can be used with a calibrator for measurement. The ambient color temperature value of the environment is the color temperature of the light in the surrounding environment. The color temperature difference data of the original color temperature value of the screen and the ambient color temperature value of the environment is calculated as the original color temperature value of the screen - the ambient color temperature value = the color temperature difference data.

[0061] In this embodiment, the color characteristic information includes: color gamut coverage, that is, the color gamut of a display device refers to the range of colors it can display; and color depth, that is, the number of colors that each pixel can present.

[0062] In this embodiment, the LAB color space is a color model based on human visual perception, which is closer to the human eye's perception of color. A preset data table contains LAB data corresponding to different color temperature differences and color feature information. This data table is obtained through experimental measurement, professional software generation or reference to relevant literature. Ensuring the accuracy and completeness of the data table is crucial for subsequent queries. Use the extracted color temperature difference data and color feature information as query conditions to search in the preset data table. It can be implemented by programming and completed using a database query language (such as SQL) or other data processing tools. Make sure that the query conditions match the fields in the data table so that the corresponding LAB data can be accurately found.

[0063] In this embodiment, the conversion from LAB color space to XYZ color space can be performed by the following steps, referring to the standard reference white point D65 (Xn=0.95047, Yn=1.00000, Zn=1.08883) as a benchmark: Define constants and variables: Xn, Yn, Zn: XYZ coordinates of the reference white point of the XYZ space. Here we choose D65 as the standard, that is, Xn=0.95047, Yn=1.00000, Zn=1.08883. L, a, b: three components of the input LAB color space, where L represents brightness, a represents the red and green components of chromaticity, and b represents the yellow and blue components of chromaticity. f: a constant used to determine the input of the nonlinear function F(x). Calculate the value of f: when Lab(Y / Yn)>(6 / 29) 3 When, f=1 / 3. Otherwise, f=(29 / 6) 2 / 3. Define the nonlinear function F(x): F(x) is a nonlinear function used to approximate the inverse transformation. We only need to know the relationship between its input and output, without the specific function expression. Apply the transformation formula: X = Xn*F(Lab(Y / Yn+f)), Y = Lab(Y) (Note that Y here is the Y component in the LAB space, not the calculated value) Z = Zn*F(Lab(Y / Yn-f)); Normalize the result (if necessary): The X, Y, and Z values ​​in the transformation result are normalized.

[0064] Determining a first color matrix according to the first XYZ data includes: obtaining intermediate data based on a gamma correction function for the first XYZ data; a color matrix is ​​a mathematical model that describes color changes, which can be used to represent the transformation of a color space. To convert the intermediate data into a color matrix, an inverse gamma correction function and an inverse conversion matrix are required. The inverse gamma correction function can convert the X, Y, and Z components of the color space corresponding to the intermediate data into the X, Y, and Z components of a common XYZ color space, and the inverse conversion matrix can convert the X, Y, and Z components of the common XYZ color space into a color matrix.

[0065] In this embodiment, the second XYZ data is determined according to the color feature information, and for the conversion from RGB to XYZ, a standard conversion matrix is ​​used for conversion to obtain the second XYZ data. The method for obtaining the second color matrix according to the second XYZ data is consistent with the method for obtaining the first color matrix. The second color matrix reflects the performance of the display device under various colors and its deviation from the standard color space. The color correction parameters are determined according to the first color matrix and the second color matrix, and the display device is corrected.

[0066] The beneficial effects of the above technical solution are: avoiding the problem of different colors under different light sources, querying the preset data table based on the color temperature difference data and color feature information, determining the corresponding LAB data, and then determining the first color matrix. Determine the second XYZ data according to the color feature information; obtain the second color matrix, determine the color correction parameters according to the first color matrix and the second color matrix, and perform correction processing on the display device. Consider the influence of the ambient color temperature on the display content of the display device, and improve the accuracy of color correction.

[0067] According to some embodiments of the present invention, the original color temperature value of the screen of the display device is determined based on color analysis software.

[0068] like Figure 2 As shown, according to some embodiments of the present invention, obtaining the ambient color temperature value of the environment in which the display device is located includes steps S11-S16:

[0069] S11, obtaining an environment image of the environment in which the display device is located;

[0070] S12, performing image segmentation processing on the environment image to obtain a plurality of local images; obtaining the XYZ color value of each pixel in the local image; the XYZ color value includes an X channel value, a Y channel value and a Z channel value;

[0071] S13, calculating the X channel average value, the Y channel average value and the Z channel average value according to the XYZ color value of each pixel in the local image;

[0072] S14, calculating the ratio of the average value of the X channel to the average value of the Y channel as the first ratio; calculating the ratio of the average value of the Z channel to the average value of the Y channel as the second ratio; using the ratio of the average value of the X channel to the average value of the Z channel as the third ratio; generating three-dimensional coordinates according to the first ratio, the second ratio and the third ratio, searching in a preset three-dimensional color temperature model according to the three-dimensional coordinates, determining the color temperature value corresponding to the coordinate point as the color temperature value of the local image;

[0073] S15, determining a ratio of an area of ​​the local image to an area of ​​the environment image as a weight coefficient corresponding to the local image;

[0074] S16. Perform weighted calculation according to the color temperature value of each local image and the corresponding weight coefficient to obtain the ambient color temperature value of the environment where the display device is located.

[0075] The working principle and beneficial effect of the above technical solution are as follows: obtaining an environmental image of the environment in which the display device is located, performing image segmentation processing on the environmental image, and obtaining a plurality of local images; obtaining the XYZ color value of each pixel in the local image, calculating the ratio of the average value of the X channel to the average value of the Y channel as the first ratio; calculating the ratio of the average value of the Z channel to the average value of the Y channel as the second ratio; using the ratio of the average value of the X channel to the average value of the Z channel as the third ratio; generating three-dimensional coordinates according to the first ratio, the second ratio and the third ratio, searching in a preset three-dimensional color temperature model according to the three-dimensional coordinates, determining the color temperature value corresponding to the coordinate point as the color temperature value of the local image, determining the ratio of the area of ​​the local image to the area of ​​the environmental image as the weight coefficient corresponding to the local image, performing weighted calculation according to the color temperature value of each local image and the corresponding weight coefficient, and obtaining the environmental color temperature value of the environment in which the display device is located, so as to facilitate accurate determination of the environmental color temperature value.

[0076] like Figure 3 As shown, according to some embodiments of the present invention, obtaining color feature information of a display device includes steps S21-S24:

[0077] S21, collecting initial spectrum information of XYZ channels of the display device;

[0078] S22, extracting features from the initial spectrum information to determine a number of color attribute features;

[0079] S23, sorting a number of color attribute features according to importance based on a principal component analysis algorithm, and selecting color attribute features whose importance is greater than a preset threshold as target color attribute features;

[0080] S24, input the target color attribute characteristics into a pre-built color neural model, and output the color feature information of the display device.

[0081] The working principle and beneficial effect of the above technical solution are as follows: In this embodiment, the initial spectral information of the XYZ channel of the display device is collected, including: performing spectral measurement based on a spectrophotometer, a spectrometer or other specialized measuring equipment, placing the display device in an environment with sufficient light to facilitate measuring the spectral information of its XYZ channel. The measuring device is started to measure the XYZ channels of the display device one by one. This will involve measuring the reflectivity or emissivity of each channel at different wavelengths, and the collected data will be converted into spectral information.

[0082] In this embodiment, based on the principal component analysis algorithm, several color attribute features are sorted according to importance, and color attribute features with importance greater than a preset threshold are screened out as target color attribute features, including: Principal component analysis (PCA) is a dimensionality reduction technology used to identify the main trends and patterns in data. When sorting the importance of color attribute features, PCA can find out the factors with the greatest influence and use them as target color attribute features. The correlation coefficient matrix between each color attribute feature is calculated, and the color attribute features are reduced in dimension using the PCA algorithm. PCA converts the linearly correlated variables in the original feature space into new linearly independent variables, namely, principal components, through orthogonal transformation. These principal components are sorted according to the size of the variance (i.e., importance), and the first principal component has the largest variance, and the order is reduced in sequence. The number of principal components to be retained is determined according to the cumulative contribution rate or a preset threshold. The cumulative contribution rate refers to the ratio of the sum of the variances of the first few principal components to the total variance, and the number of principal components whose cumulative contribution rate reaches a certain level (such as 85%) is usually selected. The retained principal components are explained to understand the combination of color attribute features they represent. This involves looking at the load matrix of the principal component to understand the weight of each original feature in the principal component. Based on the load matrix of the principal component and the preset threshold, the color attribute features with importance greater than the threshold are screened out. These features have a large weight in the principal component and are therefore considered to have an important influence on the color description.

[0083] In this embodiment, the target color attribute features are input into a pre-built color neural model, and the color feature information of the display device is output, including: the color neural model internally processes and calculates the received target color attribute features. Feature extraction, conversion and combination are performed through the hidden layer of the model to capture the complex relationship between the color attributes and the color features of the display device. After calculation and processing by the model, the model will output the color feature information of the display device, including color gamut coverage, color accuracy index, brightness distribution, etc. It is convenient to accurately determine the color feature information of the display device.

[0084] According to some embodiments of the present invention, feature extraction is performed on the initial spectral information to determine a number of color attribute features, including:

[0085] Based on the Mahalanobis distance algorithm, the abnormal information of the initial spectral information is screened out, and noise reduction is performed to obtain noise-reduced data;

[0086] The spectral data of the corresponding band of visible light color is selected from the denoised data and mapped into a spectral curve, and several color attribute features in the spectral curve are extracted.

[0087] Working principle and beneficial effects of the above technical solution: MahalanoZis distance is an effective method for measuring the similarity of two unknown sample sets. It takes into account the connection between various characteristics (for example, there may be correlation between the various bands of spectral data). Calculate MahalanoZis distance: For each data point in the initial spectral information, calculate its MahalanoZis distance with other points or a central point in the data set. Set a threshold: Set a threshold according to the distribution of the data and the statistical characteristics of the MahalanoZis distance. Screen out abnormal points: Data points whose MahalanoZis distance exceeds the threshold are regarded as abnormal information and screened out from the data set. Use a denoising algorithm to process the spectral data after the abnormal information is screened out, such as moving average filtering, median filtering, wavelet transform, etc. Select a suitable denoising algorithm according to the type and degree of noise in the data. Apply the selected denoising algorithm to the spectral data. Check the denoised data to ensure that both the noise is removed and the useful spectral information is retained. Select the spectral data corresponding to the band of visible light color and map it to a spectral curve: Map the denoised spectral data to a spectral curve in wavelength or frequency order. The approximate wavelength range of visible light is 400-700 nanometers. Select data within this wavelength range from the spectrum curve. Analyze the spectrum curve: Analyze the spectrum data of the selected visible light band and extract several color attribute features from the spectrum curve.

[0088] According to some embodiments of the present invention, the preset data table is a color temperature difference-color feature-LAB data comparison data table;

[0089] The method for obtaining the preset data table includes:

[0090] Obtain reference color information of display devices at different color temperature differences;

[0091] Create a preset data table based on the reference color information.

[0092] The working principle and beneficial effects of the above technical solution: 1. Determine the color temperature range and difference; set up the test environment to ensure that the test environment is stable and avoid interference from external light sources. At the same time, ensure that the display device is set to standard mode or calibration mode to obtain accurate results. Generate or select reference colors Select a set of representative reference colors that should cover the entire visible spectrum and be obvious and easy to distinguish for the human eye. You can use standard color samples in the color space, or customize colors according to specific needs. Measure color information under different color temperature differences, record data, and record the measurement data of each reference color under different color temperature differences. Ensure that the data is accurate and complete, and mark the corresponding color temperature value and reference color. Establish a preset data table Based on the recorded data, use spreadsheet software (such as Excel) or database software to establish a preset data table. Data verification and collation, verify the established data table to ensure the accuracy and consistency of the data. It is convenient to accurately establish a preset data table.

[0093] According to some embodiments of the present invention, after the display device is calibrated, the method further includes: testing the calibrated display device.

[0094] The beneficial effects of the above technical solution are: it is convenient to verify the correction effect and display effect of the calibrated display device, and it is convenient to improve the accuracy of color correction.

[0095] According to some embodiments of the present invention, before the display device is calibrated, it is also included to determine whether the display device is faulty, and when it is determined that the display device is faulty, a maintenance instruction is issued.

[0096] The working principle and beneficial effects of the above technical solution: Before the display device is calibrated, it also includes judging whether the display device is faulty, so as to correct inaccurate color correction, handle faults in time and improve maintenance efficiency.

[0097] According to some embodiments of the present invention, determining whether a display device is faulty includes:

[0098] Determine whether the display device is faulty based on a database generated by fault record data, where P fault record data exist in the database, each fault record data contains the values ​​of N indicators monitored by the display device at different operating faults, and an analysis matrix X is formed based on the values ​​of the N indicators of the P fault record data, the analysis matrix X has P rows and N columns, and each fault record data corresponds to whether an operating fault occurs, and a state vector Y formed by the faulty part, and the vector Y contains D1 values;

[0099] Perform feature learning on the analysis matrix X;

[0100] W11=rand(N,D1) W12=rand(N)

[0101]

[0102] Wherein, W11=rand(N, D1) is to generate a random matrix W11 with N rows and D1 columns; W12=rand(N) is to generate a random vector W12 with N elements whose values ​​are between 0 and 1; W1 is the result of normalizing the matrix W11; W2 is the result of normalizing the vector W12; sum is the sum, f(X i ) is used for X i The feature learning vector obtained by solving, X i is the value of the i-th row of the analysis matrix X; W2 e To solve each element in vector W2 to the power of e, e is a natural constant, To vector All elements in are compared with 0 and the larger value is taken, i = 1, 2, 3...P;

[0103] Construct a i )’s fault characteristic value analysis parameters:

[0104]

[0105] Where SS is the fault characteristic value analysis parameter; j≠Y i For j, take the vector Y except the Yth i All the remaining values ​​of Y i is the position of the value equal to the i-th value of vector Y1 in the middle of vector Y; f(X i ) j is the vector f(X i ), is the vector f(X i ) i value, W2 t is the value of the tth element of vector W2, W1 t，s is the value of the tth row and Sth column of the matrix W1, max() is the maximum value in the brackets, i=1, 2, 3...P, t=1, 2, 3...N, S=1, 2, 3...D1;

[0106] The current detection data of the display device is obtained, including the values ​​of N indicators, and the average value is taken, and compared with the fault characteristic value analysis parameter. When it is determined that the average value is greater than the fault characteristic value analysis parameter, it indicates that the display device has a fault; otherwise, it indicates that the display device has not a fault.

[0107] The working principle and beneficial effects of the above technical solution are as follows: judging whether a display device is faulty based on a database generated by fault record data, wherein there are P fault record data in the database, each fault record data contains the values ​​of N indicators monitored by the display device at different operating faults, and forming an analysis matrix X based on the values ​​of N indicators of the P fault record data, performing feature learning on the analysis matrix X, and constructing a fault characteristic value analysis parameter about: obtaining the current detection data of the display device, including the values ​​of N indicators, taking the average value, and comparing it with the fault characteristic value analysis parameter, when it is determined that the average value is greater than the fault characteristic value analysis parameter, it indicates that the display device is faulty; otherwise, it indicates that the display device is not faulty, which is convenient for improving the accuracy of judging whether the display device is faulty.

[0108] According to some embodiments of the present invention, determining color correction parameters according to the first color matrix and the second color matrix, and performing correction processing on the display device includes:

[0109] The first color matrix is ​​compared with the second color matrix, a difference parameter is determined as a color correction parameter, and correction processing is performed on the display device based on the color correction parameter.

[0110] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A display device calibration method with a built-in color management module, characterized in that: include: Obtaining the original color temperature value of the screen of the display device and the ambient color temperature value of the environment in which the display device is located, and calculating the color temperature difference data between the original color temperature value of the screen and the ambient color temperature value of the environment in which the display device is located; Get the color characteristic information of the display device; According to the color temperature difference data and color feature information, the preset data table is searched to determine the corresponding LAB data; Performing data conversion on the LAB data to obtain first XYZ data; obtaining a first color matrix according to the first XYZ data; Determine second XYZ data according to the color feature information; obtain a second color matrix according to the second XYZ data; Color correction parameters are determined according to the first color matrix and the second color matrix, and correction processing is performed on the display device.

2. The display device calibration method with built-in color management module according to claim 1, characterized in that: Determine the original screen color temperature value of the display device based on color analysis software.

3. The display device calibration method with built-in color management module according to claim 1, characterized in that: Get the ambient color temperature value of the display device's environment, including: Acquire an environmental image of the environment in which the display device is located; Perform image segmentation processing on the environment image to obtain several local images; obtain the XYZ color value of each pixel in the local image; the XYZ color value includes the X channel value, the Y channel value and the Z channel value; Calculate the average value of the X channel, the average value of the Y channel and the average value of the Z channel according to the XYZ color value of each pixel in the local image; Calculate the ratio of the average value of the X channel to the average value of the Y channel as the first ratio; calculate the ratio of the average value of the Z channel to the average value of the Y channel as the second ratio; use the ratio of the average value of the X channel to the average value of the Z channel as the third ratio; generate three-dimensional coordinates according to the first ratio, the second ratio and the third ratio, search in a preset three-dimensional color temperature model according to the three-dimensional coordinates, determine the color temperature value corresponding to the coordinate point, and use it as the color temperature value of the local image; Determine the ratio of the area of ​​the local image to the area of ​​the environment image as a weight coefficient corresponding to the local image; A weighted calculation is performed based on the color temperature value of each local image and the corresponding weight coefficient to obtain the ambient color temperature value of the environment in which the display device is located.

4. The display device calibration method with built-in color management module according to claim 1, characterized in that: Get the color characteristic information of the display device, including: Collect the initial spectrum information of the XYZ channels of the display device; Extract features from the initial spectral information and determine several color attribute features; Based on the principal component analysis algorithm, several color attribute features are sorted according to their importance, and color attribute features with importance greater than a preset threshold are selected as target color attribute features; The target color attribute features are input into the pre-built color neural model, and the color feature information of the display device is output.

5. The display device calibration method with built-in color management module according to claim 4, characterized in that: Extract features from the initial spectral information and determine several color attribute features, including: Based on the Mahalanobis distance algorithm, the abnormal information of the initial spectral information is screened out, and noise reduction is performed to obtain noise-reduced data; The spectral data of the corresponding band of visible light color is selected from the denoised data and mapped into a spectral curve, and several color attribute features in the spectral curve are extracted.

6. The display device calibration method with built-in color management module according to claim 1, characterized in that: The preset data table is the color temperature difference-color characteristics-LAB data comparison data table; The method for obtaining the preset data table includes: Obtain reference color information of display devices at different color temperature differences; Create a preset data table based on the reference color information.

7. The display device calibration method with built-in color management module according to claim 1, characterized in that: After the display device is calibrated, the method further includes: testing the calibrated display device.

8. The display device calibration method with built-in color management module according to claim 1, characterized in that: Before the display device is calibrated, it is also included to determine whether the display device is faulty, and when it is determined that the display device is faulty, a maintenance instruction is issued.

9. The display device calibration method with built-in color management module according to claim 8, characterized in that: Determine whether the display device is faulty, including: Determine whether the display device is faulty based on a database generated by fault record data, wherein there are P fault record data in the database, each fault record data contains the values ​​of N indicators monitored by the display device at different operating faults, and an analysis matrix X is formed based on the values ​​of the N indicators of the P fault record data, wherein the analysis matrix X has P rows and N columns, and each fault record data corresponds to whether an operating fault occurs, and a state vector Y formed by the faulty part, wherein the vector Y contains D1 values; Perform feature learning on the analysis matrix X; W11=rand(N,D1) W12=rand(N) Where, W11=rand(N,D1) is to generate a random matrix W11 with N rows and D1 columns; W12=rand(N) is to generate a random vector W12 with N elements whose values ​​are 0 to 1; W1 is the result of normalizing the matrix W11; W2 is the result of normalizing the vector W12; sum is the sum, f(X i ) is used for X i The feature learning vector obtained by solving, X i is the value of the i-th row of the analysis matrix X; W2 e To solve each element in vector W2 to the power of e, e is a natural constant, To vector All elements in are compared with 0 and the larger value is taken, i = 1, 2, 3...P; Construct a i )’s fault characteristic value analysis parameters: Where SS is the fault characteristic value analysis parameter; j≠Y i For j, take the vector Y except the Yth i All the remaining values ​​of Y i is the position of the value in vector Y that is equal to the i-th value of vector Y; f(X i ) j is the vector f(X i ), is the vector f(X i ) i value, W2 t is the value of the tth element of vector W2, W1 t,s is the value of the tth row and Sth column of the matrix W1, max() is the maximum value in the brackets, i = 1, 2, 3...P, t = 1, 2, 3...N, S = 1, 2, 3...D1; The current detection data of the display device is obtained, including the values ​​of N indicators, and the average value is taken, and compared with the fault characteristic value analysis parameter. When it is determined that the average value is greater than the fault characteristic value analysis parameter, it indicates that the display device has a fault; otherwise, it indicates that the display device has not a fault.

10. The display device calibration method with built-in color management module according to claim 1, characterized in that: Determining color correction parameters according to the first color matrix and the second color matrix, and performing correction processing on the display device, including: The first color matrix is ​​compared with the second color matrix, a difference parameter is determined as a color correction parameter, and correction processing is performed on the display device based on the color correction parameter.

Images