Color calibration method and system based on display driving chip

Abstract

The invention relates to a color calibration method and system based on a display driving chip, and the method comprises the following steps: carrying out the sub-pixel spectrum reconstruction of an original spectrum data stream based on a color gamut boundary vector group, and obtaining a sub-pixel spectrum matrix; performing white point chromaticity estimation on the sub-pixel spectrum matrix to obtain white point chromaticity coordinates; performing gamma curve correction on the sub-pixel spectrum matrix based on the white point chromaticity coordinates to obtain a gamma correction coefficient matrix; performing color space conversion on the gamma correction coefficient matrix through a preset three-dimensional lookup table to obtain color control parameters, and inputting the color control parameters into a preset display driving chip to drive sub-pixels of each pixel unit in the display panel to emit light to obtain color calibration display output, the defects of a traditional display driving chip in the aspect of color calibration are overcome, and the defects are mainly embodied in the technical problems that color differences between different display panels cannot be effectively processed and the color drift phenomenon occurs along with time lapse.

Description

Technical Field

[0001] The present invention relates to the technical field of display driver chips, and in particular to a color calibration method and system based on a display driver chip. Background Art

[0002] With the rapid development of display technology, consumers are increasingly demanding color expression and visual experience of display devices. However, traditional display driver chips have shortcomings in color calibration, mainly in that they cannot effectively handle color differences between different display panels and color drift over time. These problems not only affect users' visual enjoyment, but also bring challenges to manufacturers, especially in maintaining product consistency in multi-batch, large-scale production.

[0003] Further analysis found that existing color calibration methods often rely on fixed parameters or simple linear correction models, which leads to limitations in correction accuracy and scope of application. Especially when facing display panels with complex spectral characteristics, such as new generation display technologies such as OLED (organic light-emitting diode) or Micro LED, traditional methods find it difficult to accurately capture and correct the true luminous characteristics of each sub-pixel. In addition, these methods usually ignore the impact of ambient lighting conditions on display effects, which limits the color reproduction ability in practical applications.

[0004] In response to the above problems, researchers are committed to developing an advanced color calibration method based on display driver chips, aiming to improve the accuracy and adaptability of color calibration through precise spectral sampling, color gamut boundary extraction, and sub-pixel spectral reconstruction. This method can not only effectively solve the color drift problem caused by material aging, but also automatically adjust the color performance according to different usage environments, thereby providing a more stable and realistic color experience. This progress has opened up a new path for improving the overall performance of display devices, and also provided an important reference for the development of industry standards. Summary of the invention

[0005] The main purpose of the present invention is to provide a color calibration method and system based on a display driver chip, which solves the shortcomings of traditional display driver chips in color calibration, mainly manifested in the inability to effectively handle the color differences between different display panels and the technical problem of color drift occurring over time.

[0006] To achieve the above object, the present invention provides a color calibration method based on a display driver chip, comprising the following steps: Performing multi-region spectral sampling on the light signal emitted by the pixel unit array of the display panel to obtain an original spectral data stream; Extracting the color gamut boundary of the original spectral data stream by polynomial fitting to obtain a color gamut boundary vector group; Performing sub-pixel spectrum reconstruction on the original spectral data stream based on the color gamut boundary vector group to obtain a sub-pixel spectrum matrix; Performing white point chromaticity estimation on the sub-pixel spectrum matrix by using a Gaussian mixture model to obtain white point chromaticity coordinates; Performing gamma curve correction on the sub-pixel spectrum matrix based on the white point chromaticity coordinates to obtain a gamma correction coefficient matrix; The gamma correction coefficient matrix is ​​converted into color space through a preset three-dimensional lookup table to obtain color control parameters, and the color control parameters are input into a preset display driver chip to drive the sub-pixels of each pixel unit in the display panel to emit light, thereby obtaining a color calibrated display output.

[0007] Furthermore, the multi-region spectral sampling of the light signal emitted by the pixel unit array of the display panel to obtain the original spectral data stream includes: Using a preset microlens array to focus the optical signal on the pixel unit array of the display panel to obtain a focused optical signal, and performing optical fiber bundle splitting on the focused optical signal to obtain a multi-channel split optical signal; wherein the multi-channel split optical signal includes the pixel unit spectrum information; Performing photoelectric conversion on the multi-channel split light signals through a photodiode array to obtain a photoelectric conversion signal, and performing analog-to-digital conversion on the photoelectric conversion signal to obtain a digital spectrum signal; Performing wavelength calibration on the digital spectrum signal based on a preset wavelength calibration matrix to obtain calibration spectrum data, and performing dark current compensation on the calibration spectrum data to obtain compensated spectrum data; The spatial resolution of the compensated spectral data is enhanced based on a spatial interpolation algorithm to obtain an original spectral data stream.

[0008] Furthermore, the color gamut boundary extraction is performed on the original spectral data stream by polynomial fitting to obtain a color gamut boundary vector group, including: Performing chromaticity coordinate conversion on the original spectral data stream to obtain a chromaticity coordinate set, and performing color gamut segment sampling on the chromaticity coordinate set to obtain a color gamut sampling point sequence; Reconstructing the color gamut sampling point sequence into non-uniform sampling points to obtain a uniform sampling point sequence, and performing convex hull analysis on the uniform sampling point sequence to obtain color gamut boundary contour points; Performing cubic function fitting on the color gamut boundary contour points based on Chebyshev polynomials to obtain a piecewise fitting curve, and performing curvature analysis on the piecewise fitting curve to obtain color gamut boundary feature points; wherein the color gamut boundary feature points include curvature extreme point and inflection point information; Based on a preset Bernstein polynomial, polynomial coefficients are extracted from the color gamut boundary feature points to obtain a polynomial coefficient matrix, and the polynomial coefficient matrix is ​​orthogonalized to obtain an orthogonal basis vector group, and the orthogonal basis vector group is standardized by Gram-Schmidt orthogonalization to obtain a color gamut boundary vector group.

[0009] Furthermore, the sub-pixel spectrum reconstruction of the original spectral data stream based on the color gamut boundary vector group to obtain a sub-pixel spectrum matrix includes: Performing color gamut constraint on the original spectral data stream by using the color gamut boundary vector group to obtain color gamut constrained spectral data, and performing sub-pixel spectral separation on the color gamut constrained spectral data to obtain sub-pixel spectral components; wherein the sub-pixel spectral components include spectral information of the three primary colors of red, green and blue; Performing spectral basis expansion on the sub-pixel spectral components based on a preset spectral basis function to obtain spectral basis expansion coefficients, and performing orthogonal processing on the spectral basis expansion coefficients to obtain an orthogonal spectral basis coefficient matrix; Reconstructing the sub-pixel spectrum components by using the orthogonal spectral basis coefficient matrix to obtain a reconstructed sub-pixel spectrum; The reconstructed sub-pixel spectra are spectrally peak aligned to obtain aligned sub-pixel spectra, and the aligned sub-pixel spectra are spectrally matrixed to obtain a sub-pixel spectrum matrix; wherein the sub-pixel spectrum matrix includes red, green and blue primary color spectrum matrices.

[0010] Furthermore, the white point chromaticity estimation of the sub-pixel spectrum matrix by using a Gaussian mixture model to obtain white point chromaticity coordinates includes: Extracting spectral features from the sub-pixel spectral matrix to obtain a spectral feature vector group; Based on the Gaussian mixture model, the probability density of the spectral feature vector group is estimated to obtain the spectral probability density distribution, and the spectral probability density distribution is estimated by maximum likelihood to obtain the spectral clustering center; wherein the spectral clustering center includes the clustering centers of the three primary colors of red, green and blue; Performing chromaticity coordinate conversion on the spectral cluster centers to obtain a cluster center chromaticity coordinate set, and performing weighted averaging on the cluster center chromaticity coordinate set to obtain a weighted average chromaticity coordinate; Performing chromaticity space optimization on the weighted average chromaticity coordinates based on a preset Mahalanobis distance function to obtain optimized chromaticity coordinates, and performing color temperature matching and correction on the optimized chromaticity coordinates to obtain corrected chromaticity coordinates; White point fitting is performed on the corrected chromaticity coordinates to obtain a white point fitting curve, and chromaticity coordinates are extracted from the white point fitting curve to obtain white point chromaticity coordinates; wherein the white point chromaticity coordinates include white point coordinates of the three primary colors of red, green and blue.

[0011] Furthermore, performing gamma curve correction on the sub-pixel spectrum matrix based on the white point chromaticity coordinates to obtain a gamma correction coefficient matrix includes: Performing spectral energy distribution analysis on the sub-pixel spectral matrix based on the white point chromaticity coordinates to obtain a spectral energy distribution curve, and performing piecewise cubic spline interpolation on the spectral energy distribution curve to obtain a piecewise spectral energy curve; Performing spectral response linearization processing on the sub-pixel spectral matrix through the segmented spectral energy curve to obtain a linearized spectral response matrix, and performing chromatic adaptation transformation on the linearized spectral response matrix to obtain a chromatic adaptation spectral response matrix; Based on the chromatic adaptation spectral response matrix, the linearized spectral response matrix is ​​fitted with an inverse gamma function to obtain an inverse gamma function coefficient matrix, and the inverse gamma function coefficient matrix is ​​approximated by a polynomial to obtain an approximated gamma function coefficient matrix; Calculating the gamma value of the inverse gamma function coefficient matrix by using the approximated gamma function coefficient matrix to obtain a sub-pixel gamma value matrix; A gamma curve is generated based on the sub-pixel gamma value matrix to obtain a sub-pixel gamma correction curve, and a gamma correction coefficient matrix is ​​constructed based on the sub-pixel gamma correction curve.

[0012] Further, the color space conversion of the gamma correction coefficient matrix is ​​performed through a preset three-dimensional lookup table to obtain color control parameters, and the color control parameters are input into a preset display driver chip to drive the sub-pixels of each pixel unit in the display panel to emit light, thereby obtaining a color calibrated display output, including: Performing color space mapping on the gamma correction coefficient matrix through a preset three-dimensional lookup table to obtain an initial color mapping matrix; Performing color space conversion on the initial color mapping matrix through chromaticity uniformity processing to obtain uniform color space data, and performing color difference quantization on the uniform color space data to obtain quantized color difference data; Dynamic range adjustment is performed on the quantized color difference data by contrast enhancement to obtain adjusted color space data, and saturation correction is performed on the adjusted color space data to obtain corrected color space data; Performing reverse mapping based on the corrected color space data to obtain color control parameters, and performing pulse width modulation encoding on the color control parameters to obtain encoded color control parameters; The encoded color control parameters are input into a preset display driver chip to drive the sub-pixels of each pixel unit in the display panel to emit light, thereby obtaining a color calibrated display output.

[0013] The present invention also provides a color calibration device based on a display driver chip, comprising: A sampling module, used for performing multi-region spectral sampling on the light signal emitted by the pixel unit array of the display panel to obtain an original spectral data stream; An extraction module, used for extracting the color gamut boundary of the original spectral data stream by polynomial fitting to obtain a color gamut boundary vector group; A reconstruction module, configured to perform sub-pixel spectrum reconstruction on the original spectral data stream based on the color gamut boundary vector group to obtain a sub-pixel spectrum matrix; An estimation module, used for performing white point chromaticity estimation on the sub-pixel spectrum matrix through a Gaussian mixture model to obtain white point chromaticity coordinates; A correction module, used for performing gamma curve correction on the sub-pixel spectrum matrix based on the white point chromaticity coordinates to obtain a gamma correction coefficient matrix; The conversion module is used to perform color space conversion on the gamma correction coefficient matrix through a preset three-dimensional lookup table to obtain color control parameters, and input the color control parameters into a preset display driver chip to drive the sub-pixels of each pixel unit in the display panel to emit light, thereby obtaining a color calibrated display output.

[0014] The present invention also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the above methods when executing the computer program.

[0015] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above-mentioned methods are implemented.

[0016] The present invention provides a color calibration method based on a display driver chip, comprising the following steps: collecting audio digital signals at a target site through a preset multi-channel sensor to obtain a multi-dimensional audio data stream; performing multi-resolution spectrum decomposition on the multi-dimensional audio data stream to obtain hierarchical spectrum features; performing complex domain transformation on the hierarchical spectrum features to obtain complex spectrum representation data; reconstructing the complex spectrum representation data in the frequency domain through a sub-band energy analysis technique to obtain an acoustic parameter sequence; performing group delay compensation on the acoustic parameter sequence to obtain a calibrated spectrum sequence; performing reverse Fourier reconstruction on the calibrated spectrum sequence based on a preset segmented fast Fourier algorithm in a SOC chip to obtain an enhanced audio digital signal, thereby solving the shortcomings of traditional display driver chips in color calibration, which are mainly reflected in the inability to effectively handle the color differences between different display panels and the color drift phenomenon that occurs over time. The sub-pixel spectrum reconstruction of the original spectrum data stream based on the color gamut boundary vector group is realized to generate a sub-pixel spectrum matrix. This process enables the luminous characteristics of each sub-pixel to be accurately reconstructed, thereby significantly improving the color saturation and contrast of the displayed image. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a schematic diagram of steps of a color calibration method based on a display driver chip in one embodiment of the present invention; Figure 2 is a structural block diagram of a color calibration device based on a display driver chip in one embodiment of the present invention; Figure 3 It is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0018] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0020] like Figure 1 As shown, Figure 1 It is a schematic diagram of the steps of a color calibration method based on a display driver chip in one embodiment of the present invention; In one embodiment of the present invention, a color calibration method based on a display driver chip is provided, comprising the following steps: Step S1 , performing multi-region spectral sampling on a light signal emitted by a pixel unit array of a display panel to obtain an original spectral data stream.

[0021] Specifically, in the color calibration process of the display panel, multi-region spectral sampling of the light signal emitted by the pixel unit array is a crucial first step. This step is to obtain the original spectral data stream and provide basic data for subsequent processing. Specifically, the process first requires the entire display panel to be partitioned, with each region representing an independent sampling point. The selection of these regions needs to take into account the overall uniformity of the panel and the potential color differences to ensure that the collected data can fully reflect the true luminous characteristics of the panel. For example, in a high-resolution OLED display, dozens or even hundreds of sampling points may be selected to cover every corner and center area of ​​the screen to ensure that the spectral characteristics of each part can be accurately captured. Next, for each selected area, a professional spectrometer is used to measure the light signal. The spectrometer can decompose the intensity of light at different wavelengths to form a complete spectral curve. This curve contains rich information, such as parameters such as the main wavelength, color purity, and brightness, which constitute the so-called original spectral data stream. It is worth noting that in order to ensure the accuracy of the data, it is usually necessary to repeat the measurement multiple times under the same conditions and calculate the average value to reduce the influence of random errors. For example, when quality controlling a new batch of displays on a production line, this precise multi-region spectral sampling method can effectively identify and correct color deviation problems caused by manufacturing process differences. In addition, considering the complexity of actual application scenarios, such as changes in ambient lighting conditions or aging of materials after long-term use, the strategy of multi-region spectral sampling is particularly important. It not only helps to discover and quantify the impact of these factors on the display effect, but also provides key input for subsequent color gamut boundary extraction and sub-pixel spectral reconstruction. For example, in the design and debugging of high-end professional displays, engineers use this method to optimize color reproduction performance to ensure a consistent and high-quality visual experience in various working environments. In this way, not only can the market competitiveness of the product be improved, but also the user's demand for accurate color performance can be met. Therefore, multi-region spectral sampling of the light signal emitted by the pixel unit array of the display panel is not only the basis for achieving accurate color calibration, but also a key link in improving the overall display quality. In this process, meticulous regional division, precise spectral measurement and scientific data processing work together to ensure the effectiveness and reliability of the final color calibration results.

[0022] Step S2, extracting the color gamut boundary of the original spectral data stream by polynomial fitting to obtain a color gamut boundary vector group.

[0023] Specifically, in the color calibration process of the display panel, color gamut boundary extraction of the original spectral data stream by polynomial fitting is one of the most important steps. This process aims to identify the color gamut boundary from the complex spectral data and convert it into a color gamut boundary vector group that is convenient for subsequent processing. First, based on the original spectral data stream obtained in the previous step, these data contain the light intensity information at different wavelengths at each sampling point, forming a high-dimensional data set. In order to simplify and optimize these data, a polynomial fitting method is used to approximate the actual spectral distribution curve. Polynomial fitting is a mathematical technique that makes the fitting curve as close to the actual data point as possible by selecting the appropriate polynomial order and coefficients. Specifically, when applying polynomial fitting, the order of the fitting polynomial must first be determined, which is usually determined according to the complexity of the original spectral data and the required accuracy. For example, when processing the spectral data of an OLED display, considering that its luminous characteristics are relatively complex, a higher-order polynomial may be selected to ensure the fitting accuracy. Then, an optimization algorithm such as the least squares method is used to calculate the coefficients of the polynomial so that the sum of squares of the error between the fitting curve and the actual spectral data is minimized. The purpose of this is to accurately capture the characteristics of the light signal emitted by each pixel unit array, thereby laying the foundation for the accurate extraction of the color gamut boundary. Once the fitted polynomial function is obtained, the next task is to extract the color gamut boundary from it. The color gamut boundary refers to the limit range that can cover all possible color combinations. It is an important indicator to measure the color reproduction ability of the display device. At this stage, by analyzing the fitting curve, the color coordinate points representing the color gamut limit can be determined, and these points together constitute the color gamut boundary. For example, in a standard RGB display system, the color gamut boundary is usually defined by the maximum brightness value of the three primary colors of red, green, and blue and their mixed colors. These boundary points are organized into a set of vectors, namely the color gamut boundary vector group, to facilitate the sub-pixel spectrum reconstruction and gamma curve correction in the subsequent steps. To better understand the application scenario of this process, we can consider that on a high-end display production line, engineers need to ensure that each screen can provide consistent and high-quality color performance. Extracting the color gamut boundary of the original spectral data stream through polynomial fitting can not only help identify and quantify the color deviation introduced during the manufacturing process, but also provide key parameters for the subsequent calibration step. For example, when adjusting the color settings of a new batch of displays, this method can quickly locate and correct potential problems to ensure that the color reproduction performance of the final product meets the design standards. Therefore, color gamut boundary extraction achieved through polynomial fitting is not only a key step in improving display quality, but also an effective means to ensure product consistency and reliability. Throughout the process, precise data fitting and meticulous boundary extraction work together to ensure the effectiveness and accuracy of color calibration results.

[0024] Step S3: reconstruct sub-pixel spectra of the original spectral data stream based on the color gamut boundary vector group to obtain a sub-pixel spectrum matrix.

[0025] Specifically, sub-pixel spectrum reconstruction of the original spectral data stream based on the color gamut boundary vector group is one of the key steps in the display panel color calibration process, which aims to reconstruct the spectral characteristics of each sub-pixel through an accurate mathematical model to obtain a sub-pixel spectrum matrix. This process first relies on the color gamut boundary vector group obtained in the previous step. These vector groups contain the color coordinate points of the color gamut boundary and represent the maximum color range that the display device can reproduce. Next, using these color gamut boundary information, combined with the specific light intensity and wavelength distribution in the original spectral data stream, the spectral characteristics of each sub-pixel are finely reconstructed. In actual operation, it is first necessary to match the color gamut boundary vector group with the original spectral data stream to find the spectral feature area corresponding to each sub-pixel. For example, in an RGB display system, the red, green, and blue sub-pixels each have a unique spectral distribution. By analyzing these spectral distributions and mapping them to the space defined by the color gamut boundary vector group, the relative position and contribution of each sub-pixel within its color gamut boundary can be determined. Then, using appropriate mathematical models, such as linear combinations or nonlinear transformations, these spectral features are reorganized into a new set of spectral matrices, namely, sub-pixel spectral matrices. This matrix not only contains the light intensity information of each sub-pixel at different wavelengths, but also reflects their relationship with each other and their role in the entire color gamut. To better understand the application scenario of this process, we can consider a production line of high-end displays where engineers need to ensure that each screen can provide consistent and high-quality color performance. For example, when dealing with OLED displays, different batches of displays may have slight color differences due to the characteristics of their luminescent materials and the influence of the manufacturing process. By reconstructing the sub-pixel spectrum of the original spectral data stream based on the color gamut boundary vector group, these differences can be effectively identified and quantified. Suppose a batch of displays has a shift in the spectral distribution of the red sub-pixel, resulting in a slight color shift in the overall picture. With the above method, engineers can accurately locate the problem and correct this deviation by adjusting the spectral parameters of the red sub-pixel so that the color reproduction performance of the final product meets the design standard. In addition, this method can also be used to optimize the performance of display devices under various environmental conditions. For example, under different lighting conditions, the human eye's perception of color will change. Through sub-pixel spectral reconstruction, the display effect under different ambient light can be simulated, and the luminous characteristics of each sub-pixel can be adjusted accordingly to ensure a stable and realistic color experience in any environment. In short, sub-pixel spectrum reconstruction of the original spectral data stream based on the color gamut boundary vector group is not only a key technical means to improve display quality, but also an effective way to ensure product consistency and reliability. In this process, precise data matching and meticulous spectrum reconstruction work together to ensure the effectiveness and accuracy of color calibration results. In this way, not only can the user experience be improved, but also the market competitiveness of the product can be enhanced.

[0026] Step S4, performing white point chromaticity estimation on the sub-pixel spectrum matrix through a Gaussian mixture model to obtain white point chromaticity coordinates.

[0027] Specifically, estimating the white point chromaticity of the sub-pixel spectral matrix through a Gaussian mixture model is a crucial step in the color calibration process of the display panel. This process aims to use statistical methods to extract the chromaticity coordinates of the white point from complex spectral data, thereby providing basic parameters for subsequent gamma curve correction and color space conversion. Specifically, it is first necessary to use the sub-pixel spectral matrix obtained in the previous step, which contains the light intensity information of each sub-pixel at different wavelengths and their mutual relationship. The Gaussian mixture model (GMM) is a powerful probabilistic model that simulates complex data structures by representing the data set as a weighted sum of multiple Gaussian distributions. In practice, the first step in applying the Gaussian mixture model is to determine the appropriate number of Gaussian components and the initial parameter settings. This is usually optimized by experience or through techniques such as cross-validation. For example, when dealing with OLED displays, due to the complexity of its luminous characteristics, multiple Gaussian components may be required to accurately describe the spectral distribution of each sub-pixel. Next, the expectation maximization (EM) algorithm is used to iteratively update the model parameters so that the model can better fit the data in the sub-pixel spectral matrix. In this process, the Gaussian mixture model not only considers the spectral characteristics of each sub-pixel, but also integrates the interactions between them to generate a comprehensive and accurate spectral description. Once the Gaussian mixture model is trained and converged, it can be used to estimate the white point chromaticity of the sub-pixel spectral matrix. Specifically, by analyzing the probability distribution of the model output, the color coordinates that are most likely to represent the white point can be identified. This is because the white point is usually located in the center of the color gamut with a balanced ratio of the three primary colors of red, green, and blue. Therefore, through the Gaussian mixture model, these key points can be separated from the complex spectral data and mapped to a standard chromaticity diagram, such as the CIE 1931 xy chromaticity diagram, to obtain the white point chromaticity coordinates. To better understand the application scenario of this process, we can consider that on a production line of high-end monitors, engineers need to ensure that each screen can provide consistent and high-quality color performance. For example, when adjusting the color settings of a new batch of monitors, it may be found that some screens have slight deviations in white display, causing the picture to look yellowish or bluish. By estimating the white point chromaticity of the sub-pixel spectral matrix through a Gaussian mixture model, engineers can quickly locate the problem and correct the deviation by adjusting relevant parameters. Suppose a batch of displays has a shift in the spectral distribution of the green sub-pixel, resulting in a slight green shift in the overall picture. Through the above method, engineers can accurately identify the change in the white point chromaticity coordinates and adjust the luminous characteristics of the green sub-pixel accordingly, so that the color reproduction performance of the final product meets the design standards. In addition, this method can also be used to optimize the performance of display devices under various environmental conditions. For example, under different lighting conditions, the human eye's perception of color changes.Through white point chromaticity estimation, the display effects under different ambient light conditions can be simulated, and the luminous characteristics of each sub-pixel can be adjusted accordingly to ensure a stable and realistic color experience in any environment. In short, using a Gaussian mixture model to estimate the white point chromaticity of the sub-pixel spectral matrix is ​​not only a key technical means to improve display quality, but also an effective way to ensure product consistency and reliability. In this process, precise data modeling and meticulous chromaticity estimation work together to ensure the effectiveness and accuracy of color calibration results. In this way, not only can the user experience be improved, but the market competitiveness of the product can also be enhanced.

[0028] Step S5, performing gamma curve correction on the sub-pixel spectrum matrix based on the white point chromaticity coordinates to obtain a gamma correction coefficient matrix.

[0029] Specifically, gamma curve correction of the sub-pixel spectrum matrix based on the white point chromaticity coordinates is one of the key steps in the color calibration process of the display panel. It aims to optimize the display effect through precise mathematical adjustments to ensure the consistency and accuracy of colors at different brightness levels. First, the white point chromaticity coordinates obtained in the previous step need to be used. These coordinates represent the ideal position of white performance under standard conditions of the display device. The white point chromaticity coordinates not only reflect the color balance state of the display at the highest brightness, but also provide a benchmark reference point for the subsequent gamma curve correction. During the correction process, the white point chromaticity coordinates are first combined with the sub-pixel spectrum matrix to analyze the response characteristics of each sub-pixel at different brightness levels. Since the human eye's perception of brightness changes is nonlinear, directly using the original spectral data will cause color distortion or unevenness in low brightness areas. To overcome this challenge, gamma curve correction uses a nonlinear transformation method to adjust the output brightness value of each sub-pixel to maintain a consistent visual experience across the entire brightness range. Specifically, the gamma correction coefficient for each sub-pixel is calculated based on the standard brightness response curve determined by the white point chromaticity coordinates. These coefficients are organized into a matrix, the gamma correction coefficient matrix, which is used to guide subsequent brightness adjustments. In actual operation, the generation of the gamma correction coefficient matrix is ​​an iterative optimization process. First, the initial gamma correction coefficients are calculated based on the white point chromaticity coordinates and the preset target gamma value (usually 2.2). Then, by simulating brightness adjustments on the sub-pixel spectrum matrix and comparing it with the target brightness response curve, the gamma correction coefficients are gradually optimized so that the actual brightness response is as close to the ideal state as possible. For example, when dealing with OLED displays, due to their self-luminous characteristics, the sub-pixel responses at different brightness levels vary greatly, so more sophisticated gamma correction is required to ensure color consistency. In this way, color shift and brightness unevenness in low-brightness areas can be effectively reduced, and the overall display quality can be improved. To better understand the application scenario of this process, we can consider that on a high-end display production line, engineers need to ensure that each screen can provide consistent and high-quality color performance. For example, when debugging a new batch of displays, it may be found that some screens show slight color deviations or brightness unevenness in low-brightness areas. By performing gamma curve correction on the sub-pixel spectral matrix based on the white point chromaticity coordinates, engineers can quickly locate and correct these problems. Suppose a batch of displays has an offset in the brightness response of the blue sub-pixel, causing the picture to show a slight blue offset in low-brightness areas. Through the above method, engineers can accurately calculate the new gamma correction coefficient and apply it to the brightness adjustment of the blue sub-pixel, so that the color reproduction performance of the final product meets the design standard. In addition, this method can also be used to optimize the performance of display devices under various environmental conditions.For example, under different lighting conditions, the human eye's perception of brightness and color will change. Through gamma curve correction, the display effect under different ambient light can be simulated, and the brightness response of each sub-pixel can be adjusted accordingly to ensure a stable and realistic color experience in any environment. In short, gamma curve correction of the sub-pixel spectrum matrix based on the white point chromaticity coordinates is not only a key technical means to improve display quality, but also an effective way to ensure product consistency and reliability. In this process, precise data adjustment and meticulous brightness optimization work together to ensure the effectiveness and accuracy of color calibration results. In this way, not only can the user experience be improved, but also the market competitiveness of the product can be enhanced.

[0030] Step S6, performing color space conversion on the gamma correction coefficient matrix through a preset three-dimensional lookup table to obtain color control parameters, and inputting the color control parameters into a preset display driver chip to drive the sub-pixels of each pixel unit in the display panel to emit light, thereby obtaining a color calibrated display output.

[0031] Specifically, color space conversion of the gamma correction coefficient matrix through a preset three-dimensional lookup table is the last step in the color calibration process of the display panel, which aims to convert the data after gamma curve correction into practical color control parameters, and finally drive the sub-pixels of each pixel unit in the display panel to emit light. This step first relies on the gamma correction coefficient matrix obtained in the previous step. These matrices contain the correction coefficients of each sub-pixel at different brightness levels to ensure the consistency and accuracy of the color. Specifically, a three-dimensional lookup table (3D LUT) is a powerful tool for color space conversion, which can map the input color value to the output color value, thereby achieving accurate color calibration and optimization. When applying a three-dimensional lookup table, it is first necessary to pre-generate a suitable lookup table according to the specific characteristics of the display. This lookup table is usually based on a standard color space (such as sRGB or Adobe RGB) and is optimized for its accuracy through a series of tests and adjustments. For example, when dealing with high-end OLED displays, due to their complex spectral characteristics and high dynamic range (HDR) capabilities, a very sophisticated three-dimensional lookup table may be required to ensure the accuracy and consistency of color conversion. In the calibration process, the gamma correction coefficient matrix is ​​used as input and the color space conversion is performed through a three-dimensional lookup table. A three-dimensional lookup table is essentially a multidimensional array in which each element corresponds to a specific input color value and its corresponding output color value. By querying this table, the values ​​in the gamma correction coefficient matrix can be quickly and accurately converted into color control parameters suitable for the display device. For example, in a typical RGB display system, the three-dimensional lookup table will map the input values ​​of the red, green and blue channels to new output values ​​to ensure the consistency of color performance across the entire color gamut. Once the color space conversion is completed, the obtained color control parameters will be input into the preset display driver chip. The display driver chip is responsible for receiving these parameters and converting them into electrical signals, which in turn drive each pixel unit in the display panel to emit light. In this process, the display driver chip not only needs to process a large amount of data, but also needs to ensure the accuracy and synchronization of the signal to avoid problems such as color distortion or flicker. For example, in a production line of high-end monitors, engineers can use this method to ensure that each screen can provide consistent and high-quality color performance. Assuming that a batch of monitors has slight color deviations in certain specific color areas, the color space conversion through the three-dimensional lookup table can accurately adjust the performance of these color areas so that the color reproduction performance of the final product meets the design standards. In addition, this method can also be used to optimize the performance of display devices under various environmental conditions. For example, the human eye's perception of color changes under different lighting conditions.By converting the color space through a three-dimensional lookup table, the display effects under different ambient lights can be simulated, and the luminous characteristics of each sub-pixel can be adjusted accordingly to ensure a stable and realistic color experience in any environment. In short, converting the gamma correction coefficient matrix to a color space through a preset three-dimensional lookup table, and inputting the color control parameters into the display driver chip to drive the sub-pixels of each pixel unit in the display panel to emit light, is not only a key technical means to improve display quality, but also an effective way to ensure product consistency and reliability. In this process, precise data conversion and meticulous color control work together to ensure the effectiveness and accuracy of the color calibration results. In this way, not only can the user experience be improved, but also the market competitiveness of the product can be enhanced.

[0032] In a specific embodiment, the multi-region spectral sampling of the light signal emitted by the pixel unit array of the display panel to obtain the original spectral data stream includes: Using a preset microlens array to focus the optical signal on the pixel unit array of the display panel to obtain a focused optical signal, and performing optical fiber bundle splitting on the focused optical signal to obtain a multi-channel split optical signal; wherein the multi-channel split optical signal includes the pixel unit spectrum information; Performing photoelectric conversion on the multi-channel split light signals through a photodiode array to obtain a photoelectric conversion signal, and performing analog-to-digital conversion on the photoelectric conversion signal to obtain a digital spectrum signal; Performing wavelength calibration on the digital spectrum signal based on a preset wavelength calibration matrix to obtain calibration spectrum data, and performing dark current compensation on the calibration spectrum data to obtain compensated spectrum data; The spatial resolution of the compensated spectral data is enhanced based on a spatial interpolation algorithm to obtain an original spectral data stream.

[0033] Specifically, in the color calibration process of the display panel, multi-region spectral sampling of the light signal emitted by the pixel unit array is a crucial first step. Specifically, this step includes using a preset microlens array to focus the light signal of the pixel unit array of the display panel to obtain a focused light signal, and decomposing these light signals into multiple split light signals through fiber bundle splitting technology. Each split light signal contains the spectral information of a specific pixel unit, thereby providing basic data for subsequent processing. First, the role of the microlens array is to accurately focus the light signal emitted by each pixel unit on the display panel. This focusing not only improves the intensity of the light signal, but also ensures the accuracy of the subsequent split light process. For example, in high-end OLED display screens, due to their self-luminous characteristics, the light signal of each pixel unit has a unique spectral distribution. Through the microlens array, these light signals can be effectively concentrated on the fiber bundle, further improving the accuracy and efficiency of sampling. Next, the fiber bundle splitting technology decomposes the focused light signal into multiple split light signals. Each split light signal corresponds to a specific wavelength range, so that the light intensity information at multiple wavelengths can be obtained at the same time. In order to convert these optical signals into electrical signals, a photodiode array is used for photoelectric conversion. The photodiode can convert the optical signal into a current signal, which is then converted into a digital spectral signal through an analog-to-digital converter (ADC). This process not only retains all the information of the original optical signal, but also facilitates subsequent data processing and analysis. After obtaining the digital spectral signal, it needs to be calibrated for wavelength. Wavelength calibration is achieved through a preset wavelength calibration matrix, which is usually pre-generated based on the spectral characteristics of a standard light source. The process of wavelength calibration can be expressed by the following formula: ,in, is the calibration spectrum data, is the wavelength calibration matrix, is the original digital spectrum signal. In this way, the wavelength offset problem caused by equipment characteristics and environmental factors can be eliminated to ensure the accuracy of the spectrum data. Next, the calibrated spectrum data is dark current compensated. Dark current refers to the background current generated by the photodiode when there is no light, which will affect the accuracy of the measurement results. By recording and subtracting the dark current value, noise interference can be effectively removed. Assuming the dark current is , then compensate the spectral data It can be expressed as: ; Finally, in order to improve the spatial resolution, the spatial interpolation algorithm is used to enhance the compensated spectral data. The spatial interpolation algorithm uses the information of neighboring pixels to supplement missing or incomplete data points, thereby improving the overall spatial resolution. Common spatial interpolation methods include bilinear interpolation, cubic interpolation, etc. For example, bilinear interpolation can be calculated using the following formula: ;in, is the interpolated spectral data, is the spectral data of neighboring pixels, is the corresponding weight coefficient. In this way, a more refined and accurate raw spectral data stream can be obtained. To better understand the application scenario of this process, we can consider that in a high-end display production line, engineers need to ensure that each screen can provide consistent and high-quality color performance. For example, when debugging a new batch of displays, it may be found that some screens have slight color deviations or uneven brightness in certain color areas. Through the above multi-region spectral sampling method, the specific causes of these problems can be accurately identified and corrected by adjusting relevant parameters. Suppose a batch of displays has a deviation in the spectral distribution of red pixels, resulting in a slight red shift in the picture. Through a series of steps such as microlens array focusing, fiber bundle splitting, photoelectric conversion, wavelength calibration, dark current compensation, and spatial interpolation, engineers can accurately extract the true spectral characteristics of red pixels and adjust their luminous characteristics accordingly, so that the color reproduction performance of the final product meets the design standards. In this way, not only can the user experience be improved, but also the market competitiveness of the product can be enhanced.

[0034] In a specific embodiment, the color gamut boundary extraction is performed on the original spectral data stream by polynomial fitting to obtain a color gamut boundary vector group, including: Performing chromaticity coordinate conversion on the original spectral data stream to obtain a chromaticity coordinate set, and performing color gamut segment sampling on the chromaticity coordinate set to obtain a color gamut sampling point sequence; Reconstructing the color gamut sampling point sequence into non-uniform sampling points to obtain a uniform sampling point sequence, and performing convex hull analysis on the uniform sampling point sequence to obtain color gamut boundary contour points; Performing cubic function fitting on the color gamut boundary contour points based on Chebyshev polynomials to obtain a piecewise fitting curve, and performing curvature analysis on the piecewise fitting curve to obtain color gamut boundary feature points; wherein the color gamut boundary feature points include curvature extreme point and inflection point information; Based on a preset Bernstein polynomial, polynomial coefficients are extracted from the color gamut boundary feature points to obtain a polynomial coefficient matrix, and the polynomial coefficient matrix is ​​orthogonalized to obtain an orthogonal basis vector group, and the orthogonal basis vector group is standardized by Gram-Schmidt orthogonalization to obtain a color gamut boundary vector group.

[0035] Specifically, in the color calibration process of the display panel, color gamut boundary extraction of the original spectral data stream by polynomial fitting is one of the key steps, which aims to identify the color gamut boundary from the complex spectral data and convert it into a color gamut boundary vector group for subsequent processing. This process first relies on the original spectral data stream obtained in the previous step, which contains detailed information about the light signals emitted by each pixel unit array. First, the original spectral data stream is converted into a chromaticity coordinate set. This step is usually based on the CIE 1931 xy chromaticity diagram or other standard chromaticity space, converting the light intensity at each wavelength into the corresponding chromaticity coordinates. For example, in an RGB display system, the three sub-pixels of red, green, and blue each have a unique spectral distribution, and these distributions can be mapped to specific positions in the standard chromaticity space through chromaticity coordinate conversion. Next, the chromaticity coordinate set is subjected to color gamut segment sampling to obtain a sequence of color gamut sampling points. The purpose of color gamut segment sampling is to ensure uniform coverage across the entire color gamut range and avoid data in certain areas being too dense or sparse. Assume that in a high-end OLED display, due to the influence of its luminescent materials and manufacturing process, the spectral characteristics of different color regions may vary significantly, so the sampling points need to be carefully designed to capture these changes. Then, the color gamut sampling point sequence is reconstructed by non-uniform sampling points to obtain a uniform sampling point sequence. Non-uniform sampling point reconstruction is a mathematical technique that converts irregularly distributed sampling points into regularly distributed sampling points through interpolation and resampling methods. This method can not only improve the consistency of the data, but also simplify the subsequent processing steps. For example, when processing certain specific color regions, it may be found that the sampling points are unevenly distributed, resulting in some details being lost or over-amplified. Through non-uniform sampling point reconstruction, these problems can be effectively solved to ensure data quality across the entire color gamut. Next, the uniform sampling point sequence is subjected to convex hull analysis to obtain the color gamut boundary contour points. Convex hull analysis is a geometric algorithm used to determine the minimum convex polygon boundary of a set of points. In this process, by analyzing the uniform sampling point sequence, key points located at the edge of the color gamut can be identified, which together constitute the color gamut boundary contour. For example, in a typical RGB display system, the color gamut boundary is usually defined by the maximum brightness value of the three primary colors of red, green, and blue and their mixed colors. Through convex hull analysis, these key points can be accurately extracted and organized into an ordered sequence. Based on the Chebyshev polynomials, cubic functions are fitted to the color gamut boundary contour points to obtain a piecewise fitting curve, and the curvature analysis of the piecewise fitting curve is performed to obtain the color gamut boundary feature points. Chebyshev polynomials are a special kind of orthogonal polynomials with good numerical stability and approximation performance. By selecting appropriate orders and coefficients, a series of cubic functions can be constructed to approximate the color gamut boundary contour points. For example, when dealing with complex-shaped color gamut boundaries, it may be necessary to use multiple cubic functions for piecewise fitting to ensure the smoothness and continuity of the overall curve.Next, the curvature analysis of the segmented fitting curve is performed to identify characteristic points such as curvature extreme points and inflection points. These characteristic points not only reflect the main form of the color gamut boundary, but also provide important references for the subsequent polynomial coefficient extraction. Based on the preset Bernstein polynomial, the polynomial coefficients of the color gamut boundary feature points are extracted to obtain the polynomial coefficient matrix, and the polynomial coefficient matrix is ​​orthogonalized to obtain an orthogonal basis vector group, and the orthogonal basis vector group is standardized by Gram-Schmidt orthogonalization to obtain the color gamut boundary vector group. Bernstein polynomial is a commonly used basis function, which is particularly suitable for parametric representation of curves and surfaces. In this process, the corresponding polynomial coefficient matrix is ​​first calculated according to the color gamut boundary feature points. Then, the polynomial coefficient matrix is ​​processed using the orthogonalization method in linear algebra (such as Gram-Schmidt orthogonalization) to generate a set of orthogonal basis vectors. These orthogonal basis vectors are not only independent of each other, but also can effectively represent the overall form of the color gamut boundary. To better understand the application scenario of this process, we can consider a production line of high-end monitors, where engineers need to ensure that each screen can provide consistent and high-quality color performance. For example, when debugging a new batch of monitors, it may be found that some screens have slight color deviations or incomplete color gamuts in certain color areas. Through the above-mentioned color gamut boundary extraction method, the specific causes of these problems can be accurately identified, and these deviations can be corrected by adjusting relevant parameters. Suppose a batch of monitors has a shift in the spectral distribution of the green sub-pixel, resulting in a slight green shift in the overall picture. Through a series of steps such as chromaticity coordinate conversion, color gamut segmented sampling, non-uniform sampling point reconstruction, convex hull analysis, Chebyshev polynomial fitting, and Bernstein polynomial coefficient extraction, engineers can accurately extract the true color gamut boundary of the green sub-pixel and adjust its luminous characteristics accordingly, so that the color reproduction performance of the final product meets the design standard.

[0036] In a specific embodiment, the sub-pixel spectrum reconstruction of the original spectral data stream based on the color gamut boundary vector group to obtain a sub-pixel spectrum matrix includes: Performing color gamut constraint on the original spectral data stream by using the color gamut boundary vector group to obtain color gamut constrained spectral data, and performing sub-pixel spectral separation on the color gamut constrained spectral data to obtain sub-pixel spectral components; wherein the sub-pixel spectral components include spectral information of the three primary colors of red, green and blue; Performing spectral basis expansion on the sub-pixel spectral components based on a preset spectral basis function to obtain spectral basis expansion coefficients, and performing orthogonal processing on the spectral basis expansion coefficients to obtain an orthogonal spectral basis coefficient matrix; Reconstructing the sub-pixel spectrum components by using the orthogonal spectral basis coefficient matrix to obtain a reconstructed sub-pixel spectrum; The reconstructed sub-pixel spectra are spectrally peak aligned to obtain aligned sub-pixel spectra, and the aligned sub-pixel spectra are spectrally matrixed to obtain a sub-pixel spectrum matrix; wherein the sub-pixel spectrum matrix includes red, green and blue primary color spectrum matrices.

[0037] Specifically, in the color calibration process of the display panel, sub-pixel spectrum reconstruction of the original spectral data stream based on the color gamut boundary vector group is one of the key steps, aiming to reconstruct the spectral characteristics of each sub-pixel through an accurate mathematical model, thereby obtaining a sub-pixel spectrum matrix. This process not only improves the color reproduction capability of the display device, but also ensures the consistency and reliability of the product. First, the original spectral data stream is constrained by the color gamut boundary vector group to obtain the color gamut constrained spectral data. Suppose we have a high-end OLED display whose original spectral data stream contains data of 1024 wavelength points. After the constraint processing of the color gamut boundary vector group, we limit these data to a standard color gamut range, such as the sRGB color gamut. In this process, 5% of the data points (about 51 points) may be adjusted or removed because they are out of the color gamut range to ensure that all colors are within the reproducible range. Next, the color gamut constrained spectral data is subjected to sub-pixel spectral separation to obtain sub-pixel spectral components. Sub-pixel spectral separation decomposes the composite spectral data into red, green, and blue primary color spectral information. Assuming that the data length of each wavelength point in the original spectral data stream is 1024, after separation, we can get three independent spectral components, each of which still contains data of 1024 wavelength points. For example, when processing a batch of displays, it is found that the spectral distribution of the red sub-pixel is slightly offset, resulting in a slight red offset on the screen. Through sub-pixel spectral separation, this offset can be accurately identified and separated into independent red spectral components, providing basic data for further adjustment. Then, the sub-pixel spectral components are spectrally expanded based on the preset spectral basis functions to obtain the spectral basis expansion coefficients. Assume that we choose to use Fourier basis functions for expansion and select the first 10-order basis functions to approximate the spectral data of each sub-pixel. This means that each sub-pixel spectral component will be decomposed into a linear combination of 10 basis functions. For each sub-pixel (red, green, and blue), we need to calculate 10 spectral basis expansion coefficients, and a total of 30 coefficients need to be calculated. These coefficients can be efficiently calculated by the least squares method or other optimization algorithms. Next, the spectral basis expansion coefficients are orthogonalized to obtain an orthogonal spectral basis coefficient matrix. Orthogonalization can be achieved through methods such as QR decomposition to generate a set of independent and orthogonal basis coefficient matrices. Suppose we obtain a 30x30 orthogonal matrix through QR decomposition, which contains the orthogonal basis coefficients of all sub-pixels. This method can not only simplify the subsequent processing steps, but also improve the computational efficiency. The sub-pixel spectral components are spectrally reconstructed through the orthogonal spectral basis coefficient matrix to obtain the reconstructed sub-pixel spectrum. Assuming that the spectral data of each sub-pixel is composed of 10 basis functions, the reconstructed spectral data will have higher smoothness and continuity. For example, when processing certain specific color areas, it may be found that the sampling points are unevenly distributed, resulting in the loss or over-amplification of some details.Through spectral reconstruction, these problems can be effectively solved to ensure the data quality in the entire color gamut. Next, the reconstructed sub-pixel spectrum is spectrally aligned to obtain an aligned sub-pixel spectrum. Assume that the spectral peaks of the red, green, and blue sub-pixels are located at different wavelengths, for example, the peak of the red sub-pixel is at 620nm, the peak of the green sub-pixel is at 530nm, and the peak of the blue sub-pixel is at 460nm. Through spectral peak alignment, it can be ensured that these peaks are aligned at the same wavelength position, thereby improving the consistency and accuracy of the color. Specifically, assuming that the peak of the red sub-pixel needs to be moved by 10nm, the peak of the green sub-pixel needs to be moved by 5nm, and the peak of the blue sub-pixel needs to be moved by 2nm, this can be achieved through appropriate mathematical transformations. Finally, the aligned sub-pixel spectrum is spectrally matrixed to obtain a sub-pixel spectral matrix. Assuming that the spectral data of each sub-pixel contains data at 1024 wavelength points, these data can be organized into a unified structure through spectral matrixing. For example, for a typical RGB display system, the generated sub-pixel spectrum matrix is ​​a 1024x3 matrix, where each column corresponds to the spectral data of a primary color (red, green, and blue). In this way, subsequent gamma curve correction and color space conversion can be easily performed. To better understand the application scenario of this process, we can consider a high-end display production line where engineers need to ensure that each screen can provide consistent and high-quality color performance. For example, when debugging a new batch of displays, it may be found that some screens have slight color deviations or uneven brightness in certain color areas. Through the above sub-pixel spectrum reconstruction method, the specific causes of these problems can be accurately identified and corrected by adjusting relevant parameters. Suppose a batch of displays has a shift in the spectral distribution of the green sub-pixel, resulting in a slight green shift in the overall picture. Through a series of steps such as color gamut constraint, sub-pixel spectrum separation, spectral basis expansion, spectral reconstruction, spectral peak alignment, and spectral matrixing, engineers can accurately extract the true spectral characteristics of the green sub-pixel and adjust its luminous characteristics accordingly, so that the color reproduction performance of the final product meets the design standard. Through these detailed steps and digital verification, the color reproduction capability and consistency of display devices can be ensured, improving user experience and enhancing the market competitiveness of products.

[0038] In a specific embodiment, the step of estimating the white point chromaticity of the sub-pixel spectrum matrix by using a Gaussian mixture model to obtain the white point chromaticity coordinates includes: Extracting spectral features from the sub-pixel spectral matrix to obtain a spectral feature vector group; Based on the Gaussian mixture model, the probability density of the spectral feature vector group is estimated to obtain the spectral probability density distribution, and the spectral probability density distribution is estimated by maximum likelihood to obtain the spectral clustering center; wherein the spectral clustering center includes the clustering centers of the three primary colors of red, green and blue; Performing chromaticity coordinate conversion on the spectral cluster centers to obtain a cluster center chromaticity coordinate set, and performing weighted averaging on the cluster center chromaticity coordinate set to obtain a weighted average chromaticity coordinate; Performing chromaticity space optimization on the weighted average chromaticity coordinates based on a preset Mahalanobis distance function to obtain optimized chromaticity coordinates, and performing color temperature matching and correction on the optimized chromaticity coordinates to obtain corrected chromaticity coordinates; White point fitting is performed on the corrected chromaticity coordinates to obtain a white point fitting curve, and chromaticity coordinates are extracted from the white point fitting curve to obtain white point chromaticity coordinates; wherein the white point chromaticity coordinates include white point coordinates of the three primary colors of red, green and blue.

[0039] Specifically, in the color calibration process of the display panel, the white point chromaticity estimation of the sub-pixel spectrum matrix through the Gaussian mixture model is one of the key steps, which aims to extract and optimize the spectral characteristics of each sub-pixel through an accurate mathematical model to obtain the white point chromaticity coordinates. This process not only improves the color reproduction ability of the display device, but also ensures the consistency and reliability of the product. First, the sub-pixel spectrum matrix is ​​subjected to spectral feature extraction to obtain a group of spectral feature vectors. Spectral feature extraction is a technique for identifying representative feature vectors from complex spectral data. Suppose we have a high-end OLED display whose sub-pixel spectrum matrix contains data at 1024 wavelength points. By performing spectral feature extraction on this data, a set of feature vectors describing the spectral characteristics of each sub-pixel (red, green, and blue) can be obtained. For example, when processing a batch of displays, it was found that some screens had slight color deviations or unevenness in specific color areas. Through spectral feature extraction, these differences can be effectively identified and converted into feature vectors to provide basic data for further analysis. Next, the probability density of the spectral feature vector group is estimated based on the Gaussian mixture model to obtain the spectral probability density distribution, and the maximum likelihood estimation is performed on the spectral probability density distribution to obtain the spectral cluster center. The Gaussian mixture model (GMM) is a commonly used probability model used to describe the probability distribution of data. In this process, the appropriate Gaussian mixture model parameters are first selected, and the corresponding cluster centers are calculated by maximum likelihood estimation. Assume that when we are dealing with a typical RGB display system, the spectral feature vector group contains data of 1024 wavelength points. Through the Gaussian mixture model, these data can be divided into three clusters, corresponding to the spectral characteristics of the red, green and blue sub-pixels, respectively. Then, through maximum likelihood estimation, the cluster centers of each cluster can be identified, and these cluster centers represent the main spectral characteristics of each primary color. Then, the chromaticity coordinates of the spectral cluster centers are converted to obtain the cluster center chromaticity coordinates set, and the cluster center chromaticity coordinates set is weighted averaged to obtain the weighted average chromaticity coordinates. Chromaticity coordinate conversion is a technique used to map spectral data to a standard chromaticity space (such as the CIE 1931 xy chromaticity diagram). Suppose we are dealing with a high-end OLED display, where the spectral cluster centers contain cluster centers of three primary colors (red, green, and blue). Through chromaticity coordinate conversion, these cluster centers can be mapped to specific locations in the standard chromaticity space. Then, by weighted averaging these cluster centers, a weighted average chromaticity coordinate can be obtained, which represents the comprehensive chromaticity characteristics of the entire display system. For example, when dealing with certain specific color areas, it may be found that the differences between certain colors are large, resulting in visual inconsistency. Through weighted averaging, these differences can be effectively balanced to ensure color consistency and accuracy.Next, the weighted average chromaticity coordinates are optimized in chromaticity space based on the preset Mahalanobis distance function to obtain optimized chromaticity coordinates, and the optimized chromaticity coordinates are color temperature matched and corrected to obtain corrected chromaticity coordinates. The Mahalanobis distance function is a method for measuring the distance between two points, which is particularly suitable for spatial analysis of multidimensional data. Suppose that when we are dealing with a high-end OLED display, the weighted average chromaticity coordinates contain the coordinates of three primary colors (red, green, and blue). Through the Mahalanobis distance function, the distribution of these coordinates can be optimized so that they are more uniform and consistent throughout the chromaticity space. Then, by color temperature matching and correction of the optimized chromaticity coordinates, it is possible to ensure that the color temperature remains consistent throughout the display system. For example, when dealing with certain specific color areas, it may be found that the color temperature of some colors is too high or too low, resulting in visual distortion. Through color temperature matching and correction, these problems can be effectively solved to ensure color consistency and accuracy. Next, the corrected chromaticity coordinates are white point fitted to obtain a white point fitting curve, and the chromaticity coordinates are extracted from the white point fitting curve to obtain the white point chromaticity coordinates. White point fitting is a technique used to extract the ideal white point coordinates from the corrected chromaticity coordinates. Assuming that we are dealing with a typical RGB display system, the corrected chromaticity coordinates contain the coordinates of the three primary colors (red, green, and blue). Through white point fitting, a curve describing the ideal white point can be generated, and the specific white point chromaticity coordinates can be extracted from it. For example, when dealing with a typical application scenario, suppose we need to generate a standard D65 white point (color temperature is about 6500K). Through white point fitting, we can ensure that the generated white point coordinates meet the expected standards.

[0040] In a specific embodiment, performing gamma curve correction on the sub-pixel spectrum matrix based on the white point chromaticity coordinates to obtain a gamma correction coefficient matrix includes: Performing spectral energy distribution analysis on the sub-pixel spectral matrix based on the white point chromaticity coordinates to obtain a spectral energy distribution curve, and performing piecewise cubic spline interpolation on the spectral energy distribution curve to obtain a piecewise spectral energy curve; Performing spectral response linearization processing on the sub-pixel spectral matrix through the segmented spectral energy curve to obtain a linearized spectral response matrix, and performing chromatic adaptation transformation on the linearized spectral response matrix to obtain a chromatic adaptation spectral response matrix; Based on the chromatic adaptation spectral response matrix, the linearized spectral response matrix is ​​fitted with an inverse gamma function to obtain an inverse gamma function coefficient matrix, and the inverse gamma function coefficient matrix is ​​approximated by a polynomial to obtain an approximated gamma function coefficient matrix; Calculating the gamma value of the inverse gamma function coefficient matrix by using the approximated gamma function coefficient matrix to obtain a sub-pixel gamma value matrix; A gamma curve is generated based on the sub-pixel gamma value matrix to obtain a sub-pixel gamma correction curve, and a gamma correction coefficient matrix is ​​constructed based on the sub-pixel gamma correction curve.

[0041] Specifically, in the color calibration process of the display panel, gamma curve correction of the sub-pixel spectrum matrix based on the white point chromaticity coordinates is one of the key steps, which aims to adjust the brightness response characteristics of each sub-pixel through an accurate mathematical model to obtain a gamma correction coefficient matrix. This process not only improves the color reproduction capability of the display device, but also ensures the consistency and reliability of the product. First, the spectral energy distribution analysis of the sub-pixel spectrum matrix is ​​performed based on the white point chromaticity coordinates to obtain a spectral energy distribution curve, and the spectral energy distribution curve is interpolated by piecewise cubic spline to obtain a piecewise spectral energy curve. Suppose we have a high-end OLED display whose sub-pixel spectrum matrix contains data of 1024 wavelength points. By performing spectral energy distribution analysis on these data, we can obtain a curve describing the energy distribution of each sub-pixel (red, green, and blue) at different wavelengths. In order to improve the smoothness and continuity of the curve, the original curve is divided into multiple small segments using the piecewise cubic spline interpolation method, and each segment is fitted with a cubic polynomial. For example, suppose we divide the curve into 10 segments, each containing 100 wavelength points, then a total of 1000 data points need to be processed to ensure the smoothness and accuracy of the curve. Next, the sub-pixel spectral matrix is ​​linearized by the segmented spectral energy curve to obtain a linearized spectral response matrix, and the linearized spectral response matrix is ​​chromatically adapted to obtain a chromatically adapted spectral response matrix. Since the human eye's perception of brightness changes is nonlinear, directly using the original spectral data will cause color distortion or unevenness in low-brightness areas. Therefore, it is necessary to linearize the spectral response. Suppose we have data for 1024 wavelength points, and after linearization, a new linearized spectral response matrix can be obtained. Then, through chromatically adapted transformation, the linearized spectral response matrix is ​​converted to a color space under standard observation conditions, such as the CIE XYZ color space. Suppose that when we process a batch of displays, we find that some screens show slight color deviation or uneven brightness in low-brightness areas. Through chromatically adapted transformation, these problems can be effectively corrected to ensure color consistency and accuracy. Based on the chromatic adaptation spectral response matrix, the linearized spectral response matrix is ​​fitted with an inverse gamma function to obtain an inverse gamma function coefficient matrix, and the inverse gamma function coefficient matrix is ​​approximated by a polynomial to obtain an approximated gamma function coefficient matrix. The inverse gamma function is used to describe the nonlinear response characteristics of the display device at different brightness levels. Assuming that we choose to use a fifth-order polynomial to approximate the inverse gamma function, we need to calculate five inverse gamma function coefficients for each sub-pixel (red, green, and blue), and a total of 15 coefficients need to be calculated. These coefficients can be calculated efficiently through the least squares method or other optimization algorithms. For example, when processing certain specific brightness areas, it may be found that the sampling points are unevenly distributed, resulting in some details being lost or over-amplified. Through polynomial approximation, these problems can be effectively solved to ensure data quality across the entire brightness range.By approximating the gamma function coefficient matrix, the gamma value of the inverse gamma function coefficient matrix is ​​calculated to obtain the sub-pixel gamma value matrix. Assuming that we have data at 1024 wavelength points, after inverse gamma function fitting and polynomial approximation, a sub-pixel gamma value matrix can be obtained. This matrix contains the gamma values ​​of each sub-pixel at different brightness levels. For example, when processing a batch of displays, it was found that the red sub-pixel had a slight brightness offset problem in the low brightness area. Through gamma value calculation, this offset can be accurately identified and adjusted to ensure the consistency and accuracy of brightness. Finally, based on the sub-pixel gamma value matrix, a gamma curve is generated to obtain a sub-pixel gamma correction curve, and a gamma correction coefficient matrix is ​​constructed based on the sub-pixel gamma correction curve. Assuming that we have data at 1024 wavelength points, through gamma curve generation, a gamma correction curve describing each sub-pixel at different brightness levels can be obtained. For example, when processing a typical application scenario, it is assumed that the gamma value of the red sub-pixel is 2.2, the gamma value of the green sub-pixel is 2.0, and the gamma value of the blue sub-pixel is 2.4. Gamma curve generation ensures that these gamma values ​​remain consistent at the same brightness level. Then, based on these gamma correction curves, a gamma correction coefficient matrix can be constructed to guide subsequent brightness adjustments and color calibration. To better understand the application scenario of this process, we can consider a high-end display production line where engineers need to ensure that each screen can provide consistent and high-quality color performance. For example, when debugging a new batch of displays, it may be found that some screens have slight color deviations or uneven brightness in certain brightness areas. Through the above gamma curve correction method, the specific causes of these problems can be accurately identified and corrected by adjusting relevant parameters. Suppose a batch of displays has a shift in the brightness response of the blue sub-pixel, resulting in a slight blue shift in the low-brightness area of ​​the picture. Through a series of steps such as spectral energy distribution analysis, piecewise cubic spline interpolation, spectral response linearization, chromatic adaptation transformation, inverse gamma function fitting, polynomial approximation, gamma value calculation, and gamma curve generation, engineers can accurately extract the true brightness response characteristics of the blue sub-pixel and adjust its luminous characteristics accordingly, so that the color reproduction performance of the final product meets the design standard. Through these detailed steps and digital verification, the color reproduction capability and consistency of display devices can be ensured, user experience can be improved, and the market competitiveness of products can be enhanced. For example, in an actual production line test, assuming that we processed 100 displays, 95% of the screens achieved the expected color reproduction performance after gamma curve correction, and only 5% of the screens needed further adjustment, which proved the effectiveness and practicality of the method.

[0042] In a specific embodiment, the color space conversion of the gamma correction coefficient matrix is ​​performed through a preset three-dimensional lookup table to obtain color control parameters, and the color control parameters are input into a preset display driver chip to drive the sub-pixels of each pixel unit in the display panel to emit light, thereby obtaining a color calibrated display output, including: Performing color space mapping on the gamma correction coefficient matrix through a preset three-dimensional lookup table to obtain an initial color mapping matrix; Performing color space conversion on the initial color mapping matrix through chromaticity uniformity processing to obtain uniform color space data, and performing color difference quantization on the uniform color space data to obtain quantized color difference data; Dynamic range adjustment is performed on the quantized color difference data by contrast enhancement to obtain adjusted color space data, and saturation correction is performed on the adjusted color space data to obtain corrected color space data; Performing reverse mapping based on the corrected color space data to obtain color control parameters, and performing pulse width modulation encoding on the color control parameters to obtain encoded color control parameters; The encoded color control parameters are input into a preset display driver chip to drive the sub-pixels of each pixel unit in the display panel to emit light, thereby obtaining a color calibrated display output.

[0043] Specifically, in the color calibration process of the display panel, color space conversion of the gamma correction coefficient matrix through a preset three-dimensional lookup table is one of the key steps, which aims to adjust the brightness and color characteristics of each sub-pixel through an accurate mathematical model, thereby obtaining color control parameters, and finally driving the sub-pixels of each pixel unit in the display panel to emit light, so as to achieve color calibration display output. This process not only improves the color reproduction capability of the display device, but also ensures the consistency and reliability of the product. First, the gamma correction coefficient matrix is ​​mapped to color space through a preset three-dimensional lookup table to obtain an initial color mapping matrix. A three-dimensional lookup table (3D LUT) is a powerful tool for color space conversion, which can map input color values ​​to output color values, thereby achieving accurate color calibration and optimization. Suppose we have a high-end OLED display whose gamma correction coefficient matrix contains data for 1024 wavelength points. By using a preset three-dimensional lookup table, we can map this data to a standard color space (such as sRGB or Adobe RGB) to generate an initial color mapping matrix. For example, when processing a batch of displays, it was found that some screens had slight color deviations or unevenness in specific color areas. Through the mapping function of the three-dimensional lookup table, these problems can be effectively corrected to ensure color consistency and accuracy. Next, the initial color mapping matrix is ​​converted into color space through chromaticity uniformity processing to obtain uniform color space data, and the uniform color space data is quantified to obtain quantized color difference data. Chromaticity uniformity processing is a technology used to eliminate the unevenness in color distribution so that the color performance is consistent throughout the color gamut. Assume that when we are dealing with a typical RGB display system, the initial color mapping matrix contains data of 1024 wavelength points. After chromaticity uniformity processing, a new uniform color space data set can be obtained. Then, by quantizing the color difference of the uniform color space data, the differences between different colors can be identified and quantified into specific values. For example, when dealing with certain specific color areas, it may be found that the differences between some colors are large, resulting in visual inconsistency. Through color difference quantization, these differences can be accurately measured and provide basic data for further adjustment. Then, the quantized color difference data is dynamically adjusted through contrast enhancement to obtain adjusted color space data, and the adjusted color space data is saturated to obtain corrected color space data. Contrast enhancement is a technique used to improve the contrast of an image so that it can show clear details at different brightness levels. Assuming that we are dealing with a high-end OLED display, the quantitative color difference data contains data at 1024 wavelength points. Through contrast enhancement, the dynamic range of this data can be adjusted so that the image can show good contrast in both low-brightness and high-brightness areas.Then, by performing saturation correction on the adjusted color space data, the saturation of the color can be ensured to be consistent throughout the color gamut. For example, when processing certain specific color areas, it may be found that the saturation of some colors is insufficient or too high, resulting in visual distortion. Through saturation correction, these problems can be effectively solved to ensure color consistency and accuracy. Reverse mapping is performed based on the corrected color space data to obtain color control parameters, and the color control parameters are pulse width modulated encoded to obtain encoded color control parameters. Reverse mapping is a technique for converting the corrected color space data back to the original color space in order to generate specific color control parameters. Assume that when we are processing a typical RGB display system, the corrected color space data contains data of 1024 wavelength points. Through reverse mapping, these data can be converted back to the original color space and the corresponding color control parameters can be generated. Then, by pulse width modulation (PWM) encoding of the color control parameters, these parameters can be converted into digital signals suitable for display driver chip processing. For example, when processing a batch of displays, assume that we need to generate 1024 color control parameters, each corresponding to the data of a wavelength point. Through PWM encoding, these parameters can be converted into digital signals suitable for display driver chip processing to ensure that the luminous characteristics of each sub-pixel meet expectations. Finally, the coded color control parameters are input into the preset display driver chip to drive the sub-pixels of each pixel unit in the display panel to emit light, and obtain a color calibrated display output. The display driver chip is responsible for receiving these coded color control parameters and converting them into electrical signals, thereby driving each pixel unit in the display panel to emit light. Assume that in an actual production line test, we process 100 display screens, of which 95% of the screens achieve the expected color reproduction performance after the above series of steps, and only 5% of the screens need further adjustment. This proves the effectiveness and practicality of this method. For example, on a high-end display production line, engineers need to ensure that each screen can provide consistent and high-quality color performance. Through the above series of steps such as color space conversion, chromaticity uniformity processing, contrast enhancement, saturation correction, reverse mapping and PWM encoding, engineers can accurately adjust the luminous characteristics of each sub-pixel so that the color reproduction performance of the final product meets the design standard. To better understand the application scenario of this process, we can consider that on a high-end display production line, engineers need to ensure that each screen can provide consistent and high-quality color performance. For example, when debugging a new batch of displays, you may find that some screens have slight color deviations or uneven brightness in certain color areas. Through the above gamma curve correction method, the specific causes of these problems can be accurately identified and the relevant parameters can be adjusted to correct these deviations.Suppose a batch of displays has a shift in the spectral distribution of the green sub-pixel, resulting in a slight green shift in the overall picture. Through a series of steps such as color space conversion, chromaticity equalization, contrast enhancement, saturation correction, reverse mapping, and PWM encoding, engineers can accurately extract the true spectral characteristics of the green sub-pixel and adjust its luminous characteristics accordingly, so that the color reproduction performance of the final product meets the design standards. Through these detailed steps and digital verification, the color reproduction capability and consistency of the display device can be ensured, the user experience can be improved, and the market competitiveness of the product can be enhanced. For example, in an actual production line test, suppose we processed 100 displays, 95% of the screens achieved the expected color reproduction performance after the above series of steps, and only 5% of the screens needed further adjustment, which proved the effectiveness and practicality of the method.

[0044] The above describes the color calibration method based on the display driver chip in the embodiment of the present invention. The following describes the color calibration system based on the display driver chip in the embodiment of the present invention. Figure 2 In one embodiment of the present invention, a color calibration system based on a display driver chip includes: The sampling module 21 is used to perform multi-region spectral sampling on the light signal emitted by the pixel unit array of the display panel to obtain an original spectral data stream; An extraction module 22, configured to extract the color gamut boundary of the original spectral data stream by polynomial fitting to obtain a color gamut boundary vector group; A reconstruction module 23, configured to perform sub-pixel spectrum reconstruction on the original spectral data stream based on the color gamut boundary vector group to obtain a sub-pixel spectrum matrix; An estimation module 24, configured to perform white point chromaticity estimation on the sub-pixel spectrum matrix by using a Gaussian mixture model to obtain white point chromaticity coordinates; A correction module 25, configured to perform gamma curve correction on the sub-pixel spectrum matrix based on the white point chromaticity coordinates to obtain a gamma correction coefficient matrix; The conversion module 26 is used to perform color space conversion on the gamma correction coefficient matrix through a preset three-dimensional lookup table to obtain color control parameters, and input the color control parameters into a preset display driver chip to drive the sub-pixels of each pixel unit in the display panel to emit light, thereby obtaining a color calibrated display output.

[0045] In this embodiment, for the specific implementation of each unit in the above system embodiment, please refer to the above method embodiment, which will not be repeated here.

[0046] Reference Figure 3 The present invention also provides a computer device in an embodiment, wherein the internal structure of the computer device can be as follows: Figure 3 As shown. The computer device includes a processor, a memory, a display screen, an input device, a network interface and a database connected through a system bus. Among them, the processor designed by the computer is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0047] Those skilled in the art will understand that Figure 3 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0048] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the above method is implemented. It can be understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0049] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided by the present invention and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM.

[0050] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0051] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A color calibration method based on a display driver chip, characterized in that: The following steps are involved: Performing multi-region spectral sampling on the light signal emitted by the pixel unit array of the display panel to obtain an original spectral data stream; Extracting the color gamut boundary of the original spectral data stream by polynomial fitting to obtain a color gamut boundary vector group; Performing sub-pixel spectrum reconstruction on the original spectral data stream based on the color gamut boundary vector group to obtain a sub-pixel spectrum matrix; Performing white point chromaticity estimation on the sub-pixel spectrum matrix by using a Gaussian mixture model to obtain white point chromaticity coordinates; Performing gamma curve correction on the sub-pixel spectrum matrix based on the white point chromaticity coordinates to obtain a gamma correction coefficient matrix; The gamma correction coefficient matrix is ​​converted into color space through a preset three-dimensional lookup table to obtain color control parameters, and the color control parameters are input into a preset display driver chip to drive the sub-pixels of each pixel unit in the display panel to emit light, thereby obtaining a color calibrated display output.

2. The color calibration method based on the display driver chip according to claim 1, characterized in that: The multi-region spectrum sampling of the light signal emitted by the pixel unit array of the display panel to obtain the original spectrum data stream includes: Using a preset microlens array to focus the optical signal on the pixel unit array of the display panel to obtain a focused optical signal, and performing optical fiber bundle splitting on the focused optical signal to obtain a multi-channel split optical signal; wherein the multi-channel split optical signal includes the pixel unit spectrum information; Performing photoelectric conversion on the multi-channel split light signals through a photodiode array to obtain a photoelectric conversion signal, and performing analog-to-digital conversion on the photoelectric conversion signal to obtain a digital spectrum signal; Performing wavelength calibration on the digital spectrum signal based on a preset wavelength calibration matrix to obtain calibration spectrum data, and performing dark current compensation on the calibration spectrum data to obtain compensated spectrum data; The spatial resolution of the compensated spectral data is enhanced based on a spatial interpolation algorithm to obtain an original spectral data stream.

3. The color calibration method based on the display driver chip according to claim 1, characterized in that: The color gamut boundary extraction is performed on the original spectral data stream by polynomial fitting to obtain a color gamut boundary vector group, including: Performing chromaticity coordinate conversion on the original spectral data stream to obtain a chromaticity coordinate set, and performing color gamut segment sampling on the chromaticity coordinate set to obtain a color gamut sampling point sequence; Reconstructing the color gamut sampling point sequence into non-uniform sampling points to obtain a uniform sampling point sequence, and performing convex hull analysis on the uniform sampling point sequence to obtain color gamut boundary contour points; Performing cubic function fitting on the color gamut boundary contour points based on Chebyshev polynomials to obtain a piecewise fitting curve, and performing curvature analysis on the piecewise fitting curve to obtain color gamut boundary feature points; wherein the color gamut boundary feature points include curvature extreme point and inflection point information; Based on a preset Bernstein polynomial, polynomial coefficients are extracted from the color gamut boundary feature points to obtain a polynomial coefficient matrix, and the polynomial coefficient matrix is ​​orthogonalized to obtain an orthogonal basis vector group, and the orthogonal basis vector group is standardized by Gram-Schmidt orthogonalization to obtain a color gamut boundary vector group.

4. The color calibration method based on the display driver chip according to claim 1, characterized in that: The step of reconstructing the sub-pixel spectrum of the original spectral data stream based on the color gamut boundary vector group to obtain a sub-pixel spectrum matrix includes: Performing color gamut constraint on the original spectral data stream by using the color gamut boundary vector group to obtain color gamut constrained spectral data, and performing sub-pixel spectral separation on the color gamut constrained spectral data to obtain sub-pixel spectral components; wherein the sub-pixel spectral components include spectral information of the three primary colors of red, green and blue; Performing spectral basis expansion on the sub-pixel spectral components based on a preset spectral basis function to obtain spectral basis expansion coefficients, and performing orthogonal processing on the spectral basis expansion coefficients to obtain an orthogonal spectral basis coefficient matrix; Reconstructing the sub-pixel spectrum components by using the orthogonal spectral basis coefficient matrix to obtain a reconstructed sub-pixel spectrum; The reconstructed sub-pixel spectra are spectrally peak aligned to obtain aligned sub-pixel spectra, and the aligned sub-pixel spectra are spectrally matrixed to obtain a sub-pixel spectrum matrix; wherein the sub-pixel spectrum matrix includes red, green and blue primary color spectrum matrices.

5. The color calibration method based on the display driver chip according to claim 1, characterized in that: The step of estimating the white point chromaticity of the sub-pixel spectrum matrix by using a Gaussian mixture model to obtain white point chromaticity coordinates includes: Extracting spectral features from the sub-pixel spectral matrix to obtain a spectral feature vector group; Based on the Gaussian mixture model, the probability density of the spectral feature vector group is estimated to obtain the spectral probability density distribution, and the spectral probability density distribution is estimated by maximum likelihood to obtain the spectral clustering center; wherein the spectral clustering center includes the clustering centers of the three primary colors of red, green and blue; Performing chromaticity coordinate conversion on the spectral cluster centers to obtain a cluster center chromaticity coordinate set, and performing weighted averaging on the cluster center chromaticity coordinate set to obtain a weighted average chromaticity coordinate; Performing chromaticity space optimization on the weighted average chromaticity coordinates based on a preset Mahalanobis distance function to obtain optimized chromaticity coordinates, and performing color temperature matching and correction on the optimized chromaticity coordinates to obtain corrected chromaticity coordinates; White point fitting is performed on the corrected chromaticity coordinates to obtain a white point fitting curve, and chromaticity coordinates are extracted from the white point fitting curve to obtain white point chromaticity coordinates; wherein the white point chromaticity coordinates include white point coordinates of the three primary colors of red, green and blue.

6. The color calibration method based on the display driver chip according to claim 1, characterized in that: The performing gamma curve correction on the sub-pixel spectrum matrix based on the white point chromaticity coordinates to obtain a gamma correction coefficient matrix includes: Performing spectral energy distribution analysis on the sub-pixel spectral matrix based on the white point chromaticity coordinates to obtain a spectral energy distribution curve, and performing piecewise cubic spline interpolation on the spectral energy distribution curve to obtain a piecewise spectral energy curve; Performing spectral response linearization processing on the sub-pixel spectral matrix through the segmented spectral energy curve to obtain a linearized spectral response matrix, and performing chromatic adaptation transformation on the linearized spectral response matrix to obtain a chromatic adaptation spectral response matrix; Based on the chromatic adaptation spectral response matrix, the linearized spectral response matrix is ​​fitted with an inverse gamma function to obtain an inverse gamma function coefficient matrix, and the inverse gamma function coefficient matrix is ​​approximated by a polynomial to obtain an approximated gamma function coefficient matrix; Calculating the gamma value of the inverse gamma function coefficient matrix by using the approximated gamma function coefficient matrix to obtain a sub-pixel gamma value matrix; A gamma curve is generated based on the sub-pixel gamma value matrix to obtain a sub-pixel gamma correction curve, and a gamma correction coefficient matrix is ​​constructed based on the sub-pixel gamma correction curve.

7. The color calibration method based on a display driver chip according to claim 1, characterized in that: The method comprises: performing color space conversion on the gamma correction coefficient matrix through a preset three-dimensional lookup table to obtain color control parameters, and inputting the color control parameters into a preset display driver chip to drive the sub-pixels of each pixel unit in the display panel to emit light, thereby obtaining a color calibrated display output, including: Performing color space mapping on the gamma correction coefficient matrix through a preset three-dimensional lookup table to obtain an initial color mapping matrix; Performing color space conversion on the initial color mapping matrix through chromaticity uniformity processing to obtain uniform color space data, and performing color difference quantization on the uniform color space data to obtain quantized color difference data; Dynamic range adjustment is performed on the quantized color difference data by contrast enhancement to obtain adjusted color space data, and saturation correction is performed on the adjusted color space data to obtain corrected color space data; Performing reverse mapping based on the corrected color space data to obtain color control parameters, and performing pulse width modulation encoding on the color control parameters to obtain encoded color control parameters; The encoded color control parameters are input into a preset display driver chip to drive the sub-pixels of each pixel unit in the display panel to emit light, thereby obtaining a color calibrated display output.

8. A color calibration system based on a display driver chip, characterized in that: include: A sampling module, used for performing multi-region spectral sampling on the light signal emitted by the pixel unit array of the display panel to obtain an original spectral data stream; An extraction module, used for extracting the color gamut boundary of the original spectral data stream by polynomial fitting to obtain a color gamut boundary vector group; A reconstruction module, configured to perform sub-pixel spectrum reconstruction on the original spectral data stream based on the color gamut boundary vector group to obtain a sub-pixel spectrum matrix; An estimation module, used for performing white point chromaticity estimation on the sub-pixel spectrum matrix through a Gaussian mixture model to obtain white point chromaticity coordinates; A correction module, used for performing gamma curve correction on the sub-pixel spectrum matrix based on the white point chromaticity coordinates to obtain a gamma correction coefficient matrix; The conversion module is used to perform color space conversion on the gamma correction coefficient matrix through a preset three-dimensional lookup table to obtain color control parameters, and input the color control parameters into a preset display driver chip to drive the sub-pixels of each pixel unit in the display panel to emit light, thereby obtaining a color calibrated display output.

9. A computer device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

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

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