Brightness compensation method and device based on step-by-step processing, and storage medium

Abstract

The invention discloses a brightness compensation method and device based on step-by-step processing and a storage medium, which are used for improving the brightness compensation efficiency of a novel display screen. Acquiring an acquisition image; acquiring position data and brightness data of each pixel point; modeling the brightness compensation coefficients of all the collected images to generate relation matrix data; adjusting the relation matrix data according to the brightness data and the position data of each pixel point in each acquired image and a predetermined data order, and generating a brightness compensation coefficient expression; performing decomposition optimization according to a brightness transformation matrix corresponding to brightness order matrix data in the brightness compensation coefficient expression to generate a result matrix; generating coefficient images of the elements of the result matrix in sequence; according to the pixel position matrix, pixels at the same position of the coefficient image and the acquisition image are integrated by taking the image as a unit, and a brightness compensation coefficient is generated; and compensating the brightness of each pixel point by using the brightness compensation coefficient.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of display screen detection, and in particular to a brightness compensation method based on step-by-step processing. Background Art

[0002] With the continuous development of the display field, the current display functions are becoming more and more, and the appearance structure is becoming more and more sophisticated. For example, large-size display screens, flexible screens, spliced ​​curved screens, ultra-high-resolution display screens, ultra-narrow bezel display screens and other sophisticated display screens. With the popularity of new display screens, the phenomenon of display unevenness (Mura) has become the focus of attention. The Mura phenomenon is usually caused by factors such as the production process level and raw material purity of different types of display screens.

[0003] In the field of new display panels, the main technical means for brightness compensation technology for display unevenness (Mura) phenomenon include global brightness adjustment, regional brightness correction, etc. In the process of adjusting the brightness of each pixel by the brightness compensation algorithm (ie, demura) of the display screen, the relationship between the brightness and each pixel is mainly obtained based on multiple captured grayscale images, which is mainly based on mathematical modeling. As the precision of new display screens continues to increase, and the resolution of the captured images used for brightness compensation is getting higher and higher, the amount of data for solving the coefficient vector pixel by pixel in the brightness compensation link is growing exponentially, the amount of calculation is extremely large, and the calculation time is greatly extended, the numerical stability is poor, and the brightness compensation efficiency of new display screens is reduced. Summary of the invention

[0004] The present application discloses a brightness compensation method, device and storage medium based on step-by-step processing, which are used to improve the brightness compensation efficiency of a new display screen.

[0005] In a first aspect, an embodiment of the present application provides a brightness compensation method based on step-by-step processing, comprising: Obtain N captured images of the display screen to be tested at different preset display grayscales, where N is an integer greater than or equal to 2; obtain the position data and brightness data of each pixel in each captured image; model the brightness compensation coefficients of all captured images to generate relationship matrix data, which includes brightness compensation coefficient matrix data, pixel position matrix data and brightness order matrix data; adjust the relationship matrix data according to the brightness data, position data and predetermined data order of each pixel in each captured image to generate a brightness compensation coefficient expression; decompose and optimize the brightness transformation matrix corresponding to the brightness order matrix data in the brightness compensation coefficient expression to generate a result matrix; sequentially generate N*N coefficient images from the elements of the result matrix, and the value of each coefficient image is any element of the result matrix; process the NxN coefficient images and the N captured images step by step, and integrate the coefficient images and the captured images for pixels at the same position in units of images according to the pixel position matrix to generate a brightness compensation coefficient; and use the brightness compensation coefficient to compensate the brightness at each pixel.

[0006] Optionally, the step of decomposing and optimizing the brightness transformation matrix corresponding to the brightness order matrix data in the brightness compensation coefficient expression to generate the result matrix includes: The QR decomposition method is used to decompose and optimize the brightness transformation matrix corresponding to the brightness order matrix data in the brightness compensation coefficient expression to generate a result matrix.

[0007] Optionally, the step of using a QR decomposition method to decompose and optimize the brightness transformation matrix corresponding to the brightness order matrix data in the brightness compensation coefficient expression, and generating a result matrix includes: Perform QR decomposition optimization on the brightness transformation matrix corresponding to the brightness order matrix data in the brightness compensation coefficient expression to generate an orthogonal data set and a triangular data set; Generate a result matrix based on the orthogonal decomposition data and the triangular decomposition data.

[0008] Optionally, the NxN coefficient images and the N acquired images are processed in steps, and the coefficient images and the acquired images are integrated step by step for pixels at the same position in units of images according to the pixel position matrix. The step of generating the brightness compensation coefficient includes: determining the coefficient images of the first row in the NxN coefficient images and the corresponding first acquired image, integrating the pixels at the same position in units of images according to the pixel position matrix, and generating a first step-by-step processing result; determining the coefficient images of the second row in the NxN coefficient images and the corresponding second acquired image, integrating the pixels at the same position in units of images according to the pixel position matrix, and then combining the integration result with the first step-by-step processing result to generate a second step-by-step processing result; and so on, determining the coefficient images of the Nth row and the corresponding Nth acquired image, integrating the pixels at the same position in units of images according to the pixel position matrix, and then combining the integration result with the N-1th step-by-step processing result to generate a target processing result; and determining the target processing result as the brightness compensation coefficient.

[0009] Optionally, after the steps of acquiring N captured images of the display screen to be tested at different preset display grayscales, and acquiring position data and brightness data of each pixel in each captured image, the brightness compensation method further includes: Perform image preprocessing on N captured images to eliminate lens distortion, noise and redundant background areas.

[0010] Optionally, the step of obtaining N captured images of the display screen to be tested at different preset display grayscales includes: The display screen to be tested is subjected to lighting processing of different preset display grayscale images, and the central point area of ​​the display screen to be tested is lit; Adjust the position of the sampling camera so that the sampling center of the sampling camera is aligned with the center point of the display screen to be tested; The sampling camera collects images at the center point of the center point area to generate a collected image.

[0011] Optionally, the display screen to be tested is a display screen composed of several flexible spliced ​​screens; the step of obtaining N captured images of the display screen to be tested at different preset display grayscales includes: combining the flexible spliced ​​display screens to form a display screen to be tested; lighting the display screen to be tested with different preset display grayscale images, lighting the central point area of ​​the display screen to be tested, and lighting the central pixel point of each flexible spliced ​​display screen; adjusting the position of the sampling camera so that the sampling center of the sampling camera is aligned with the central point of the central point area, and collecting the first image to be adjusted at the central point of the central point area through the sampling camera; performing a warping analysis on the display screen to be tested according to the position coordinates of the central point area in the first image to be adjusted, the coordinate information and grayscale information of the central pixel point of each flexible spliced ​​display screen; when the warping analysis result shows that there is no flexible splicing warping defect, lighting the display screen to be tested with different preset display grayscale images, collecting through the sampling camera, and generating a collected image; When the warpage analysis result shows that there is a flexible splicing warpage defect, the flexible splicing screen with the flexible splicing warpage defect is replaced according to the warpage analysis result; The replaced display screen to be tested is imaged again until there is no flexible splicing warping defect, and the image is re-captured by the sampling camera to generate a captured image.

[0012] Optionally, the step of performing a warping analysis on the display screen to be tested according to the position coordinates of the central point area in the first image to be adjusted, the coordinate information of the central pixel point of each flexible spliced ​​display screen, and the grayscale information includes: expanding the spliced ​​screen points according to the position coordinates of the central point area in the first image to be adjusted, and generating a reference midpoint on each flexible spliced ​​screen area; performing a collective orientation deviation analysis on the coordinate information of the central pixel point of each flexible spliced ​​display screen on the first image to be adjusted and the reference midpoint; when the analysis result shows that there is a warping defect, determining the first image to be adjusted according to the collective orientation deviation analysis result. The warping defect area on the image is adjusted, and the warping defect area is composed of at least one flexible splicing display screen area; a number of grayscale gradient images are generated according to the warping defect direction information of the collective directional deviation analysis result and the preset gradient degree, and the gradient degree is determined by the size of the flexible splicing screen; the grayscale gradient image is lit on the flexible splicing screen corresponding to the warping defect area; the sampling camera position is adjusted so that the sampling center of the sampling camera is aligned with the center point of the flexible splicing screen, and the warping defect detection image is collected by the sampling camera; the warping grayscale gradient analysis is performed on the warping defect detection image to generate the warping analysis result.

[0013] In a second aspect, an embodiment of the present application provides a brightness compensation device based on step-by-step processing, comprising: a first acquisition unit, for acquiring N captured images of a display screen to be tested at different preset display grayscales, where N is an integer greater than or equal to 2; a second acquisition unit, for acquiring position data and brightness data of each pixel in each captured image; a first generation unit, for modeling brightness compensation coefficients of all captured images, and generating relationship matrix data, the relationship matrix data including brightness compensation coefficient matrix data, pixel position matrix data, and brightness order matrix data; a second generation unit, for generating a relationship matrix based on the brightness data, position data, and predetermined data order of each pixel in each captured image. The matrix data is adjusted to generate a brightness compensation coefficient expression; the third generation unit is used to decompose and optimize the brightness transformation matrix corresponding to the brightness order matrix data in the brightness compensation coefficient expression to generate a result matrix; the fourth generation unit is used to sequentially generate N*N coefficient images from the elements of the result matrix, and the value of each coefficient image is any element of the result matrix; the fifth generation unit is used to process the NxN coefficient images and the N acquisition images in steps, and integrate the coefficient images and the acquisition images for pixels at the same position in units of images according to the pixel position matrix to generate a brightness compensation coefficient; the compensation unit is used to compensate the brightness of each pixel point using the brightness compensation coefficient.

[0014] Optionally, the third generation unit includes: The QR decomposition method is used to decompose and optimize the brightness transformation matrix corresponding to the brightness order matrix data in the brightness compensation coefficient expression to generate a result matrix.

[0015] Optionally, the third generation unit includes: Perform QR decomposition optimization on the brightness transformation matrix corresponding to the brightness order matrix data in the brightness compensation coefficient expression to generate an orthogonal data set and a triangular data set; Generate a result matrix based on the orthogonal decomposition data and the triangular decomposition data.

[0016] Optionally, the fifth generation unit includes: determining the coefficient image of the first row in the NxN coefficient images and the corresponding first acquired image, integrating the pixels at the same position according to the pixel position matrix and in units of images, and generating a first step-by-step processing result; determining the coefficient image of the second row in the NxN coefficient images and the corresponding second acquired image, integrating the pixels at the same position according to the pixel position matrix and in units of images, and then combining the integration result with the first step-by-step processing result to generate a second step-by-step processing result; and so on, determining the coefficient image of the Nth row and the corresponding Nth acquired image, integrating the pixels at the same position according to the pixel position matrix and in units of images, and then combining the integration result with the N-1th step-by-step processing result to generate a target processing result; and determining the target processing result as the brightness compensation coefficient.

[0017] Optionally, after the steps of acquiring N captured images of the display screen to be tested at different preset display grayscales, and acquiring position data and brightness data of each pixel in each captured image, the brightness compensation method further includes: Perform image preprocessing on N captured images to eliminate lens distortion, noise and redundant background areas.

[0018] Optionally, the step of obtaining N captured images of the display screen to be tested at different preset display grayscales includes: The display screen to be tested is subjected to lighting processing of different preset display grayscale images, and the central point area of ​​the display screen to be tested is lit; Adjust the position of the sampling camera so that the sampling center of the sampling camera is aligned with the center point of the display screen to be tested; The sampling camera collects images at the center point of the center point area to generate a collected image.

[0019] Optionally, the display screen to be tested is a display screen composed of several flexible spliced ​​screens; the first acquisition unit includes: a splicing module, which is used to combine the flexible spliced ​​screens to form a display screen to be tested; a first lighting module, which is used to light up the display screen to be tested with different preset display grayscale images, light up the central point area of ​​the display screen to be tested, and light up the central pixel point of each flexible spliced ​​screen; a first acquisition module, which is used to adjust the position of the sampling camera so that the sampling center of the sampling camera is aligned with the central point of the central point area, and the first image to be adjusted is collected by the sampling camera at the central point of the central point area; an analysis module, which is used to analyze the position of the central point area in the first image to be adjusted according to the position of the central point area The first acquisition module is used for performing warpage analysis on the display screen to be tested based on the coordinate information of the center pixel point of each flexible splicing display screen and the grayscale information; the second acquisition module is used for lighting up the display screen to be tested with different preset display grayscale images when the warpage analysis result shows that there is no flexible splicing warpage defect, and collecting the images through the sampling camera to generate a collected image; the replacement module is used for replacing the flexible splicing screen with the flexible splicing warpage defect according to the warpage analysis result when the warpage analysis result shows that there is a flexible splicing warpage defect; the generation module is used for collecting images of the replaced display screen to be tested again until there is no flexible splicing warpage defect, and collecting the images again through the sampling camera to generate a collected image.

[0020] Optionally, the analysis module includes: expanding the splicing screen points according to the position coordinates of the central point area in the first image to be adjusted, and generating a reference midpoint on each flexible splicing screen area; performing a collective directional deviation analysis on the coordinate information of the central pixel point of each flexible splicing display screen on the first image to be adjusted and the reference midpoint; when the analysis result shows that there is a warping defect, determining the warping defect area on the first image to be adjusted according to the collective directional deviation analysis result, and the warping defect area is composed of at least one flexible splicing display screen area; generating a number of grayscale gradient images according to the warping defect direction information and a preset gradient degree of the collective directional deviation analysis result, and the gradient degree is determined by the size of the flexible splicing screen; lighting up the grayscale gradient image on the flexible splicing screen corresponding to the warping defect area; adjusting the sampling camera position so that the sampling center of the sampling camera is aligned with the center point of the flexible splicing screen, and collecting the warping defect detection image through the sampling camera; performing a warping grayscale gradient analysis on the warping defect detection image to generate a warping analysis result.

[0021] In the third aspect, an embodiment of the present application provides a brightness compensation device based on step-by-step processing, comprising: a processor, a memory, an input-output unit, and a bus; the processor is connected to the memory, the input-output unit, and the bus; the memory stores a program, and the processor calls the program to execute the first aspect and any optional brightness compensation method of the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a program is stored. When the program is executed on a computer, the program performs the first aspect and any optional brightness compensation method of the first aspect.

[0023] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages: The present application first obtains N captured images of the display screen to be tested at different preset display grayscales, where N is an integer greater than or equal to 2. Next, the position data and brightness data of each pixel in each captured image are obtained. Then, the brightness compensation coefficients of all captured images are modeled to generate relationship matrix data, wherein the relationship matrix data includes brightness compensation coefficient matrix data, pixel position matrix data, and brightness order matrix data. The relationship matrix data is adjusted according to the brightness data, position data, and predetermined data order of each pixel in each captured image to generate a brightness compensation coefficient expression. The brightness transformation matrix corresponding to the brightness order matrix data in the brightness compensation coefficient expression is decomposed and optimized to generate a result matrix; the elements of the result matrix are sequentially generated into N*N coefficient images, and the value of each coefficient image is any element of the result matrix; the NxN coefficient images and the N captured images are processed step by step, and the coefficient images and the captured images are sequentially integrated for the pixels at the same position in units of images according to the pixel position matrix to generate a brightness compensation coefficient; the brightness at each pixel is compensated using the brightness compensation coefficient.

[0024] By decomposing and optimizing the brightness transformation matrix corresponding to the brightness order matrix data in the brightness compensation coefficient expression, the corresponding result matrix is ​​obtained by using the least squares method for QR decomposition, instead of directly solving the matrix in the original modeling. This method improves the numerical stability of brightness compensation and thus improves the brightness compensation efficiency of the new display screen. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0026] Figure 1 A schematic diagram of an embodiment of a brightness compensation method based on step-by-step processing of the present application; Figure 2 A schematic diagram of an embodiment of a method for generating a brightness compensation coefficient for the present application; Figure 3 A schematic diagram of an embodiment of a method for generating a result matrix for the present application; Figure 4 A schematic diagram of an embodiment of a method for acquiring an image of a display screen to be tested according to the present application; Figure 5 A schematic diagram of an embodiment of a method for processing flexible splicing warping defects in the present application; Figure 6 A schematic diagram of an embodiment of a method for analyzing warping defects of a flexible splicing screen of the present application; Figure 7 A schematic diagram of an embodiment of a brightness compensation device based on step-by-step processing of the present application; Figure 8 A schematic diagram of another embodiment of the brightness compensation device based on step-by-step processing of the present application; Fig. 9 This is a schematic diagram of another embodiment of the method for performing image preprocessing based on a captured image in the present application; Fig.10 This is a schematic diagram of another embodiment of the method for capturing display screen images according to the present application. DETAILED DESCRIPTION

[0027] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0028] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0029] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0030] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0031] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0032] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0033] In the prior art, in the field of new display panels, the main technical means for brightness compensation technology for display unevenness (Mura) phenomenon include global brightness adjustment, regional brightness correction, etc. In the process of adjusting the brightness of each pixel by the brightness compensation algorithm (ie, demura) of the display screen, the relationship between the brightness and each pixel is mainly obtained based on multiple captured grayscale images, which is mainly obtained based on mathematical modeling. In the brightness compensation of new display screens, it is generally necessary to perform mathematical modeling fitting across images for the pixels at the same position of multiple images, and in order to ensure the brightness compensation effect, the grayscale images currently used for brightness compensation are all high-resolution, usually 7680*4320. If the coefficients are solved directly based on matrix operations, the pixel data at the same position of N images (with a resolution of m*n) need to be constructed into a three-dimensional matrix N*N*(m*n). The amount of data for solving the coefficient vector pixel by pixel by pixel through the least squares normal equation grows exponentially, and the amount of calculation is extremely large. On the one hand, this will take up a lot of memory resources and easily lead to insufficient memory problems; on the other hand, a large number of matrix multiplications and inversion operations greatly prolong the calculation time, and the numerical stability is poor, which seriously affects the real-time and practicality of the algorithm. Currently, GPU acceleration can also be achieved through CUDA / OpenCL, which can perform highly parallel computing acceleration processing on a pixel-by-pixel basis, but it is limited by video memory capacity, hardware parallel efficiency, algorithm adaptability, and programming complexity.

[0034] Based on this, the present application discloses a brightness compensation method, device and storage medium based on step-by-step processing, which are used to improve the brightness compensation efficiency of a new display screen.

[0035] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0036] The method of the present application can be applied to a server, a device, a terminal or other devices with logic processing capabilities, and the present application does not limit this. For the convenience of description, the following description is made by taking the execution subject as an example of a terminal.

[0037] See also Figure 1 The present application provides an embodiment of a brightness compensation method based on step-by-step processing, comprising: 101. Obtain N captured images of the display screen to be tested at different preset display grayscales, where N is an integer greater than or equal to 2.

[0038] In this embodiment, target images (sampling images) of the display panel at different preset display grayscales are acquired through an industrial-level sampling camera.

[0039] 102. Obtain position data and brightness data of each pixel in each collected image.

[0040] After obtaining the sampled images of the display panel at different preset display grayscales through an industrial-grade sampling camera, the brightness data and position information corresponding to each pixel in the sampled image of the target grayscale are obtained. Before obtaining the brightness data, due to factors such as camera lens distortion, noise and background area, the target image should be preprocessed first, including but not limited to coordinate transformation, geometric correction, Gaussian filtering, etc., to avoid abnormal compensation effect. After preprocessing the sampled image, it is necessary to obtain the position data and brightness data of each pixel in each captured image. Next, it is necessary to fit the brightness compensation coefficients of multiple grayscale images step by step, and accumulate the step-by-step fitting coefficient results.

[0041] 103. Model the brightness compensation coefficients of all collected images to generate relationship matrix data, which includes brightness compensation coefficient matrix data, pixel position matrix data and brightness order matrix data.

[0042] The terminal models the brightness compensation coefficients of all collected images and generates relationship matrix data, wherein the relationship matrix data includes brightness compensation coefficient matrix data, pixel position matrix data and brightness order matrix data.

[0043] Assume that N high-resolution (m*n) sampling images of different grayscales need to be taken, and the position of each pixel is , where i and j are the positions of the pixel coordinates respectively. Next, high-order modeling is performed to calculate the corresponding N brightness compensation coefficients. The compensation coefficient set is , to , brightness order matrix data , pixel position matrix data and brightness compensation coefficient matrix data As shown below:

[0044]

[0045]

[0046] Then fit is performed, and the expression of the relationship matrix data is as follows:

[0047]

[0048] in,

[0049] 104. Adjust the relationship matrix data according to the brightness data, position data and predetermined data order of each pixel point in each collected image to generate a brightness compensation coefficient expression.

[0050] The terminal adjusts the relationship matrix data according to the brightness data, position data and predetermined data order of each pixel in each captured image to generate a brightness compensation coefficient expression. Specifically, the terminal fits the final brightness compensation coefficient by solving the normal equations based on the least squares method. , to for:

[0051] 105. Decompose and optimize the brightness transformation matrix corresponding to the brightness order matrix data in the brightness compensation coefficient expression to generate a result matrix.

[0052] After deriving the final brightness compensation coefficient, the brightness order matrix data of the parameters can be combined , pixel position matrix data Perform calculations.

[0053] At this time, if we directly combine the brightness order matrix data right When performing matrix inversion operations, due to the high precision and large number of pixels of the linear display screen, the problem of poor numerical stability is prone to occur. In order to improve numerical stability, the brightness transformation matrix corresponding to the brightness order matrix data in the brightness compensation coefficient expression can be decomposed and optimized to generate a result matrix. The specific decomposition and optimization method is described in the subsequent embodiments.

[0054] Then calculate the decomposed data The result matrix .

[0055] 106. Generate N*N coefficient images in sequence using the elements of the result matrix, and the value of each coefficient image is any element of the result matrix.

[0056] The terminal generates N*N coefficient images in sequence from the elements of the result matrix. The value of each coefficient image is any element of the result matrix. Specifically, the matrix The elements of are sequentially generated into NxN coefficient images REImage (1,1) ~REImage (N,N) , at this time the image resolution is m*n, and the values ​​of each coefficient image are matrices Any element Re 1*1 , Re 1*2 , Re 1*3 , ..., Re (N-1)*(N-1) ,Re (N)*(N) .

[0057] 107. The NxN coefficient images and the N acquired images are processed step by step, and the coefficient images and the acquired images are sequentially integrated for pixels at the same position in units of images according to the pixel position matrix to generate brightness compensation coefficients.

[0058] The terminal processes NxN coefficient images and N acquired images in steps, and integrates the pixels at the same position of the coefficient images and the acquired images in units of images according to the pixel position matrix to generate brightness compensation coefficients, that is, a set of coefficient images and an acquired image are processed in steps, and then the next set of coefficient images and the next acquired image are used in turn to integrate the processed data of the previous set and finally generate brightness compensation coefficients.

[0059] 108. Use the brightness compensation coefficient to compensate the brightness of each pixel.

[0060] Finally, the terminal uses the brightness compensation coefficient to compensate the brightness of each pixel.

[0061] The present application first obtains N captured images of the display screen to be tested at different preset display grayscales, where N is an integer greater than or equal to 2. Next, the position data and brightness data of each pixel in each captured image are obtained. Then, the brightness compensation coefficients of all captured images are modeled to generate relationship matrix data, wherein the relationship matrix data includes brightness compensation coefficient matrix data, pixel position matrix data, and brightness order matrix data. The relationship matrix data is adjusted according to the brightness data, position data, and predetermined data order of each pixel in each captured image to generate a brightness compensation coefficient expression. The brightness transformation matrix corresponding to the brightness order matrix data in the brightness compensation coefficient expression is decomposed and optimized to generate a result matrix; the elements of the result matrix are sequentially generated into N*N coefficient images, and the value of each coefficient image is any element of the result matrix; the NxN coefficient images and the N captured images are processed step by step, and the coefficient images and the captured images are sequentially integrated for the pixels at the same position in units of images according to the pixel position matrix to generate a brightness compensation coefficient; the brightness at each pixel is compensated using the brightness compensation coefficient.

[0062] By decomposing and optimizing the brightness transformation matrix corresponding to the brightness order matrix data in the brightness compensation coefficient expression, the corresponding result matrix is ​​obtained by using the least squares method for QR decomposition, instead of directly solving the matrix in the original modeling. This method improves the numerical stability of brightness compensation and thus improves the brightness compensation efficiency of the new display screen.

[0063] See also Figure 2 The present application provides an embodiment of a method for generating a brightness compensation coefficient, comprising: 201. Determine the coefficient image of the first row in the NxN coefficient images and the corresponding first acquisition image, integrate the pixel points at the same position according to the pixel position matrix and in units of images, and generate a first step processing result.

[0064] This embodiment proposes a brightness compensation method for performing step-by-step processing on multiple grayscale images, which can reduce memory usage, has low programming complexity, and is not limited by hardware conditions.

[0065] The terminal calculates the decomposed expression obtained by QR decomposition The result matrix , at this time the brightness compensation coefficient is:

[0066] The terminal generates N*N coefficient images in sequence from the elements of the result matrix. The value of each coefficient image is any element of the result matrix. Specifically, the matrix The elements of are sequentially generated into NxN coefficient images REImage(1,1) ~REImage (N,N) , at this time the image resolution is m*n, and the values ​​of each coefficient image are matrices Any element Re 1*1 , Re 1*2 , Re 1*3 , ..., Re (N-1)*(N-1) ,Re (N)*(N) .

[0067] For NxN coefficient images REImage (1,1) ~REImage (N,N) And the grayscale image PixelImage that needs to be fitted 1 ~PixelImage N Multiply the pixels at the same position in units of images to obtain the first step processing result image CoefImage1, that is, the first step processing result, as shown below:

[0068] Among them, i and j represent the position coordinates of the pixel points on the image, (1,1) and (1,2) are the coefficient matrices The element position of NxN coefficient image REImage (1,1) ~REImage (N,N) Reference coefficient matrix The coefficient image at the corresponding position in , i.e. ReImage （1，1） It refers to a constant image composed of the elements of the first row and first column of the coefficient matrix ReNxN. CoefImage1 includes N corresponding data and is a set.

[0069] 202. Determine the coefficient image of the second row in the NxN coefficient images and the corresponding second acquisition image, integrate the pixel points at the same position according to the pixel position matrix and in units of images, and then combine the integration result with the first step processing result to generate a second step processing result.

[0070] Next, the second step processing is superimposed on the first step processing result image CoefImage1 to obtain the second step processing result image CoefImage2, that is:

[0071] 203. Similarly, determine the coefficient image of the Nth row and the corresponding Nth acquired image, integrate the pixel points at the same position according to the pixel position matrix and in units of images, and then combine the integration result with the N-1th step processing result to generate the target processing result.

[0072] By analogy, when the Nth step processing result is obtained, the specific result form of the fitting calculation coefficient FinalCoeflImage(N,i,j) of the pixels at the same position of the final multiple grayscale images is obtained:

[0073] 204. Determine the target processing result as a brightness compensation coefficient.

[0074] Finally, the target processing result is obtained. In the above solution, the terminal performs pixel-by-pixel fitting for the same position pixels of multiple high-resolution (m*n) grayscale images PixelImage1~PixelImageN. The calculation amount is large and the calculation time is greatly extended. Currently, GPU and Cuda can be used for high-parallel acceleration processing, but it is necessary to master the CUDA programming model in depth. At the same time, the video memory usage increases linearly with the number of pixels, and data parallelism increases the pressure of memory management.

[0075] Secondly, by calculating the corresponding brightness fitting coefficients for multiple grayscale images step by step in units of images and then superimposing them to obtain the fitting coefficients corresponding to each pixel, calculation of each single pixel can be avoided, which can further improve the brightness compensation efficiency of the new display screen.

[0076] See also Figure 3 The present application provides an embodiment of a method for generating a result matrix, comprising: 301. Decompose and optimize the brightness transformation matrix corresponding to the brightness order matrix data in the brightness compensation coefficient expression to generate an orthogonal data set and a triangular data set.

[0077] In this embodiment, the terminal decomposes and optimizes the brightness transformation matrix corresponding to the brightness order matrix data in the brightness compensation coefficient expression to generate an orthogonal data set and a triangular data set. Specifically, the QR decomposition method is used for auxiliary optimization to convert the brightness order matrix data (coefficient matrix) Decompose into orthogonal data sets and triangular (upper triangle) data sets , there are the following expressions: .

[0078] The decomposition expression can be obtained by QR decomposition:

[0079] Compared with directly targeting To solve the brightness compensation coefficient, the computational complexity of the QR decomposition method is reduced from O(N 3 ) is reduced to O(N 2 ), which can greatly improve the calculation speed and increase efficiency.

[0080] 302. Generate a result matrix according to the orthogonal decomposition data and the triangular decomposition data.

[0081] Calculation of the decomposed expression obtained by QR decomposition The result matrix , at this time the brightness compensation coefficient is:

[0082] The result matrix generated in this way can better calculate the brightness compensation coefficient.

[0083] See also Figure 4 The present application provides an embodiment of a method for acquiring an image of a display screen to be tested, wherein the display screen to be tested is a display screen composed of a plurality of flexible spliced ​​screens, including: 401. Combine the flexible spliced ​​display screens to form a display screen to be tested.

[0084] In this embodiment, the flexible splicing screen is a new type of display screen, which has the functions of bendability and multi-piece assembly. It is different from the conventional splicing screen. The conventional splicing screen is a fixed block, and the curvature of the splicing screen needs to be designed in advance, and then spliced ​​to form a large-scale curved screen. The flexible splicing screen of the present application can still be bent after splicing, and the pixels between the splicing screens will not be cut.

[0085] A fixed edge connection layer is added to a certain level inside the traditional flexible display screen, so that two flexible screens can be connected through the edge connection layer, and the edge connection layer is located below the pixel layer to avoid affecting the display of the pixel layer of the display screen. However, there are certain problems with the existing edge connection layer, that is, the edge connection layer is usually magnetic splicing plus interlocking splicing, and a corresponding structure needs to be designed on the edge connection layer. This type of structure is relatively small, which makes it very easy for this structure to have alignment errors during the splicing process. This error will cause further abnormalities in the subsequent splicing, thereby generating warping defects. The warping defects can be divided into linear warping defects. This type of defect will cause linear convex warping on the flexible splicing screen and drive other flexible splicing screens (flexible splicing screens connected to it) to produce linear warping together. Another type of nonlinear warping defect is that at least two flexible splicing screens connected to the target flexible splicing screen are around the target flexible splicing screen, and at the same time, the target flexible splicing screen is not aligned, so that the target flexible splicing screen is internally squeezed and irregular warping is generated.

[0086] 402 , the display screen to be tested is subjected to lighting processing of different preset display grayscale images, the central point area of ​​the display screen to be tested is lit, and the central pixel point of each flexible spliced ​​display screen is lit.

[0087] The terminal lights up the display screen to be tested with different preset grayscale images, lights up the center point area of ​​the display screen to be tested, and lights up the center pixel of each flexible spliced ​​display screen. That is, the center position of the whole and each spliced ​​screen is determined. The center point area can be in the middle of a flexible spliced ​​screen or between four flexible spliced ​​screens, which is not limited here.

[0088] 403 . Adjust the position of the sampling camera so that the sampling center of the sampling camera is aligned with the center point of the central point area, and collect the first image to be adjusted at the center point of the central point area by the sampling camera.

[0089] The terminal adjusts the position of the sampling camera so that the sampling center of the sampling camera is aligned with the center point of the central point area, and collects the first image to be adjusted at the center point of the central point area through the sampling camera.

[0090] 404 . Perform a warping analysis on the display screen to be tested according to the position coordinates of the central point area in the first image to be adjusted, the coordinate information and the grayscale information of the central pixel point of each flexible spliced ​​display screen.

[0091] The terminal first performs a preliminary detection based on the position coordinates of the center point area and the coordinate information of the center pixel of each flexible spliced ​​display screen, and then performs further detection using the grayscale information. The specific analysis steps are described in the subsequent embodiments.

[0092] 405. When the warpage analysis result shows that there is no flexible splicing warpage defect, the display screen to be tested is lit up with different preset display grayscale images, and is collected by a sampling camera to generate a collected image.

[0093] When the warping analysis results show that there is no flexible splicing warping defect, the display screen to be tested is lit up with different preset display grayscale images, that is, a uniform grayscale image of the area with no center point lighting is input, and collected by a sampling camera to generate a corresponding collected image.

[0094] See also Figure 5 The present application provides an embodiment of a method for processing a flexible splicing warping defect, comprising: 501. When the warpage analysis result shows that there is a flexible splicing warpage defect, the flexible splicing screen with the flexible splicing warpage defect is replaced according to the warpage analysis result.

[0095] 502. Capture images of the replaced display screen to be tested again until there is no flexible splicing warping defect, and recapture images by using a sampling camera to generate captured images.

[0096] When the warpage analysis result shows that there is a flexible splicing warpage defect, the terminal replaces the flexible splicing screen with the flexible splicing warpage defect according to the warpage analysis result. Then the terminal will collect images of the replaced display screen again until there is no flexible splicing warpage defect, and re-collect through the sampling camera to generate a collected image.

[0097] See also Figure 6 The present application provides an embodiment of a method for analyzing warping defects of a flexible splicing screen, comprising: 601. Expand the points of the spliced ​​screen according to the position coordinates of the central point area in the first image to be adjusted, and generate a reference midpoint on each flexible spliced ​​screen area.

[0098] In this embodiment, the splicing screen points are first expanded according to the position coordinates of the center point area in the first image to be adjusted, and a reference midpoint is generated on each flexible splicing screen area. That is, based on a standard screen without warping defects as a reference, the position that the center point of each flexible splicing screen should reach is set.

[0099] 602. Perform collective orientation deviation analysis on the coordinate information of the central pixel point of each flexible spliced ​​display screen on the first image to be adjusted and the reference midpoint.

[0100] Next, the terminal uses the center point of the center point area as a reference to perform directional deviation analysis on the coordinate information of the reference midpoint and the detected central pixel point on each flexible splicing screen area. Specifically, the center point of the center point area is connected to the reference midpoint of each flexible splicing screen, and the straight line is used as the detection direction of the position deviation. Then, the coordinate information of the central pixel point detected by each flexible splicing screen is calculated with the corresponding reference straight line for distance and direction calculation to generate the first distance direction and the first vertical distance.

[0101] Then, the coordinate information of the detected central pixel point and its corresponding reference midpoint are analyzed in terms of direction and distance to generate a second direction and a second distance.

[0102] The linear deviation of each flexible splicing screen is generated according to the first distance direction and the first vertical distance, and the nonlinear deviation is generated according to the second direction and the second distance. Because the nonlinear warping defect will affect the display of the central area of ​​the flexible splicing screen, it may be farther away from the sampling camera than other pixels, it may be closer to the sampling camera, and even the direction of the central pixel lighting is not aligned with the sampling camera. Therefore, it is necessary to analyze the second direction and the second distance.

[0103] The linear deviation and nonlinear deviation of a central pixel point and the central pixel points around it are analyzed to determine the possibility of warping defects in the corresponding flexible splicing screen.

[0104] 603. When the analysis result shows that a warping defect exists, determine a warping defect area on the first image to be adjusted according to the collective orientation deviation analysis result, where the warping defect area consists of at least one flexible spliced ​​display screen area.

[0105] When the analysis result shows that a warping defect exists, it is only preliminarily determined that it may exist and further testing is required. The terminal determines the possible warping defect area on the first image to be adjusted (i.e., at least one flexible spliced ​​screen with a warping defect) based on the collective orientation deviation analysis result. The warping defect area consists of at least one flexible spliced ​​display area.

[0106] 604. Generate a plurality of grayscale gradient images according to the warping defect direction information of the collective directional deviation analysis result and a preset gradient degree, wherein the gradient degree is determined by the size of the flexible splicing screen.

[0107] The terminal generates several grayscale gradient images based on the warping defect direction information and the preset gradient degree of the collective directional deviation analysis results. The gradient degree is determined by the size of the flexible splicing screen. First, the terminal generates the image grayscale range for linear warping defects and the image grayscale range for nonlinear warping according to the possibility of the warping type, and then sets the gradient degree according to the size of the flexible splicing screen. Then, the gradient direction is set according to the direction between the flexible splicing screen and the center point, and the corresponding grayscale gradient image is generated.

[0108] 605. Perform grayscale gradient lighting processing on the flexible spliced ​​screen corresponding to the warping defect area.

[0109] Next, the terminal lights up the grayscale gradient image on the flexible splicing screen corresponding to the warping defect area.

[0110] 606. Adjust the position of the sampling camera so that the sampling center of the sampling camera is aligned with the center point of the flexible splicing screen, and collect a warping defect detection image through the sampling camera.

[0111] The terminal adjusts the position of the sampling camera so that the sampling center of the sampling camera is aligned with the center point of the flexible splicing screen, and collects warping defect detection images through the sampling camera. Such warping defect detection images can specifically detect different types of warping defects.

[0112] 607. Perform a warping grayscale gradient analysis on the warping defect detection image to generate a warping analysis result.

[0113] The terminal performs a warping grayscale gradient analysis on the warping defect detection image to generate a warping analysis result. Specifically, the gradient analysis is performed based on the line connecting the center points of the flexible splicing screen and the center area of ​​the screen to be detected as the direction, and then determines whether the gradient has distortion of the warping defect.

[0114] See also Fig. 9 The present application provides an embodiment of a method for collecting images and performing image preprocessing, comprising: 901. Perform image preprocessing on the N captured images, where the image preprocessing is used to eliminate lens distortion, noise, and redundant background areas.

[0115] In this embodiment, before acquiring the brightness data, due to factors such as camera lens distortion, noise and background area, the target image should be preprocessed, including but not limited to coordinate transformation, geometric correction, Gaussian filtering, etc., to avoid abnormal compensation effect.

[0116] See also Fig.10 The present application provides an embodiment of a method for collecting a display screen image, comprising: 1001. Light up the display screen to be tested with different preset grayscale images, and light up the central point area of ​​the display screen to be tested; 1002. Adjust the position of the sampling camera so that the sampling center of the sampling camera is aligned with the center point of the display screen to be tested; 1003. Capture an image at the center point of the center point area using a sampling camera to generate a captured image.

[0117] In this embodiment, when the display screen is a conventional display screen, warping analysis may not be performed and data collection may be performed directly. The terminal lights up the display screen to be tested with different preset display grayscale images, lights up the center point area of ​​the display screen to be tested, and then adjusts the position of the sampling camera so that the sampling center of the sampling camera is aligned with the center point of the display screen to be tested. Finally, the sampling camera is used to collect images at the center point of the center point area to generate a collected image.

[0118] See also Figure 7 The present application provides an embodiment of a brightness compensation device based on step-by-step processing, comprising: The first acquisition unit 701 is used to acquire N captured images of the display screen to be tested at different preset display grayscales, where N is an integer greater than or equal to 2.

[0119] Optionally, the display screen to be tested is a display screen composed of several flexible spliced ​​screens.

[0120] The first acquisition unit 701 includes: The splicing module 7011 is used to combine the flexible splicing display screens to form a display screen to be tested.

[0121] The first lighting module 7012 is used to perform lighting processing on the display screen to be tested with different preset display grayscale images, light up the central point area of ​​the display screen to be tested, and light up the central pixel point of each flexible spliced ​​display screen.

[0122] The first acquisition module 7013 is used to adjust the position of the sampling camera so that the sampling center of the sampling camera is aligned with the center point of the central point area, and acquire the first image to be adjusted at the center point of the central point area through the sampling camera.

[0123] The analysis module 7014 is used to perform a warping analysis on the display screen to be tested according to the position coordinates of the central point area in the first image to be adjusted, the coordinate information and grayscale information of the central pixel point of each flexible spliced ​​display screen.

[0124] Optionally, the analysis module 7014 includes: The spliced ​​screen points are expanded according to the position coordinates of the central point area in the first image to be adjusted, and a reference midpoint is generated on each flexible spliced ​​screen area.

[0125] A collective orientation deviation analysis is performed on the coordinate information of the central pixel point of each flexible spliced ​​display screen on the first image to be adjusted and the reference midpoint.

[0126] When the analysis result shows that a warping defect exists, a warping defect area on the first image to be adjusted is determined according to the collective orientation deviation analysis result, and the warping defect area consists of at least one flexible spliced ​​display screen area.

[0127] Several grayscale gradient images are generated based on the warping defect direction information of the collective directional deviation analysis results and the preset gradient degree, and the gradient degree is determined by the size of the flexible splicing screen.

[0128] A grayscale gradient image is lit up on the flexible spliced ​​screen corresponding to the warping defect area.

[0129] The position of the sampling camera is adjusted so that the sampling center of the sampling camera is aligned with the center point of the flexible splicing screen, and the warping defect detection image is collected by the sampling camera.

[0130] Perform warping grayscale gradient analysis on the warping defect detection image to generate the warping analysis result.

[0131] The replacement module 7015 is used to replace the flexible splicing screen with the flexible splicing warping defect according to the warping analysis result when the warping analysis result shows that there is a flexible splicing warping defect.

[0132] The generating module 7016 is used to collect images of the replaced display screen to be tested again until there is no flexible splicing warping defect, and to collect images again through the sampling camera to generate collected images.

[0133] The second acquisition module 7017 is used to light up the display screen to be tested with different preset display grayscale images when the warpage analysis result shows that there is no flexible splicing warpage defect, and to acquire images through a sampling camera to generate acquired images.

[0134] The second acquisition unit 702 is used to acquire the position data and brightness data of each pixel in each captured image.

[0135] The first generating unit 703 is used to model the brightness compensation coefficients of all collected images and generate relationship matrix data, where the relationship matrix data includes brightness compensation coefficient matrix data, pixel position matrix data and brightness order matrix data.

[0136] The second generating unit 704 is used to adjust the relationship matrix data according to the brightness data, position data and a predetermined data order of each pixel point in each collected image to generate a brightness compensation coefficient expression.

[0137] The third generating unit 705 is used to perform decomposition and optimization according to the brightness transformation matrix corresponding to the brightness order matrix data in the brightness compensation coefficient expression to generate a result matrix.

[0138] Optionally, the third generating unit 705 generates a result matrix including: The brightness transformation matrix corresponding to the brightness order matrix data in the brightness compensation coefficient expression is decomposed and optimized to generate an orthogonal data set and a triangular data set.

[0139] Generate a result matrix based on the orthogonal decomposition data and the triangular decomposition data.

[0140] The fourth generating unit 706 is used to generate N*N coefficient images in sequence from the elements of the result matrix, and the value of each coefficient image is any element of the result matrix.

[0141] The fifth generating unit 707 is used to process the NxN coefficient images and the N acquired images in steps, and integrate the coefficient images and the acquired images in the same position pixels in units of images according to the pixel position matrix to generate brightness compensation coefficients.

[0142] Optionally, the fifth generating unit 707 includes: Determine the coefficient image of the first row in the NxN coefficient images and the corresponding first acquisition image, integrate the pixels at the same position according to the pixel position matrix and in units of images, and generate a first step processing result.

[0143] Determine the coefficient image of the second row in the NxN coefficient images and the corresponding second acquisition image, integrate the pixel points at the same position according to the pixel position matrix and in units of images, and then combine the integration result with the first step processing result to generate a second step processing result.

[0144] Similarly, determine the coefficient image of the Nth row and the corresponding Nth acquired image, integrate the pixels at the same position according to the pixel position matrix and in units of images, and then combine the integration result with the N-1th step processing result to generate the target processing result.

[0145] The target processing result is determined as a brightness compensation coefficient.

[0146] The compensation unit 708 is used to compensate the brightness of each pixel using a brightness compensation coefficient.

[0147] See also Figure 8 , the present application provides a brightness compensation device based on step-by-step processing, comprising: Processor 801 , memory 802 , input-output unit 803 , and bus 804 .

[0148] The processor 801 is connected to the memory 802 , the input and output unit 803 , and the bus 804 .

[0149] The memory 802 stores a program, and the processor 801 calls the program to execute the following steps: Figure 1 , Figure 2 and Figure 3 , Figure 4 , Figure 5 , Figure 6 , Fig. 9 and Fig.10 Brightness compensation method in .

[0150] The present application provides a computer-readable storage medium, on which a program is stored, and when the program is executed on a computer, the program performs the following steps: Figure 1 , Figure 2 and Figure 3 , Figure 4 , Figure 5 , Figure 6 , Fig. 9 and Fig.10 Brightness compensation method in .

[0151] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0152] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0153] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present application.

[0154] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0155] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, read-only memory), random access memory (RAM, random access memory), disk or optical disk and other media that can store program code.

Claims

1. A brightness compensation method based on step-by-step processing, characterized in that: include: Obtain N captured images of the display screen to be tested at different preset display grayscales, where N is an integer greater than or equal to 2; Obtain the position data and brightness data of each pixel in each collected image; Modeling the brightness compensation coefficients of all collected images to generate relationship matrix data, wherein the relationship matrix data includes brightness compensation coefficient matrix data, pixel position matrix data and brightness order matrix data; The relationship matrix data is adjusted according to the brightness data, position data and a predetermined data order of each pixel point in each collected image to generate a brightness compensation coefficient expression; Decomposing and optimizing the brightness transformation matrix corresponding to the brightness order matrix data in the brightness compensation coefficient expression to generate a result matrix; Generate N*N coefficient images in sequence from the elements of the result matrix, where the value of each coefficient image is any element of the result matrix; The NxN coefficient images and the N collected images are processed step by step, and the coefficient images and the collected images are sequentially integrated with pixels at the same position in units of images according to the pixel position matrix to generate brightness compensation coefficients; The brightness compensation coefficient is used to compensate the brightness of each pixel.

2. The brightness compensation method according to claim 1, characterized in that: The step of decomposing and optimizing the brightness transformation matrix corresponding to the brightness order matrix data in the brightness compensation coefficient expression to generate a result matrix comprises: The QR decomposition method is used to decompose and optimize the brightness transformation matrix corresponding to the brightness order matrix data in the brightness compensation coefficient expression to generate a result matrix.

3. The brightness compensation method according to claim 2, characterized in that: The step of using the QR decomposition method to decompose and optimize the brightness transformation matrix corresponding to the brightness order matrix data in the brightness compensation coefficient expression to generate a result matrix includes: Performing QR decomposition optimization on the brightness transformation matrix corresponding to the brightness order matrix data in the brightness compensation coefficient expression to generate an orthogonal data set and a triangular data set; A result matrix is ​​generated according to the orthogonal decomposition data and the triangular decomposition data.

4. The brightness compensation method according to claim 2, characterized in that: The step of processing the NxN coefficient images and the N collected images in steps, integrating the coefficient images and the collected images in steps according to the pixel position matrix for pixels at the same position in units of images, and generating brightness compensation coefficients comprises: Determine the coefficient image of the first row in the NxN coefficient images and the corresponding first acquisition image, integrate the pixel points at the same position according to the pixel position matrix and in units of images, and generate a first step processing result; Determine the coefficient image of the second row in the NxN coefficient images and the corresponding second acquisition image, integrate the pixel points at the same position according to the pixel position matrix and in units of images, and then combine the integration result with the first step processing result to generate a second step processing result; Similarly, the coefficient image of the Nth row and the corresponding Nth acquisition image are determined, the pixel points at the same position are integrated according to the pixel position matrix and in units of images, and then the integration result is combined with the N-1th step processing result to generate the target processing result; The target processing result is determined as a brightness compensation coefficient.

5. The brightness compensation method according to claim 1, characterized in that: After the steps of acquiring N captured images of the display screen to be tested at different preset display grayscales, and acquiring position data and brightness data of each pixel in each captured image, the brightness compensation method further includes: The N captured images are subjected to image preprocessing, wherein the image preprocessing is used to eliminate lens distortion, noise and redundant background areas.

6. The brightness compensation method according to any one of claims 1 to 5, characterized in that: The step of obtaining N captured images of the display screen to be tested at different preset display grayscales includes: The display screen to be tested is subjected to lighting processing of different preset display grayscale images, and the central point area of ​​the display screen to be tested is lit; Adjusting the position of the sampling camera so that the sampling center of the sampling camera is aligned with the center point of the display screen to be tested; The sampling camera collects images at the center point of the center point area to generate a collected image.

7. The brightness compensation method according to any one of claims 1 to 5, characterized in that: The display screen to be tested is a display screen composed of several flexible spliced ​​screens; The step of obtaining N captured images of the display screen to be tested at different preset display grayscales includes: Combine the flexible spliced ​​display screens to form a display screen to be tested; The display screen to be tested is subjected to lighting processing of different preset display grayscale images, the central point area of ​​the display screen to be tested is lit, and the central pixel point of each flexible spliced ​​display screen is lit; Adjusting the position of the sampling camera so that the sampling center of the sampling camera is aligned with the center point of the central point area, and collecting the first image to be adjusted at the center point of the central point area by the sampling camera; Performing a warping analysis on the display screen to be tested according to the position coordinates of the center point area in the first image to be adjusted, the coordinate information and grayscale information of the center pixel point of each flexible spliced ​​display screen; When the warping analysis result shows that there is no flexible splicing warping defect, the display screen to be tested is illuminated with different preset display grayscale images, and a sampling camera is used to collect images to generate collected images; When the warping analysis result shows that there is a flexible splicing warping defect, the flexible splicing screen with the flexible splicing warping defect is replaced according to the warping analysis result; The replaced display screen to be tested is imaged again until there is no flexible splicing warping defect, and the image is re-captured by the sampling camera to generate a captured image.

8. A brightness compensation device based on step-by-step processing, characterized in that: include: A first acquisition unit is used to acquire N captured images of the display screen to be tested at different preset display grayscales, where N is an integer greater than or equal to 2; A second acquisition unit is used to acquire position data and brightness data of each pixel in each collected image; A first generating unit, used for modeling the brightness compensation coefficients of all collected images to generate relationship matrix data, wherein the relationship matrix data includes brightness compensation coefficient matrix data, pixel position matrix data and brightness order matrix data; A second generating unit, used to adjust the relationship matrix data according to the brightness data, position data and a predetermined data order of each pixel point in each collected image, and generate a brightness compensation coefficient expression; A third generating unit is used to perform decomposition optimization according to the brightness transformation matrix corresponding to the brightness order matrix data in the brightness compensation coefficient expression to generate a result matrix; A fourth generating unit, used to sequentially generate N*N coefficient images from the elements of the result matrix, wherein the value of each coefficient image is any element of the result matrix; A fifth generating unit is used to process the NxN coefficient images and the N collected images in steps, and integrate the coefficient images and the collected images in the same position pixels in units of images according to the pixel position matrix to generate brightness compensation coefficients; The compensation unit is used to compensate the brightness of each pixel using the brightness compensation coefficient.

9. The brightness compensation device according to claim 8, characterized in that: The third generating unit comprises: Decomposing and optimizing the brightness transformation matrix corresponding to the brightness order matrix data in the brightness compensation coefficient expression to generate an orthogonal data set and a triangular data set; A result matrix is ​​generated according to the orthogonal decomposition data and the triangular decomposition data.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program, and when the program is executed on a computer, the brightness compensation method according to any one of claims 1 to 7 is performed.

Images