A method, system and electronic device for extracting sub-pixel luminance of a display

Abstract

The application discloses a method, system and electronic equipment for extracting display screen sub-pixel brightness, and is used for improving the precision of extracting display screen sub-pixel brightness. The method for extracting display screen sub-pixel brightness comprises the following steps: acquiring a first shooting image and a second shooting image; performing color channel separation on the second shooting image to obtain a separated image; calculating the center coordinates of each sub-pixel in the separated image and storing the center coordinates into a row coordinate image and a column coordinate image; performing two-dimensional mean filtering on the row coordinate image and the column coordinate image respectively, and arranging to obtain the correction coordinates of the sub-pixels in the non-edge region; performing linear interpolation on the correction coordinates of the sub-pixels in the non-edge region adjacent to the sub-pixels in the edge region to obtain the correction coordinates of the sub-pixels in the edge region; acquiring the two-dimensional Gaussian function expression corresponding to each sub-pixel according to the correction coordinates of the sub-pixels and a sub-pixel block algorithm; and calculating the accurate brightness corresponding to each sub-pixel by using a weighted estimation algorithm.

Description

Technical Field

[0001] This application relates to the field of display screen repair technology, and in particular to a method, system, and electronic device for extracting the brightness of sub-pixels of a display screen. Background Technology

[0002] In the manufacturing process of OLED display panels, due to multiple factors such as material properties and process precision, the display screen is prone to Mura defects, which result in uneven color and brightness. Therefore, display panels need to use optical Demura systems to compensate for and repair Mura defects. This involves using a high-resolution industrial camera to capture images of the lit display screen and locating and extracting the brightness of sub-pixels from the captured images. This allows for the detection of Mura defects on the panel and the performance of high-precision brightness compensation, thereby optimizing display quality and improving yield. Traditional compensation and repair methods typically use the neighborhood maxima method for sub-pixel brightness extraction. This involves finding the local maximum brightness value in the captured image as an approximate location of the sub-pixel, and then accumulating the brightness values ​​of the camera pixels within the surrounding region of interest (ROI) centered on this location to obtain the brightness value of that sub-pixel.

[0003] However, when the LED density of the display increases, leading to a decrease in the sampling rate, the number of camera pixels corresponding to each sub-pixel decreases, and the signal distribution of the sub-pixels in the image is easily affected by noise. At this time, the neighborhood maxima method will produce a serious pixel misalignment error due to insufficient camera sampling, that is, the camera pixel grid and the panel sub-pixel grid cannot be precisely aligned, resulting in a deviation in pixel brightness extraction, poor Demura compensation effect, and a relatively rough visual effect of the compensated display panel. Summary of the Invention

[0004] This application discloses a method, system, and electronic device for extracting the brightness of sub-pixels of a display screen, which improves the accuracy of extracting the brightness of sub-pixels of a display screen.

[0005] The first aspect of this application discloses a method for extracting the brightness of sub-pixels of a display screen, comprising:

[0006] An industrial camera is used to acquire a first image of the target positioning map and a second image of the target grayscale map, which are obtained according to the physical characteristics of the display screen to be compensated.

[0007] Perform color channel separation on the second captured image to obtain a separated image;

[0008] Based on the positioning information in the first captured image, the center coordinates of each sub-pixel in the separated image are calculated, and the row coordinates and column coordinates of the center coordinates are stored in the row coordinate image and column coordinate image respectively.

[0009] For sub-pixels in non-edge regions, a preset target filter is used to perform two-dimensional mean filtering on the row coordinate image and the column coordinate image respectively, and the corrected coordinates of the sub-pixels in non-edge regions are obtained.

[0010] For sub-pixels in the edge region, linear interpolation is performed on the corrected coordinates of sub-pixels in non-edge regions adjacent to the sub-pixels in the edge region to obtain the corrected coordinates of the sub-pixels in the edge region.

[0011] The two-dimensional Gaussian function expression corresponding to each sub-pixel is obtained based on the corrected coordinates of the sub-pixel and the sub-pixel block algorithm;

[0012] Based on the two-dimensional Gaussian function expression and the pixel values ​​of all camera sampling points corresponding to each sub-pixel in the separated image, a weighted estimation algorithm is used to calculate the precise brightness corresponding to each sub-pixel.

[0013] Optionally, obtaining the two-dimensional Gaussian function expression corresponding to each sub-pixel based on the corrected coordinates of the sub-pixel and the sub-pixel block algorithm includes:

[0014] Based on the separated image, the pixels of the industrial camera are divided into multiple sub-pixel sub-blocks of the same size, and the center coordinates of each sub-pixel sub-block are calculated;

[0015] Based on the corrected coordinates of the sub-pixel, the sub-pixel sub-block to which the target sub-pixel belongs is determined, wherein the target sub-pixel is the sub-pixel of the non-edge region and the edge region whose brightness is to be extracted;

[0016] Based on the center coordinates of the sub-pixel sub-block to which the target sub-pixel belongs, determine the two-dimensional Gaussian function expression corresponding to the target sub-pixel.

[0017] Optionally, the expression for the two-dimensional Gaussian function is: ;

[0018] Wherein, (x, y) are the coordinates to be input;

[0019] The g(x,y) is a two-dimensional Gaussian function;

[0020] (u,v) represents the center coordinates of the sub-pixel sub-block to which the target sub-pixel belongs;

[0021] σ represents the Gaussian kernel standard deviation.

[0022] Optionally, the target sub-pixel corresponds to D camera sampling points on the separated image, and before calculating the precise brightness corresponding to each sub-pixel using a weighted estimation algorithm, the method further includes:

[0023] The formula for obtaining the brightness estimate is: e d=p d / [g(x d ,y d )];

[0024] Wherein, the e d The brightness of the target sub-pixel is estimated based on the d-th camera sampling point;

[0025] The p d Let d be the pixel value of the d-th camera sampling point;

[0026] The g(x) d ,y d ) is the d-th camera sampling point (x) corresponding to the target sub-pixel. d ,y d The value of the two-dimensional Gaussian function;

[0027] The value of d is a non-zero integer, and the range of d is 1 to D;

[0028] The brightness of the target sub-pixel is calculated based on the brightness estimation formula, using the formula.

[0029] Optionally, the calculation formula for the weighted estimation algorithm is as follows: ;

[0030] Wherein, Lum is the precise brightness of the target sub-pixel;

[0031] The e d The brightness of the target sub-pixel is estimated based on the d-th camera sampling point;

[0032] The w d This represents the weight corresponding to the d-th camera sampling point.

[0033] Optionally, performing color channel separation on the second captured image to obtain a separated image includes:

[0034] The second captured image is subjected to color channel separation using a color space decomposition algorithm to obtain a red channel separated image, a green channel separated image, and a blue channel separated image.

[0035] The step of calculating the center coordinates of each sub-pixel in the separated image based on the positioning information in the first captured image includes:

[0036] The positioning information includes sub-pixel region identification features, which are used to identify sub-pixel region identification features in the first captured image and confirm the spatial region of each sub-pixel.

[0037] Based on the centroid calculation formula and the spatial region, the center coordinates of each sub-pixel of the red channel separated image, the green channel separated image, and the blue channel separated image are calculated respectively.

[0038] Optionally, the target filter is a rectangular filter with both an odd length and a wide width;

[0039] When the sub-pixel arrangement of the red channel separated image and the blue channel separated image is in the form of a diamond grid, the non-zero coefficients of the target filter corresponding to the red channel separated image and the non-zero coefficients of the target filter corresponding to the blue channel separated image are both centrally symmetrically distributed in a diamond grid.

[0040] When the sub-pixel arrangement of the green channel separated image is in the form of a rectangular grid, the non-zero coefficients of the target filter corresponding to the green channel separated image are distributed in a centrally symmetrical square grid.

[0041] Optionally, the step of linearly interpolating the corrected coordinates of the sub-pixels in the non-edge region adjacent to the sub-pixels of the edge region to obtain the corrected coordinates of the sub-pixels in the edge region includes:

[0042] Based on the length and width of the target filter, the edge regions of the separated image are identified, including the upper edge, lower edge, left edge, and right edge;

[0043] The corrected coordinates of the sub-pixels in the adjacent non-edge regions corresponding to the sub-pixels of the upper edge, the lower edge, the left edge, and the right edge are obtained respectively.

[0044] Based on the preset edge correction formula and the correction coordinates of the sub-pixels of the adjacent non-edge regions, the correction coordinates of the sub-pixels of the upper edge, the lower edge, the left edge, and the right edge are calculated respectively.

[0045] A second aspect of this application discloses a system for extracting the brightness of sub-pixels of a display screen, comprising:

[0046] The shooting unit is used to acquire a first image of a target positioning map and a second image of a target grayscale map through an industrial camera. The target positioning map and the target grayscale map are set according to the physical characteristics of the display screen to be compensated.

[0047] A separation unit is used to perform color channel separation on the second captured image to obtain a separated image;

[0048] The center coordinate calculation unit is used to calculate the center coordinates of each sub-pixel in the separated image based on the positioning information in the first captured image, and store the row coordinates and column coordinates of the center coordinates into the row coordinate image and column coordinate image respectively;

[0049] The first corrected coordinate unit is used to perform two-dimensional mean filtering on the row coordinate image and the column coordinate image respectively through a preset target filter for sub-pixels in non-edge regions, and sort them to obtain the corrected coordinates of the sub-pixels in non-edge regions.

[0050] The second correction coordinate unit is used to perform linear interpolation on the correction coordinates of the sub-pixels in non-edge regions adjacent to the sub-pixels in the edge region for the sub-pixels in the edge region, so as to obtain the correction coordinates of the sub-pixels in the edge region.

[0051] The unit for obtaining the two-dimensional Gaussian function expression is used to obtain the two-dimensional Gaussian function expression corresponding to each sub-pixel based on the corrected coordinates of the sub-pixel and the sub-pixel block algorithm.

[0052] The accurate brightness calculation unit is used to calculate the accurate brightness corresponding to each sub-pixel using a weighted estimation algorithm based on the two-dimensional Gaussian function expression and the pixel values ​​of all camera sampling points corresponding to each sub-pixel in the separated image.

[0053] A third aspect of this application provides an electronic device, comprising:

[0054] Processor, memory, input / output units, and bus;

[0055] The processor is connected to memory, input / output units, and a bus;

[0056] The memory stores a program, which the processor calls to execute methods such as the first aspect and any optional method of the first aspect for extracting the brightness of subpixels of the display screen.

[0057] The fourth aspect of this application provides a computer-readable storage medium on which a program is stored, which, when executed on a computer, performs a method for extracting the brightness of subpixels of a display screen, as described in the first aspect and any optional method of the first aspect.

[0058] As can be seen from the above technical solutions, the embodiments of this application have the following advantages:

[0059] This application provides a method for extracting the brightness of sub-pixels of a display screen. Using an industrial camera, a first image of a target positioning map and a second image of a target grayscale map are acquired. The target positioning map and the target grayscale map are set according to the physical characteristics of the display screen to be compensated. Color channels are separated in the second image to obtain a separated image. Based on the positioning information in the first image, the center coordinates of each sub-pixel in the separated image are calculated, and the row and column coordinates of the center coordinates are stored in the row coordinate image and column coordinate image, respectively. For sub-pixels in non-edge regions, a preset target filter is used to perform two-dimensional mean filtering on the row and column coordinate images to obtain corrected coordinates for the sub-pixels in non-edge regions. For sub-pixels in edge regions, linear interpolation is performed on the corrected coordinates of the non-edge region sub-pixels adjacent to the edge region sub-pixels to obtain corrected coordinates for the edge region sub-pixels. A two-dimensional Gaussian function expression corresponding to each sub-pixel is obtained based on the corrected coordinates of the sub-pixels and a sub-pixel block algorithm. Based on the two-dimensional Gaussian function expression and the pixel values ​​of all camera sampling points corresponding to each sub-pixel in the separated image, a weighted estimation algorithm is used to calculate the precise brightness corresponding to each sub-pixel. The method for extracting sub-pixel brightness of a display screen in this application involves customizing a target positioning map and a target grayscale map based on the physical characteristics of the display screen to be compensated, accurately calculating the initial center coordinates of the sub-pixels by combining the positioning information of the first captured image, then using a target filter adapted to the sub-pixel arrangement to perform two-dimensional mean filtering correction on the sub-pixels in non-edge regions, while optimizing the coordinates of sub-pixels in edge regions through linear interpolation, and using a sub-pixel block method to obtain a two-dimensional Gaussian function expression based on the corrected coordinates of the sub-pixels. Then, based on the two-dimensional Gaussian function expression and the pixel values ​​of all camera sampling points corresponding to the sub-pixels on the separated image, a weighted estimation method is used to calculate the accurate brightness of each sub-pixel. This significantly reduces the alignment deviation between the camera pixel grid and the panel sub-pixel grid, effectively corrects camera imaging distortion and noise interference under low sampling rates, and significantly improves the accuracy of extracting sub-pixel brightness. Attached Figure Description

[0060] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0061] Figure 1 This is a schematic diagram of an embodiment of the method for extracting the brightness of sub-pixels of a display screen according to this application;

[0062] Figure 2 This is a schematic diagram of another embodiment of the method for extracting the brightness of a sub-pixel of a display screen according to this application;

[0063] Figure 3 This is a schematic diagram of another embodiment of the method for extracting the brightness of a sub-pixel of a display screen according to this application;

[0064] Figure 4 This is a schematic diagram of another embodiment of the method for extracting the brightness of a sub-pixel of a display screen according to this application;

[0065] Figure 5 This is a schematic diagram of an embodiment of the system for extracting the brightness of sub-pixels of a display screen according to this application;

[0066] Figure 6 This is a schematic diagram of another embodiment of the electronic device of this application;

[0067] Figure 7 This is a schematic diagram of the target filter corresponding to the blue channel separated image in this application;

[0068] Figure 8 This is a schematic diagram of the target filter corresponding to the green channel separation image in this application;

[0069] Figure 9 This is a schematic diagram of the sub-pixels under the modified green channel in this application;

[0070] Figure 10 This is a schematic diagram of the sub-pixels under the modified blue channel in this application. Detailed Implementation

[0071] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0072] It should be understood that, when used in this application specification, the term "comprising" indicates the presence of the described feature, integral, step, operation, element, and / or component, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or collections thereof.

[0073] It should also be understood that the term “and / or” as used in this application specification means any combination of one or more of the associated listed items, as well as all possible combinations, and includes such combinations.

[0074] As used in this application specification, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if [the described condition or event] is detected" may be interpreted, depending on the context, as "once determined," "in response to determination," "once [the described condition or event] is detected," or "in response to detection of [the described condition or event]."

[0075] Furthermore, in the description of this application, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0076] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0077] Based on this, this application discloses a method, system, and electronic device for extracting the brightness of sub-pixels of a display screen, which can improve the accuracy of extracting the brightness of sub-pixels of a display screen.

[0078] The technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0079] The method described in this application can be applied to servers, devices, terminals, or other devices with logical processing capabilities; therefore, this application does not limit its application. For ease of description, the following description uses a terminal as the executing entity.

[0080] Please see Figure 1 This application provides an embodiment of a method for extracting the brightness of sub-pixels of a display screen, comprising:

[0081] 101. Using an industrial camera, acquire a first captured image of the corresponding target positioning map and a second captured image of the corresponding target grayscale map. The target positioning map and the target grayscale map are set according to the physical characteristics of the display screen to be compensated.

[0082] In this embodiment, the physical characteristics of the display screen to be compensated are first defined, such as a resolution of 3840×2160 for 4K or 7680×4320 for 8K, a diamond or rectangular subpixel arrangement, and grayscale response characteristics corresponding to different grayscale values. The target positioning map should be set to match the above physical characteristics, thereby embedding subpixel arrangement markers, channel differentiation marks, and boundary positioning features in the map to ensure accurate correspondence to the subpixel distribution range of the display screen; the target grayscale map is set to a standard grayscale value range of 0~255, and adopts a full-screen uniform illumination mode design to avoid signal interference in subsequent color channel separation and brightness extraction due to uneven grayscale distribution.

[0083] The pre-set target positioning map and target grayscale map are sent to the display screen to be compensated, controlling the display screen to accurately display these two types of reference images. In a darkroom environment, a high-resolution industrial camera with a Bayer filter is used to capture images according to shooting parameters that match the display screen's grayscale values ​​and luminance, acquiring a first image corresponding to the target positioning map and a second image corresponding to the target grayscale map. The first image carries the spatial positioning information of sub-pixels, and the second image carries the luminance signal information of sub-pixels.

[0084] 102. Perform color channel separation on the second captured image to obtain a separated image;

[0085] In this embodiment, a color space decomposition algorithm adapted to the color restoration logic of the Bayer filter is used to process the second captured image. Typically, the second captured image contains a luminance signal composed of a mixture of R, G, and B colors. Therefore, the algorithm can separate three independent single-channel images: a red channel separated image, a green channel separated image, and a blue channel separated image. Each channel separated image corresponds to a set of sub-pixels emitting light of the same color in the display screen to be compensated, ensuring that subsequent positioning and luminance extraction can be performed on sub-pixels of a single color channel.

[0086] 103. Based on the positioning information in the first captured image, calculate the center coordinates of each sub-pixel in the separated image, and store the row coordinates and column coordinates of the center coordinates into the row coordinate image and column coordinate image respectively;

[0087] In this embodiment, the positioning information in the first captured image includes preset sub-pixel region identifier features. These sub-pixel region identifier features are then extracted using an image recognition algorithm to accurately delineate the spatial region of each sub-pixel in the separated image, thus clarifying the boundary range of each sub-pixel in the separated image. Subsequently, using the centroid calculation formula, based on the pixel distribution within each sub-pixel's spatial region, the geometric center coordinates of that region are calculated and used as the initial center coordinates with pixel-level precision for the corresponding sub-pixel. Then, for all sub-pixels' initial center coordinates with pixel-level precision, two independent coordinate storage images are constructed: a row coordinate image and a column coordinate image. The row coordinate image stores the row coordinate values ​​of the corresponding sub-pixel's center coordinates; the column coordinate image stores the column coordinate values ​​of the corresponding sub-pixel's center coordinates. This separate storage method allows for batch processing of subsequent coordinate filtering and correction.

[0088] 104. For sub-pixels in non-edge regions, two-dimensional mean filtering is performed on the row coordinate image and column coordinate image respectively using a preset target filter to obtain the corrected coordinates of the sub-pixels in non-edge regions.

[0089] In this embodiment, a target filter is designed based on the sub-pixel arrangement of the separated images. Specifically, the target filter is set as a rectangle with both odd length and width, and the non-zero coefficients of the target filter are set according to the sub-pixel arrangement of the separated images. Then, the target filter is applied to both the row coordinate image and the column coordinate image for two-dimensional mean filtering. This utilizes the characteristic of "uniform sub-pixel arrangement within a small area of ​​the screen" to smooth the positioning error of the initial coordinates, resulting in filtered row and column coordinate images. It should be noted that due to inherent limitations of the target filter, data in edge regions cannot be processed using two-dimensional mean filtering. Therefore, the filtered row and column coordinates belong to the coordinates of sub-pixels in non-edge regions. Finally, the row and column coordinates of corresponding sub-pixels in the two filtered images are combined to obtain the corrected coordinates of the sub-pixels in non-edge regions, with sub-pixel precision.

[0090] It should be noted that the target filter is a rectangular filter with both an odd length and width. Based on the separation images in step 102 being red channel separation images, blue channel separation images, and green channel separation images, the target filters for different separation images differ. For example, when the sub-pixel arrangement of the red and blue channel separation images is a diamond grid, the non-zero coefficients of the target filter corresponding to both the red and blue channel separation images are centrally symmetrically distributed in a diamond grid. When the sub-pixel arrangement of the green channel separation image is a rectangular grid, the non-zero coefficients of the target filter corresponding to the green channel separation image are centrally symmetrically distributed in a square grid. (Refer to...) Figure 7 and Figure 8 , Figure 7 This is a schematic diagram of the target filter corresponding to the blue channel separated image in this application; Figure 8 This is a schematic diagram of the target filter corresponding to the green channel separation image in this application.

[0091] 105. For sub-pixels in the edge region, perform linear interpolation on the corrected coordinates of sub-pixels in non-edge regions adjacent to the sub-pixels in the edge region to obtain the corrected coordinates of the sub-pixels in the edge region.

[0092] In this embodiment, based on the inherent defects of the target filter mentioned in step 106, which prevents filtering of data in the edge region, the edge region range is first determined according to the length and width of the target filter (both odd numbers), thereby determining the sub-pixels of the edge region. For these sub-pixels that cannot be corrected by direct filtering, the corrected coordinates of the adjacent non-edge region sub-pixels are first obtained; then, a linear interpolation algorithm is used, taking the sub-pixels of the upper edge as an example, the coordinate compensation amount of the edge sub-pixels is derived based on the difference in corrected coordinates of adjacent non-edge sub-pixels, and the corrected coordinates of the edge sub-pixels are calculated using a linear formula; the correction process for the sub-pixels of the lower edge, left edge, and right edge refers to the logic of the upper edge, ensuring that the corrected coordinates of all edge sub-pixels are obtained, thereby achieving sub-pixel-level positioning accuracy for all sub-pixels on the screen.

[0093] 106. Obtain the two-dimensional Gaussian function expression for each sub-pixel based on the corrected coordinates of the sub-pixels and the sub-pixel block algorithm;

[0094] In this embodiment, a single pixel of the industrial camera is uniformly divided into multiple sub-pixel sub-blocks, ensuring that the sub-blocks do not overlap and completely cover the camera pixels. Then, the corrected coordinates of the sub-pixels in the non-edge and edge regions are retrieved, and the sub-pixel sub-block to which these coordinates fall is determined, thus identifying the center coordinates of the corresponding sub-pixel sub-block. Next, based on the center coordinates of the sub-pixel sub-blocks, a two-dimensional Gaussian function expression is determined for each sub-pixel sub-block; that is, sub-pixels falling into the same sub-pixel sub-block will use the same two-dimensional Gaussian function expression in subsequent steps.

[0095] 107. Based on the two-dimensional Gaussian function expression and the pixel values ​​of all camera sampling points corresponding to each sub-pixel in the separated image, use a weighted estimation algorithm to calculate the precise brightness corresponding to each sub-pixel.

[0096] In this embodiment, the pixel values ​​of all camera sampling points corresponding to the target sub-pixel in the separated image are extracted. That is, multiple effective sampling points are selected within the sub-pixel sub-block range to which the corrected coordinates of the sub-pixel belong, centered on the corrected coordinates of the sub-pixel, and the pixel values ​​of each sampling point are obtained. Then, based on the pixel value of each sampling point and the two-dimensional Gaussian function expression obtained in step 106, the brightness of each sub-pixel estimated based on a single sampling point is obtained. This brightness is only an estimate and cannot be directly used as the final result. Next, a weight is assigned to each brightness, and the accurate brightness corresponding to each sub-pixel is obtained through a weighted estimation algorithm. This accurate brightness is the final result, that is, the brightness extraction of the sub-pixel is completed.

[0097] In this embodiment, the method for extracting the brightness of sub-pixels of the display screen is to customize a target positioning map and a target grayscale map according to the physical characteristics of the display screen to be compensated, accurately calculate the initial center coordinates of the sub-pixels by combining the positioning information of the first captured image, and then use a target filter adapted to the sub-pixel arrangement to perform two-dimensional mean filtering correction on the sub-pixels in the non-edge region. At the same time, the coordinates of the sub-pixels in the edge region are optimized by linear interpolation, and a two-dimensional Gaussian function expression is obtained based on the corrected coordinates of the sub-pixels using a sub-pixel block method. Then, based on the two-dimensional Gaussian function expression and the pixel values ​​of all camera sampling points corresponding to the sub-pixels on the separated image, the accurate brightness of each sub-pixel is calculated by a weighted estimation method. This significantly reduces the alignment deviation between the camera pixel grid and the panel sub-pixel grid, effectively corrects camera imaging distortion and noise interference under low sampling rate, and significantly improves the accuracy of extracting the brightness of sub-pixels.

[0098] Please see Figure 2 Step 106 involves obtaining the two-dimensional Gaussian function expression for each sub-pixel based on the corrected coordinates of the sub-pixels and the sub-pixel block algorithm. Step 107 involves calculating the precise brightness of each sub-pixel using a weighted estimation algorithm based on the two-dimensional Gaussian function expression and the pixel values ​​of all camera sampling points corresponding to each sub-pixel in the separated image. Specifically, this may include, but is not limited to, the following:

[0099] 201. Based on the separated image, divide the pixels of the industrial camera into multiple sub-pixel sub-blocks of the same size, and calculate the center coordinates of each sub-pixel sub-block;

[0100] 202. Based on the corrected coordinates of the sub-pixels, determine the sub-pixel sub-block to which the target sub-pixel belongs. The target sub-pixel is the sub-pixel of the non-edge region and the edge region whose brightness is to be extracted.

[0101] 203. Determine the two-dimensional Gaussian function expression corresponding to the target sub-pixel based on the center coordinates of the sub-pixel sub-block to which the target sub-pixel belongs;

[0102] 204. Obtain the pixel values ​​of all camera sampling points corresponding to the target sub-pixel in the separated image;

[0103] 205. Obtain the formula for calculating brightness estimation;

[0104] 206. Calculate the brightness of the target sub-pixel based on the estimated brightness of the d-th camera sampling point using the brightness estimation formula;

[0105] 207. Using the weighted estimation algorithm, the accurate brightness of the target sub-pixel is calculated based on the brightness of the target sub-pixel estimated from the d-th camera sampling point.

[0106] In this embodiment, based on the pixel grid of the separated image, a single camera pixel corresponding to the center of the corrected sub-pixel within the image acquired by the industrial camera is locked, and it is uniformly divided into K... 2 K is a positive integer ≥ 2, consisting of three identical square sub-blocks. The sub-block boundaries must be parallel to the camera pixel boundaries, with no overlap between sub-blocks and complete coverage of the camera pixels. Then, based on the physical coordinates of the camera pixels, the geometric center coordinates (u, v) of each sub-block are calculated. These center coordinates will serve as the center of the Gaussian function used to approximate the point spread function.

[0107] The target sub-pixel includes all sub-pixels in both non-edge and edge regions for which brightness is to be extracted. First, the corrected coordinates of the non-edge region sub-pixels obtained in step 104 and the corrected coordinates of the edge region sub-pixels obtained in step 105 are retrieved to form a set of sub-pixel precision coordinates for the entire screen sub-pixels. Then, the corrected coordinates (x, y) of each target sub-pixel are matched with the coordinate range of the camera pixel sub-block to determine the specific sub-block interval into which the corrected coordinates fall, thereby determining the camera pixel sub-block corresponding to each target sub-pixel and establishing the correspondence between target sub-pixels and sub-blocks.

[0108] Based on the center coordinates (u, v) of the sub-block corresponding to the target sub-pixel obtained in step 401, the corresponding two-dimensional Gaussian function expression is determined. This yields the two-dimensional Gaussian function used to approximate the point spread function of the target sub-pixel. This two-dimensional Gaussian function characterizes the diffusion distribution characteristics of the sub-pixel brightness within the camera pixels. It should be noted that the expression for the two-dimensional Gaussian function is: ;

[0109] Where (x,y) are the coordinates to be input;

[0110] g(x,y) is a two-dimensional Gaussian function;

[0111] (u,v) represents the center coordinates of the sub-pixel sub-block to which the target sub-pixel belongs;

[0112] σ represents the standard deviation of the Gaussian kernel. Therefore, there is a corresponding relationship between the target sub-pixel and the two-dimensional Gaussian function expression.

[0113] Next, using the corrected coordinates of the target sub-pixel as the center, select D camera sampling points (D is a positive integer ≥ 4) corresponding to the target sub-pixel in the separated image. Then, directly extract the pixel values ​​p of these camera sampling points in the separated image. d (d=1,2,...,D) Organize the pixel values ​​of all camera sampling points into a set for subsequent brightness calculation.

[0114] Retrieve all the camera sampling point pixel values ​​obtained in step 404, and the two-dimensional Gaussian function expression corresponding to the target sub-pixel obtained in step 403. Substitute the pixel values ​​of each group of camera sampling points, the coordinates of each group of camera sampling points, and the two-dimensional Gaussian function expression of the target sub-pixel into the brightness estimation calculation formula in sequence to obtain the brightness of the target sub-pixel estimated based on the pixel values ​​of each camera sampling point in the corresponding group. The brightness estimation calculation formula is: e d =p d / [g(x d ,y d )];

[0115] Among them, e d The brightness of the target sub-pixel is estimated based on the d-th camera sampling point;

[0116] p d Let d be the pixel value of the d-th camera sampling point;

[0117] g(x d ,y d ) represents the Gaussian function value of the target sub-pixel at the d-th camera sampling point;

[0118] d is a non-zero integer, and the range of d is 1 to D.

[0119] The specific calculation steps are as follows: First, determine the two-dimensional Gaussian function expression using the center coordinates (u,v) of the sub-pixel sub-block. Then, substitute the coordinates (xd, yd) of the d-th camera sampling point corresponding to the target sub-pixel into the two-dimensional Gaussian function expression to obtain g(x d ,y d Then, the pixel value p of the d-th camera sampling point is... d and g(x) d ,y d Substituting into the brightness estimation formula, we get: e d =p d / [g(x d ,y d )], to obtain a definite e d The value of .

[0120] Then, the brightness estimate obtained in step 406 and the preset weights are substituted into the preset brightness extraction model. By calculating the ratio of weighted summation to weighted summation, the accurate brightness value of the target sub-pixel is finally obtained. This brightness value can be directly used for Demura color brightness compensation of OLED display panels.

[0121] It should be noted that the preset brightness extraction model is a weighted average model, and the calculation formula for the weighted average model is as follows: ;

[0122] Where Lum is the brightness of the target sub-pixel;

[0123] e d The brightness of the target sub-pixel is calculated based on the d-th camera sampling point;

[0124] w d This represents the weight corresponding to the d-th camera sampling point.

[0125] Furthermore, the weight w corresponding to each camera sampling point d The selection can be made according to the actual situation. The selection criteria can be the pixel value of the camera sampling point, the reciprocal of the distance from the point to the center coordinate of the corresponding sub-pixel, or the value of the Gaussian function of the corresponding sub-pixel at the camera sampling point.

[0126] Please see Figure 3 Step 102, which involves separating the color channels of the second captured image to obtain a separated image, may specifically include, but is not limited to, the following:

[0127] 301. Using a color space decomposition algorithm, the second captured image is subjected to color channel separation to obtain a red channel separated image, a green channel separated image, and a blue channel separated image;

[0128] 302. The positioning information includes sub-pixel region identification features. The sub-pixel region identification features in the first captured image are identified to confirm the spatial region of each sub-pixel.

[0129] 303. Based on the centroid calculation formula and spatial region, calculate the center coordinates of each sub-pixel of the red channel separated image, green channel separated image and blue channel separated image respectively.

[0130] In this embodiment, a color space decomposition algorithm adapted to the characteristics of Bayer filters in industrial cameras, such as an interpolation decomposition algorithm based on a Bayer array, is used to perform channel separation processing on the second captured image. The algorithm identifies the filter color type corresponding to pixels at different positions in the image and, combined with a color restoration model, decomposes the mixed signal into independent red, green, and blue luminance components. Finally, three non-interfering single-channel images are output: a red channel separated image, a green channel separated image, and a blue channel separated image. Each image retains only the luminance information of the corresponding color sub-pixels.

[0131] The first captured image mentioned in step 101 carries preset positioning information for the target positioning map. This positioning information includes sub-pixel region identification features, such as sub-pixel boundary markers, channel-distinguishing textures, and grid distribution markers. Using image feature recognition algorithms, such as edge detection and template matching algorithms, the sub-pixel matrix of the first captured image is traversed to extract the preset sub-pixel region identification features. Then, according to the sub-pixel arrangement rules defined in the target positioning map, the set that successfully matches the corresponding sub-pixel region identification features is defined as the spatial range of a single sub-pixel. This clarifies the boundary coordinates of each sub-pixel in the image, achieving accurate differentiation and locking of the red, green, and blue channel sub-pixel spatial regions, ensuring that the spatial regions of each sub-pixel do not overlap or omit any.

[0132] For the red, green, and blue channel separated images, the centroid calculation formula is applied to solve for the center coordinates of the corresponding color sub-pixel spatial regions identified in step 302. Specifically, the centroid calculation formula calculates the weighted centroid coordinates of each sub-pixel spatial region by statistically analyzing the coordinates and corresponding pixel values ​​of all pixels within that region. This calculation method fully considers the brightness distribution characteristics within the sub-pixel region, avoiding coordinate shifts caused by edge noise. Ultimately, pixel-level precision center coordinates are obtained for each sub-pixel in the three channel separated images, and these coordinates correspond one-to-one with the spatial position of the sub-pixel.

[0133] Reference Figure 4 In step 105, linear interpolation is performed on the corrected coordinates of the sub-pixels in the non-edge regions adjacent to the sub-pixels in the edge regions to obtain the corrected coordinates of the sub-pixels in the edge regions. This may specifically include, but is not limited to, the following:

[0134] 401. Based on the length and width of the target filter, identify the edge regions of the separated image. The edge regions include the upper edge, lower edge, left edge, and right edge.

[0135] 402. Obtain the corrected coordinates of the sub-pixels in the adjacent non-edge regions corresponding to the sub-pixels of the top edge, bottom edge, left edge, and right edge respectively;

[0136] 403. Based on the preset edge correction formula and the correction coordinates of the sub-pixels of adjacent non-edge regions, calculate the correction coordinates of the sub-pixels of the upper edge, lower edge, left edge, and right edge respectively.

[0137] In this embodiment, the length m and width n of the target filter are both odd numbers. Since the target filter needs to perform two-dimensional mean filtering centered on the sub-pixel to be filtered, when the sub-pixel is located at the edge of the separated image, the filter will exceed the image range, resulting in ineffective filtering. Therefore, the boundary region definition rule is as follows: the upper and lower edges are the (m-1) / 2 rows of sub-pixels at the top and bottom of the separated image, respectively; the left and right edges are the (n-1) / 2 columns of sub-pixels on the left and right sides of the separated image, respectively. This rule accurately divides the four edge regions (upper, lower, left, and right), ensuring that subsequent filtering is only performed directly on non-edge region sub-pixels, while edge region sub-pixels are corrected separately using interpolation.

[0138] For each sub-pixel at the top edge, the corrected coordinates of the first row of sub-pixels in the immediately below non-edge region are selected, i.e., the corrected coordinates of the sub-pixels in the (m-1) / 2+1th row, and the corrected coordinates of the second row of sub-pixels, i.e., the corrected coordinates of the sub-pixels in the (m-1) / 2+2th row. Similarly, for the bottom edge sub-pixels, the corrected coordinates of the last row and the second-to-last row of sub-pixels in the immediately above non-edge region are selected; for the left edge sub-pixels, the corrected coordinates of the first and second columns of sub-pixels in the immediately to the right non-edge region are selected; and for the right edge sub-pixels, the corrected coordinates of the last column and the second-to-last column of sub-pixels in the immediately to the left non-edge region are selected. This ensures that each edge sub-pixel can obtain two adjacent and reliable corrected coordinates of non-edge sub-pixels, providing the basic data for linear interpolation.

[0139] The default edge correction formula is a linear interpolation formula. Taking the top edge sub-pixel as an example, let its row number be i (i∈{1,...,(m-1) / 2}), and the row coordinate of the corresponding adjacent non-edge region sub-pixel after correction be R'. (m-1) / 2+1,j and R' (m-1) / 2+2,j The corrected column coordinate is C' m-1) / 2+1,j and C' (m-1) / 2+2,j Then the corrected coordinates of the upper edge sub-pixel are: R' i,j =R' (m-1) / 2+1,j-[(m-1) / 2+1-i]×(R' (m-1) / 2+2,j -R' (m-1) / 2+1,j ), C' i,j =C' (m-1) / 2+1,j -[(m-1) / 2+1-i]×(C' (m-1) / 2+2,j -C' (m-1) / 2+1,j The bottom, left, and right edge sub-pixels all follow this logic. Based on the coordinates of their corresponding adjacent non-edge sub-pixels, their corrected coordinates are derived using a linear interpolation formula, ensuring consistent sub-pixel positioning accuracy between edge and non-edge regions. (See reference...) Figure 9 and Figure 10 , Figure 9 This is a schematic diagram of the sub-pixels under the modified green channel in this application; Figure 10 This is a schematic diagram of the sub-pixels under the modified blue channel in this application.

[0140] The above embodiments illustrate the method for extracting sub-pixel brightness of a display screen provided in this application. The system, electronic device, and storage medium for extracting sub-pixel brightness of a display screen provided in this application are described below:

[0141] Please see Figure 5 This application provides an embodiment of a system for extracting the brightness of sub-pixels of a display screen, comprising:

[0142] The imaging unit 501 is used to acquire a first image of the target positioning map and a second image of the target grayscale map through an industrial camera. The target positioning map and the target grayscale map are set according to the physical characteristics of the display screen to be compensated.

[0143] Separation unit 502 is used to separate the color channels of the second captured image to obtain a separated image;

[0144] The center coordinate calculation unit 503 is used to calculate the center coordinates of each sub-pixel in the separated image based on the positioning information in the first captured image, and store the row coordinates and column coordinates of the center coordinates into the row coordinate image and column coordinate image respectively;

[0145] The first corrected coordinate unit 504 is used to perform two-dimensional mean filtering on the row coordinate image and column coordinate image respectively through a preset target filter for sub-pixels in non-edge regions, and to sort out and obtain the corrected coordinates of sub-pixels in non-edge regions.

[0146] The second correction coordinate unit 505 is used to perform linear interpolation on the correction coordinates of the sub-pixels in non-edge regions adjacent to the sub-pixels in the edge region for the sub-pixels in the edge region, so as to obtain the correction coordinates of the sub-pixels in the edge region.

[0147] Unit 506 for obtaining two-dimensional Gaussian function expression is used to obtain the two-dimensional Gaussian function expression corresponding to each sub-pixel based on the corrected coordinates of the sub-pixel and the sub-pixel block algorithm.

[0148] The precision brightness calculation unit 507 is used to calculate the precision brightness corresponding to each sub-pixel using a weighted estimation algorithm based on the two-dimensional Gaussian function expression and the pixel values ​​of all camera sampling points corresponding to each sub-pixel in the separated image.

[0149] Optionally, the unit 506 for obtaining the two-dimensional Gaussian function expression is specifically used for:

[0150] Based on the separated image, the pixels of the industrial camera are divided into multiple sub-pixel sub-blocks of the same size, and the center coordinates of each sub-pixel sub-block are calculated;

[0151] Based on the corrected coordinates of the sub-pixels, the sub-pixel sub-block to which the target sub-pixel belongs is determined. The target sub-pixel is the sub-pixel of the non-edge region and the edge region whose brightness is to be extracted.

[0152] The two-dimensional Gaussian function expression corresponding to the target sub-pixel is determined based on the center coordinates of the sub-pixel sub-block to which the target sub-pixel belongs.

[0153] Optionally, the expression for the two-dimensional Gaussian function is: ;

[0154] Where (x,y) are the coordinates to be input;

[0155] g(x,y) is a two-dimensional Gaussian function;

[0156] (u,v) represents the center coordinates of the sub-pixel sub-block to which the target sub-pixel belongs;

[0157] σ is the Gaussian kernel standard deviation.

[0158] Optionally, the target sub-pixel corresponds to D camera sampling points on the separated image, and before calculating the precise brightness unit 507, it also includes:

[0159] Formula acquisition unit 508 is used to obtain the brightness estimation calculation formula: e d =p d / [g(x d ,y d )];

[0160] Among them, e d The brightness of the target sub-pixel is estimated based on the d-th camera sampling point;

[0161] p d Let d be the pixel value of the d-th camera sampling point;

[0162] g(x d ,yd ) represents the d-th camera sampling point (x) corresponding to the target sub-pixel. d ,y d The value of the two-dimensional Gaussian function;

[0163] d is a non-zero integer, and the range of d is 1 to D;

[0164] The brightness estimation unit 509 is used to calculate the brightness of the target sub-pixel based on the estimated brightness of the d-th camera sampling point using the brightness estimation calculation formula.

[0165] Optionally, the preset brightness extraction model is a weighted average model, and the calculation formula for the weighted average model is as follows: ;

[0166] Where Lum is the brightness of the target sub-pixel;

[0167] e d The brightness of the target sub-pixel is calculated based on the d-th camera sampling point;

[0168] w d This represents the weight corresponding to the d-th camera sampling point.

[0169] Optionally, the separation unit 502 is specifically used for:

[0170] The second captured image is separated into red, green and blue channels by using a color space decomposition algorithm.

[0171] Based on the positioning information in the first captured image, the center coordinates of each sub-pixel in the separated image are calculated as follows:

[0172] The positioning information includes sub-pixel region identification features, which are used to identify sub-pixel region identification features in the first captured image and confirm the spatial region of each sub-pixel.

[0173] Based on the centroid calculation formula and spatial region, calculate the center coordinates of each sub-pixel in the red channel separated image, green channel separated image, and blue channel separated image, respectively.

[0174] Optionally, the target filter is a rectangular filter with both an odd length and a wide width;

[0175] When the sub-pixel arrangement of the red channel separated image and the blue channel separated image is in the form of a diamond grid, the non-zero coefficients of the target filter corresponding to the red channel separated image and the non-zero coefficients of the target filter corresponding to the blue channel separated image are both centrally symmetrically distributed in a diamond grid.

[0176] When the sub-pixel arrangement of the green channel separated image is in the form of a rectangular grid, the non-zero coefficients of the target filter corresponding to the green channel separated image are distributed in a centrally symmetrical square grid.

[0177] Optionally, the second corrected coordinate unit 505 is specifically used for:

[0178] Based on the length and width of the target filter, identify the edge regions of the separated image. The edge regions include the top edge, bottom edge, left edge, and right edge.

[0179] Obtain the corrected coordinates of the sub-pixels in the adjacent non-edge regions corresponding to the sub-pixels of the top edge, bottom edge, left edge, and right edge respectively;

[0180] Based on the preset edge correction formula and the correction coordinates of the sub-pixels in adjacent non-edge regions, the correction coordinates of the sub-pixels of the top edge, bottom edge, left edge, and right edge are calculated respectively.

[0181] Please see Figure 6 This application provides an electronic device, including:

[0182] Processor 601, memory 602, input / output unit 603, and bus 604.

[0183] The processor 601 is connected to the memory 602, the input / output unit 603, and the bus 604.

[0184] The memory 602 stores a program, and the processor 601 calls the program to execute it, such as... Figure 1 , Figure 2 , Figure 3 or Figure 4 A method for extracting the brightness of sub-pixels of a display screen.

[0185] This application provides a computer-readable storage medium on which a program is stored, and when the program is executed on a computer, it performs the following... Figure 1 , Figure 2 , Figure 3 or Figure 4 A method for extracting the brightness of sub-pixels of a display screen.

[0186] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0187] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0188] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0189] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0190] If the integrated unit is implemented as 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 this application, in essence, 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. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method for extracting the brightness of sub-pixels of a display screen, characterized in that, include: An industrial camera is used to acquire a first image of the target positioning map and a second image of the target grayscale map, which are obtained according to the physical characteristics of the display screen to be compensated. Perform color channel separation on the second captured image to obtain a separated image; Based on the positioning information in the first captured image, the center coordinates of each sub-pixel in the separated image are calculated, and the row coordinates and column coordinates of the center coordinates are stored in the row coordinate image and column coordinate image respectively. For sub-pixels in non-edge regions, a preset target filter is used to perform two-dimensional mean filtering on the row coordinate image and the column coordinate image respectively, and the corrected coordinates of the sub-pixels in non-edge regions are obtained. For sub-pixels in the edge region, linear interpolation is performed on the corrected coordinates of sub-pixels in non-edge regions adjacent to the sub-pixels in the edge region to obtain the corrected coordinates of the sub-pixels in the edge region. The two-dimensional Gaussian function expression corresponding to each sub-pixel is obtained based on the corrected coordinates of the sub-pixel and the sub-pixel block algorithm; Based on the two-dimensional Gaussian function expression and the pixel values ​​of all camera sampling points corresponding to each sub-pixel in the separated image, a weighted estimation algorithm is used to calculate the precise brightness corresponding to each sub-pixel.

2. The method according to claim 1, characterized in that, The step of obtaining the two-dimensional Gaussian function expression corresponding to each sub-pixel based on the corrected coordinates of the sub-pixel and the sub-pixel block algorithm includes: Based on the separated image, the pixels of the industrial camera are divided into multiple sub-pixel sub-blocks of the same size, and the center coordinates of each sub-pixel sub-block are calculated; Based on the corrected coordinates of the sub-pixel, the sub-pixel sub-block to which the target sub-pixel belongs is determined, wherein the target sub-pixel is the sub-pixel of the non-edge region and the edge region whose brightness is to be extracted; Based on the center coordinates of the sub-pixel sub-block to which the target sub-pixel belongs, determine the two-dimensional Gaussian function expression corresponding to the target sub-pixel.

3. The method according to claim 2, characterized in that, The expression for the two-dimensional Gaussian function is: ; Wherein, (x, y) are the coordinates to be input; The g(x,y) is a two-dimensional Gaussian function; (u,v) represents the center coordinates of the sub-pixel sub-block to which the target sub-pixel belongs; σ represents the Gaussian kernel standard deviation.

4. The method according to claim 3, characterized in that, The target sub-pixel corresponds to D camera sampling points on the separated image. Before calculating the precise brightness corresponding to each sub-pixel using a weighted estimation algorithm, the method further includes: The formula for obtaining the brightness estimate is: e d =p d / [g(x d ,y d )]; Wherein, the e d The brightness of the target sub-pixel is estimated based on the d-th camera sampling point; The p d Let d be the pixel value of the d-th camera sampling point; The g(x) d ,y d ) is the d-th camera sampling point (x) corresponding to the target sub-pixel. d ,y d The value of the two-dimensional Gaussian function; The d is a non-zero integer, and the value of the d ranges from 1 to D; The brightness of the target sub-pixel is calculated based on the brightness estimation formula, using the formula.

5. The method according to claim 4, characterized in that, The calculation formula for the weighted estimation algorithm is as follows: ; Wherein, Lum is the precise brightness of the target sub-pixel; The e d The brightness of the target sub-pixel is estimated based on the d-th camera sampling point; The w d This represents the weight corresponding to the d-th camera sampling point.

6. The method according to claim 1, characterized in that, The step of performing color channel separation on the second captured image to obtain a separated image includes: The second captured image is subjected to color channel separation using a color space decomposition algorithm to obtain a red channel separated image, a green channel separated image, and a blue channel separated image. The step of calculating the center coordinates of each sub-pixel in the separated image based on the positioning information in the first captured image includes: The positioning information includes sub-pixel region identification features, which are used to identify sub-pixel region identification features in the first captured image and confirm the spatial region of each sub-pixel. Based on the centroid calculation formula and the spatial region, the center coordinates of each sub-pixel of the red channel separated image, the green channel separated image, and the blue channel separated image are calculated respectively.

7. The method according to claim 6, characterized in that, The target filter is a rectangular filter with both its length and width being odd numbers; When the sub-pixel arrangement of the red channel separated image and the blue channel separated image is in the form of a diamond grid, the non-zero coefficients of the target filter corresponding to the red channel separated image and the non-zero coefficients of the target filter corresponding to the blue channel separated image are both centrally symmetrically distributed in a diamond grid. When the sub-pixel arrangement of the green channel separated image is in the form of a rectangular grid, the non-zero coefficients of the target filter corresponding to the green channel separated image are distributed in a centrally symmetrical square grid.

8. The method according to claim 1, characterized in that, The step of linearly interpolating the corrected coordinates of the sub-pixels in the non-edge region adjacent to the sub-pixels of the edge region to obtain the corrected coordinates of the sub-pixels in the edge region includes: Based on the length and width of the target filter, the edge regions of the separated image are identified, including the upper edge, lower edge, left edge, and right edge; The corrected coordinates of the sub-pixels in the adjacent non-edge regions corresponding to the sub-pixels of the upper edge, the lower edge, the left edge, and the right edge are obtained respectively. Based on the preset edge correction formula and the correction coordinates of the sub-pixels of the adjacent non-edge regions, the correction coordinates of the sub-pixels of the upper edge, the lower edge, the left edge, and the right edge are calculated respectively.

9. A system for extracting the brightness of sub-pixels of a display screen, characterized in that, include: The shooting unit is used to acquire a first image of a target positioning map and a second image of a target grayscale map through an industrial camera. The target positioning map and the target grayscale map are set according to the physical characteristics of the display screen to be compensated. A separation unit is used to perform color channel separation on the second captured image to obtain a separated image; The center coordinate calculation unit is used to calculate the center coordinates of each sub-pixel in the separated image based on the positioning information in the first captured image, and store the row coordinates and column coordinates of the center coordinates into the row coordinate image and column coordinate image respectively; The first corrected coordinate unit is used to perform two-dimensional mean filtering on the row coordinate image and the column coordinate image respectively through a preset target filter for sub-pixels in non-edge regions, and sort them to obtain the corrected coordinates of the sub-pixels in non-edge regions. The second correction coordinate unit is used to perform linear interpolation on the correction coordinates of the sub-pixels in non-edge regions adjacent to the sub-pixels in the edge region for the sub-pixels in the edge region, so as to obtain the correction coordinates of the sub-pixels in the edge region. The unit for obtaining the two-dimensional Gaussian function expression is used to obtain the two-dimensional Gaussian function expression corresponding to each sub-pixel based on the corrected coordinates of the sub-pixel and the sub-pixel block algorithm. The accurate brightness calculation unit is used to calculate the accurate brightness corresponding to each sub-pixel using a weighted estimation algorithm based on the two-dimensional Gaussian function expression and the pixel values ​​of all camera sampling points corresponding to each sub-pixel in the separated image.

10. An electronic device, characterized in that, include: Processor, memory, input / output units, and bus; The processor is connected to the memory, the input / output unit, and the bus; The memory stores a program that the processor invokes to perform the method for extracting the brightness of a display subpixel as described in any one of claims 1 to 8.

Images