Method and system for optimizing positioning map for gamma correction compensation

Abstract

The invention discloses a method for optimizing a positioning map for gamma correction compensation, and the method comprises the steps: obtaining a compensation coefficient image based on a gray-scale image displayed by a display panel, and carrying out the compensation of the positioning map based on the compensation coefficient image. According to the invention, the gray value of the positioning point in the bright area after the original imaging in the optimized positioning image can be reduced, and the gray value of the dark area can be increased, so that the imaging brightness and chrominance of the optimized positioning image are more uniform, and the subsequent image quality correction algorithm processing is facilitated.

Description

Technical Field

[0001] The present invention relates to the technical field of image processing, and particularly to an optimization method and system for a positioning map for gamma correction compensation. Background Art

[0002] Due to its characteristics such as being thin and light and having a high transmittance to external light, an optical waveguide can meet the imaging requirements of an Augmented Reality (AR) glasses in terms of technical principles. However, affected by the structure, material properties of the optical waveguide or external factors, when the optical waveguide transmits optical signals, the terminal display screen will exhibit uneven brightness and chromaticity. This uneven brightness and chromaticity phenomenon will affect the performance of AR display. Especially in display scenarios that require high brightness and color uniformity, it is necessary to eliminate this uneven brightness and chromaticity phenomenon through optical imaging technology and software algorithms, and this process is called gamma correction compensation Demura.

[0003] In the actual calculation process, in order to improve the calculation accuracy, it is necessary to accurately obtain the brightness information of each sub-pixel. Therefore, it is usually necessary to obtain a positioning map with the help of an external device to determine the exact coordinates of each sub-pixel on the display panel. However, the optical waveguide often has a high degree of uneven brightness and chromaticity. Therefore, when obtaining the positioning map, the external device usually requires a large dynamic range, and the algorithm processing for the positioning screen is quite difficult. Summary of the Invention

[0004] In view of some or all of the problems in the prior art, the first aspect of the present invention provides an optimization method for a positioning map for gamma correction compensation, including:

[0005] Obtaining a compensation coefficient image img6 based on the grayscale image img3 displayed on the display panel; and

[0006] Compensating the positioning map img7 based on the compensation coefficient image img6.

[0007] Further, obtaining a compensation coefficient image img6 based on the grayscale image img3 displayed on the display panel includes:

[0008] Performing an inverse gamma processing on the grayscale image img3 to obtain an inverse gamma image img5;

[0009] Calculating the grayscale mean value of the inverse gamma image img5; and

[0010] Determining the compensation coefficient image img6 based on the grayscale mean value.

[0011] Further, performing an inverse gamma processing on the grayscale image img3 includes:

[0012] The grayscale value x of each pixel of the grayscale image img3 is calculated according to the following formula to obtain the grayscale value y of the corresponding pixel in the inverse gamma image img5:

[0013] y = x 1 / γ ,

[0014] where γ is the gamma coefficient of the display panel.

[0015] Further, based on the grayscale mean value, a compensation coefficient image img6 is determined, including:

[0016] The grayscale value y of each pixel of the inverse gamma image img5 is calculated according to the following formula to obtain the compensation coefficient z of the corresponding pixel, forming the compensation coefficient image img6:

[0017] z = mean / y,

[0018] where mean refers to the grayscale mean value of the inverse gamma image img5.

[0019] Further, obtaining the compensation coefficient image img6 based on the grayscale image img3 displayed on the display panel further includes:

[0020] Before performing the inverse gamma processing, the high-frequency noise in the grayscale image img3 is removed.

[0021] Further, removing the high-frequency noise in the grayscale image img3 includes:

[0022] Performing low-pass filtering on the grayscale image img3.

[0023] Further, the optimization method further includes:

[0024] Obtaining a picture img1 of the display panel that is displaying a grayscale screen through an imaging device, and extracting the grayscale image img3 from the picture img1.

[0025] Further, extracting the grayscale image img3 from the picture img1 includes:

[0026] Extracting the grayscale screen displayed on the display panel from the picture img1 to obtain an extracted image img2; and

[0027] Adjusting the resolution of the extracted image img2 to be consistent with the resolution of the display panel to obtain the grayscale image img3.

[0028] Further, extracting the grayscale screen img2 displayed on the display panel from the picture img1 includes:

[0029] Performing image segmentation on the picture img1 to obtain at least one connected region;

[0030] Obtain the area of each connected region through a contour analysis algorithm, and use the connected region with the largest area as the display area contour; and

[0031] Fit the display area contour to obtain an extracted image img2 that is consistent with the shape of the display area of the display panel.

[0032] Further, perform image segmentation on the picture img1, including:

[0033] Perform adaptive local grayscale threshold segmentation on the picture img1 to generate a binary image;

[0034] Connect the disconnected regions in the binary image through morphological closing operations to generate at least one connected region.

[0035] Further, the shape of the display area of the display panel is square, rectangular, polygonal, circular or irregular.

[0036] Further, use the point set polygon algorithm to fit the display contour area.

[0037] Further, extracting the grayscale picture img2 displayed on the display panel from the picture img1 further includes:

[0038] Before performing image segmentation, remove the image noise of the picture img1.

[0039] Further, remove the image noise of the picture img1 through a median filtering algorithm.

[0040] Further, adjust the resolution of the extracted grayscale picture, including:

[0041] Adjust the resolution of the extracted grayscale picture through an image interpolation algorithm.

[0042] Further, based on the compensation coefficient image img6, compensate the positioning map, including:

[0043] Multiply the grayscale value of each pixel point of the positioning map img7 by the corresponding compensation coefficient to obtain an optimized positioning map img8.

[0044] Further, the positioning map img7 includes multiple positioning points, and the grayscale values of each positioning point are the same.

[0045] Further, the compensation coefficient makes:

[0046] The grayscale value of the positioning points in the first region of the positioning map img7 is reduced; and

[0047] The gray value of the positioning point within the second region of the positioning map img7 increases, where the imaging brightness within the first region is greater than that within the second region.

[0048] Based on the optimization method as described above, the second aspect of the present invention provides an optimization system for a positioning map for gamma correction compensation, including:

[0049] A compensation coefficient calculation module, which is used to calculate a compensation coefficient based on a grayscale image; and

[0050] A compensation module, which is used to compensate the positioning map based on the compensation coefficient.

[0051] Further, the compensation coefficient calculation module is used to:

[0052] Perform inverse gamma processing on the grayscale image to obtain an inverse gamma image; and

[0053] Determine the compensation coefficient according to the grayscale mean value of the inverse gamma image.

[0054] Further, the optimization system further includes:

[0055] A first filtering module, which is communicatively connected to the compensation coefficient calculation module and is used to perform low-pass filtering on the image to remove high-frequency noise in the image.

[0056] Further, the optimization system further includes:

[0057] An imaging device, which is used to acquire a picture of a display panel that is displaying a grayscale screen; and

[0058] An image extraction module, which is communicatively connected to the imaging device and is used to extract the grayscale screen displayed on the display panel from the picture acquired by the imaging device.

[0059] Further, the optimization system further includes:

[0060] A resolution adjustment module, which is communicatively connected to the image extraction module and is used to adjust the resolution of the image extracted by the image extraction module.

[0061] Further, the image extraction module includes:

[0062] An image segmentation sub-module, which is communicatively connected to the imaging device and is used to segment the picture acquired from the imaging device to obtain at least one connected region; and

[0063] A shape fitting sub-module, which is communicatively connected to the image segmentation sub-module and is used to perform shape fitting on the connected region, and further adjust the shape of the connected region with the largest area to be consistent with the display area of the display panel.

[0064] Further, the optimization system further includes:

[0065] A second filtering module, communicatively connected to the image extraction module, for performing median filtering on the image.

[0066] A third aspect of the present invention provides a gamma correction compensation method, including:

[0067] Adopting the optimization method as before to compensate and optimize the positioning map;

[0068] Displaying the optimized positioning map through a display panel; and

[0069] Based on the optimized positioning map, performing gamma correction on the display panel.

[0070] A fourth aspect of the present invention provides a gamma correction compensation system, including:

[0071] The optimization system as before, for generating an optimized positioning map; and

[0072] A gamma correction module, communicatively connected to the optimization system, and for performing gamma correction on the display panel based on the optimized positioning map.

[0073] An optimization method and system for a positioning map for gamma correction compensation provided by the present invention generate a compensation coefficient image according to a grayscale image, and perform compensation processing on the positioning map according to the compensation coefficient image, so that the gray values of the positioning points in the originally brighter regions after imaging in the positioning map are reduced, while the gray values of the darker regions are increased, thereby making the imaging brightness and chroma of the optimized positioning map more uniform and facilitating subsequent image quality correction algorithms. This method can effectively improve the robustness of the pixel point positioning algorithm for the pupil image of the optical waveguide module, improve the brightness measurement accuracy of the image quality correction device, and has low implementation cost and convenient operation. At the same time, since the non-uniformity of the optical waveguides in the same batch is usually similar, the positioning map corrected according to the present invention can be applied to the correction of the optical waveguide modules in the same batch, thus greatly improving the correction efficiency of the optical waveguides. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] To further clarify the advantages and features of the embodiments of the present invention, more specific descriptions of the embodiments of the present invention will be presented with reference to the accompanying drawings. It can be understood that these drawings only depict typical embodiments of the present invention and will not be considered as limiting its scope. In the drawings, for clarity, the same or corresponding components will be denoted by the same or similar reference numerals.

[0075] Figure 1 A schematic diagram of a positioning map showing an embodiment of the present invention;

[0076] Figure 2The flowchart shows an optimization method for a positioning map for gamma correction compensation according to an embodiment of the present invention;

[0077] Figure 3 The schematic diagram shows an optimized positioning map according to an embodiment of the present invention;

[0078] Figure 4 The flowchart shows a process for obtaining a compensation coefficient image according to an embodiment of the present invention;

[0079] Figure 5 The flowchart shows a process for obtaining a grayscale image according to an embodiment of the present invention;

[0080] Figure 6 The flowchart shows a process for extracting a grayscale frame according to an embodiment of the present invention;

[0081] Figure 7 The flowchart shows a gamma correction compensation method according to an embodiment of the present invention;

[0082] Figure 8 The schematic diagram shows a structure of an optimization system for a positioning map for gamma correction compensation according to an embodiment of the present invention; and

[0083] Figure 9 The schematic diagram shows a structure of a gamma correction compensation system according to an embodiment of the present invention. Detailed implementation manners

[0084] It should be noted that the components in the respective drawings may be exaggerated for illustration purposes and not necessarily to scale. In the respective drawings, the same or functionally identical components are provided with the same reference numerals.

[0085] In the present invention, each embodiment is merely intended to illustrate the solution of the present invention and should not be construed as restrictive.

[0086] In the present invention, unless otherwise specified, the quantifiers "a" and "one" do not exclude the scenario of multiple elements.

[0087] It should also be noted here that in the embodiments of the present invention, for clarity and simplicity, only a part of the components or assemblies may be shown. However, those of ordinary skill in the art can understand that, under the teaching of the present invention, the required components or assemblies can be added according to the specific scenario requirements. Additionally, unless otherwise stated, the features in different embodiments of the present invention can be combined with each other. For example, a certain feature in the second embodiment can be used to replace the corresponding or functionally identical or similar feature in the first embodiment, and the resulting embodiment also falls within the scope of disclosure or the scope of record of this application.

[0088] It should also be noted here that within the scope of the present invention, terms such as "identical", "equal", "equivalent" do not mean that the two values are absolutely equal, but allow for a certain reasonable error, that is to say, these terms also cover "substantially identical", "substantially equal", "substantially equivalent". By analogy, in the present invention, terms indicating direction such as "perpendicular to", "parallel to", etc. also cover the meanings of "substantially perpendicular to", "substantially parallel to".

[0089] In the present application, the term "configured" means setting the shape, structure, material, and / or function of an object to achieve the desired technical effect, where "configured" includes various alternative technical means for achieving this technical effect, and these technical means become obvious under the teachings of the present application.

[0090] In addition, the numbering of the steps of each method of the present invention does not limit the execution order of the method steps. Unless otherwise specified, the method steps can be executed in different orders.

[0091] When performing Demura processing, after accurately determining the sub-pixel positions through the alignment pattern, the compensation data required for each pixel can be calculated more precisely based on the captured image, and then the brightness, color, etc. of the pixels can be accurately compensated, improving the image quality of the display panel, making the display effect more uniform and consistent, and avoiding problems such as uneven brightness and color deviation. The alignment pattern has a specific gray-scale distribution and pattern rule. For example, along the curved surface area from the edge to the center, the display gray-scales corresponding to multiple columns of sub-pixels change according to a set gradient pattern. Another example is that the target display gray-scales of the preset lit sub-pixels in the planar area are equal. Figure 1 shows a schematic diagram of the alignment pattern of an embodiment of the present invention. As Figure 1As shown, the positioning map includes a number of positioning points. The spacing between each positioning point can be set as needed, and the gray value of each positioning point is the same. When determining the sub-pixel position, first control the display panel to display the positioning map, and then use an imaging device such as a high-resolution industrial camera to capture the display panel with the positioning map displayed. After the imaging device captures an image, analyze the characteristics such as the boundaries and positions of different gray-scale regions in the image. According to the gray-scale change rule of the positioning map and the known sub-pixel arrangement rule, the position of each sub-pixel in the image can be determined. However, due to the non-uniformity of light waveguide propagation, the light intensity differences of each positioning point in the image of the light waveguide exit pupil are very large, which further causes changes in the spectral components of the output light, thus affecting the imaging chromaticity and making it inconvenient for subsequent Demura processing. To address this problem, the present invention provides an optimization method for a positioning map for gamma correction compensation, which adjusts the gray values of different regions of the positioning map to make the imaging brightness and chromaticity of the optimized positioning map more uniform, thereby effectively improving the robustness of the positioning algorithm for the light waveguide module exit pupil image and enhancing the measurement accuracy of the brightness and chromaticity of the Demura device.

[0092] The technical solution of the present invention will be further described below with reference to the accompanying drawings of the embodiments.

[0093] Figure 2 The flowchart of an optimization method for a positioning map for gamma correction compensation according to an embodiment of the present invention is shown. As Figure 2 shown, an optimization method for a positioning map for gamma correction compensation includes:

[0094] First, in step 201, obtain a compensation coefficient image. Based on the gray-scale image img3 displayed on the display panel, obtain a compensation coefficient image img6, where the pixel points of the compensation coefficient image img6 correspond one-to-one with the pixel points of the gray-scale image img3, and the gray value of the pixel points of the compensation coefficient image img6 is the compensation coefficient of the corresponding pixel points of the gray-scale image img3; and

[0095] Finally, in step 202, optimize the positioning map. Based on the compensation coefficient image img6, compensate the positioning map. In some embodiments, multiply the gray value of each pixel point of the positioning map img7 by the corresponding compensation coefficient to obtain the optimized positioning map img8. Since the compensation coefficient is determined according to the performance of the display panel itself, that is, the brightness difference of different pixel points during its actual display, it can reduce the gray value of the positioning points in the originally brighter regions after imaging, and increase the gray value of the darker regions, thereby reducing the brightness difference of each positioning point of the optimized positioning map img8 and making the imaging brightness more uniform, which is convenient for subsequent algorithm processing. Figure 3 The schematic diagram of the optimized positioning map according to an embodiment of the present invention is shown. As Figure 3As shown, in some embodiments, compared with the positioning map img7, the gray scale values of the positioning points in the first region of the optimized positioning map img8 decrease, and the gray scale values of the positioning points in the second region increase, where the imaging brightness in the first region is greater than that in the second region. Thus, when driving the display panel to display the optimized positioning map img8, the brightness of each positioning point tends to be consistent.

[0096] Figure 4 The flowchart shows the process of obtaining a compensation coefficient image according to an embodiment of the present invention.

[0097] As Figure 4 shown, in some embodiments, obtaining a compensation coefficient image includes:

[0098] First, in step 211, perform inverse gamma processing. Perform inverse gamma processing on the grayscale image img3 to obtain an inverse gamma image img5. In some embodiments, for each pixel point of the grayscale image img3 with a gray scale value x, the following formula is used to calculate the gray scale value y of the corresponding pixel point in the inverse gamma image img5:

[0099] y = x 1 / γ ,

[0100] where γ is the gamma coefficient of the display panel;

[0101] Next, in step 212, calculate the gray scale mean value. Calculate the gray scale mean value mean of the inverse gamma image img5. In some embodiments, the gray scale mean value of the inverse gamma image img5 is equal to the sum of the gray scale values of all pixel points of the inverse gamma image img5 divided by the total number of pixel points; and

[0102] Finally, in step 213, calculate the compensation coefficient. Based on the gray scale mean value, determine the compensation coefficient image img6. In some embodiments, for each pixel point of the inverse gamma image img5 with a gray scale value y, the following formula is used to calculate the corresponding compensation coefficient z to form the compensation coefficient image img6:

[0103] z = mean / y;

[0104] In some embodiments, before performing the inverse gamma processing, step 210 needs to be performed to remove high-frequency noise. In some embodiments, for example, the high-frequency noise in the grayscale image img3 can be removed by a low-pass filtering method. Specifically, first, the grayscale image img3 is transformed into the frequency domain using the discrete Fourier transform (DFT) or the fast Fourier transform (FFT), that is, the pixel values in the grayscale image img3 are represented as a combination of sine and cosine functions of different frequencies. Then, each point in the obtained frequency-domain image corresponds to a specific frequency component, whose amplitude represents the intensity of the frequency component, and the phase represents the relative position of the frequency component. Subsequently, a suitable low-pass filter is selected as needed, and the frequency-domain representation of the grayscale image img3 is multiplied point by point with the transfer function of the low-pass filter, which is equivalent to weighting each frequency component of the image in the frequency domain. The high-frequency components are attenuated due to the effect of the filter, while the low-frequency components remain basically unchanged. The low-pass filter can be, for example, an ideal low-pass filter, a Butterworth low-pass filter, and a Gaussian low-pass filter, etc. Finally, the filtered frequency-domain image is transformed back to the spatial domain using the inverse Fourier transform (IDFT) or the inverse fast Fourier transform (IFFT) to obtain the denoised image.

[0105] In some embodiments, the grayscale image img3 displayed by the display panel needs to be obtained by an external imaging device and further extracted by a device such as a computer. Figure 5 The flowchart of obtaining the grayscale image according to an embodiment of the present invention is shown. As Figure 5 shown, in some embodiments, obtaining the grayscale image includes:

[0106] First, in step 501, a grayscale screen is displayed. The display panel is driven to display a grayscale screen. For a monochromatic display panel, the grayscale screen refers to a screen in which the grayscale values of all pixel points are the same. In some embodiments, the grayscale values of all pixel points can be 64 or 96 or 128 or 192, etc.

[0107] Next, in step 502, a picture is taken. A picture img1 of the display panel displaying the grayscale screen is taken by an imaging device. In some embodiments, the imaging device can be, for example, an industrial camera, an imaging luminance meter, an imaging colorimeter, etc., which is mainly used to take pictures of the display panel to generate two-dimensional image data.

[0108] Next, in step 503, the grayscale image is extracted. In the picture taken by the imaging device, in addition to the display area of the display panel, it may also include other things, such as the non-display area of the display panel, and the surrounding environment where the display panel is located, etc. When calculating the compensation coefficient, the effective data is only the grayscale image displayed by the display panel. Based on this, it is necessary to extract the grayscale image displayed by the display panel from the picture img1 to obtain the extracted image img2 for subsequent operations. Figure 6 shows a schematic flowchart of extracting the grayscale image according to an embodiment of the present invention. As Figure 6 shown, in some embodiments, extracting the grayscale image includes:

[0109] First, in step 531, image segmentation is performed. Since the brightness of the grayscale image displayed on the display panel is higher than that of the non-display area, in the picture img1 captured by the imaging device, the black area can be regarded as an invalid area, and only the bright areas need to be analyzed. However, during the actual shooting process, affected by stray light and / or noise, in addition to the higher brightness in the display area in the picture img1, there may also be some smaller areas with higher brightness. Based on this, in order to correctly extract the grayscale image from the picture img1, it is necessary to first perform image segmentation on the picture img1. Through image segmentation, each area with higher brightness is segmented out to obtain at least one connected area, where each connected area is an area with higher brightness. In some embodiments, the picture img1 is segmented by an adaptive local gray-level threshold segmentation algorithm to generate a binary image, and then the disconnected areas in the binary image are connected by a morphological closing operation to generate at least one connected area. The adaptive local gray-level threshold segmentation algorithm automatically determines the threshold according to the gray-level characteristics of the local area of the image, so as to separate the target and the background in the image. Specifically, it first needs to select a suitable local window size. The window size determines the range of the local area considered by the algorithm, and is generally determined according to the characteristics of the image and actual requirements. A smaller window can capture the details in the image, but may be sensitive to noise; a larger window can smooth the noise, but may lose some detail information. Common window sizes are odd sizes such as 3×3, 5×5, 7×7, etc., to ensure that the window has a central pixel. Then, for each pixel point in the image, with this pixel point as the center, the gray-level statistics are calculated within the local window around it. Commonly used statistics include mean, median, variance, etc. For example, when calculating the mean, the gray-level values of all pixels in the window are added up and then divided by the total number of pixels in the window to obtain the gray-level mean of this local area. When calculating the variance, it is necessary to first calculate the square of the difference between each pixel and the mean, and then find the average of these squared values to obtain the gray-level variance of this local area. Then, according to the calculated local gray-level statistics, an adaptive threshold for each pixel point is determined through a certain rule. For example, the mean can be multiplied by a constant as the threshold, and this constant is an empirical constant, usually between 0 and 1, and is used to adjust the size of the threshold. In addition, other statistics such as variance can also be combined to determine the threshold to more flexibly adjust the threshold according to the gray-level change situation of the local area. Then, the gray-level value of each pixel point is compared with the corresponding adaptive threshold. If the gray-level value is greater than or equal to the adaptive threshold, the pixel point is determined to be a target pixel, usually assigned a value of 1, representing white, otherwise the pixel point is determined to be a background pixel, assigned a value of 0, representing black. Through such a comparison operation, each pixel point in the image can be classified as a target or a background, obtaining a binary image and realizing the segmentation of the image.After obtaining the binary image, the binary image can be dilated first and then eroded to connect the disconnected regions. Specifically, first, a suitable structuring element needs to be selected. The structuring element is the basic shape for morphological operations, and common ones include rectangles, circles, crosses, etc. The size and shape of the structuring element will affect the effect of the closing operation, and generally, it is selected according to the size and shape of the disconnected regions to be connected in the image. For example, if the gap of the disconnected region is small, a smaller structuring element can be selected; if the gap is large, a larger structuring element is required. The binary image is dilated with the selected structuring element. The basic principle of the dilation operation is that for each pixel in the image, if there is a pixel with a value of 1 in a certain neighborhood around it, then the value of this pixel is set to 1. The specific process is to align the center of the structuring element with each pixel in the image in turn. If there is a pixel with a value of 1 in the area covered by the structuring element, the value of the current center pixel is updated to 1. Through the dilation operation, the target region will expand outward, so that some originally disconnected regions may start to contact each other. After the dilation operation is completed, the dilated image is eroded. The erosion operation is the opposite of the dilation operation. For each pixel in the image, if all pixels in a certain neighborhood around it have a value of 1, then the value of this pixel is set to 1. Similarly, the center of the structuring element is aligned with each pixel in the image in turn. Only when all pixels in the area covered by the structuring element are 1, the value of the current center pixel is retained as 1, otherwise it is set to 0. The erosion operation can remove some noise and burrs on the edge of the dilated region, make the boundary of the connected region smoother, and at the same time restore the original shape of the target region to a certain extent, finally achieving the effective connection of the disconnected regions;

[0110] Next, in step 532, calculate the area of the connected regions. Usually, the display region, that is, the region with higher brightness corresponding to the grayscale image, has the largest area. Therefore, it is necessary to calculate the areas of the respective connected regions to find the one with the largest area, which is the contour of the display region of the display panel, or the grayscale image displayed by the display panel. In some embodiments, the areas of the respective connected regions are obtained through a contour analysis algorithm. The contour analysis algorithm can, for example, be based on pixel statistics or geometric calculations to obtain the area of the connected region. Among them, based on pixel statistics means using a connected region labeling algorithm, such as a four-connected or eight-connected algorithm, to label the connected regions in the binary image. Taking the four-connected as an example, it means that the four adjacent pixels above, below, left, and right of a pixel are its connected neighborhoods. Traverse each pixel in the image. For a pixel with a value of 1, if its adjacent labeled pixels belong to the same connected region, then label the current pixel with the same region number; if the adjacent pixels have not been labeled, assign a new region number to the current pixel, so as to achieve the labeling of all connected regions. Traverse the labeled image. For each connected region, count the number of pixel points it contains. The number of pixels in each connected region can be obtained by traversing all the pixels in the image through a loop. When a pixel belonging to a specific connected region is encountered, increment the pixel counter of that region by 1. Since each pixel corresponds to a certain physical area in the image, usually assuming that the area of each pixel is 1 unit, then the number of pixels in the connected region is directly equivalent to its area in the image. If the actual physical area needs to be obtained, conversion is also required according to information such as the resolution of the image. The method based on geometric calculation is to use a contour extraction algorithm, such as the Canny edge detection algorithm combined with a contour tracking algorithm, to extract the contour of the connected region in the binary image. Specifically, it first uses the Canny algorithm to detect the edges in the image to obtain an edge image, and then selects a starting point from the edge image and follows the edge in a certain direction, such as clockwise or counterclockwise, until it returns to the starting point, thus obtaining a complete contour. Then the extracted contour is represented in ways such as the chain code representation method and the polygon approximation method. The chain code representation method uses a series of direction codes to represent the connection directions of the points on the contour. For example, 0 to 7 are used to represent eight different directions; the polygon approximation method uses a polygon to approximately represent the contour by finding some key points on the contour and connecting these key points into a polygon. Finally, calculate the area of the connected region according to the representation method of the contour. For example, if the chain code representation method is used, the area can be calculated through Green's formula. If the polygon approximation method is used, the polygon can be divided into multiple triangles, and then the area of each triangle is calculated and summed to obtain the area of the connected region; and

[0111] Finally, in step 533, shape fitting is performed. Shape fitting is carried out on the connected region with the largest area to obtain an extracted image img2 that is consistent with the shape of the display area of the display panel. In some embodiments, the shape of the display area of the display panel is not limited and can be square, rectangular, polygonal, circular, or irregular. For different-shaped display areas, different fitting methods are adopted. For example, if the shape of the display area of the display panel is a polygon such as square or rectangular, the point set polygon algorithm can be used to achieve shape fitting. The shape fitting using the point set polygon algorithm includes steps such as determining the initial point, constructing the polygon, and optimizing the fitting. Specifically, the gray values of each pixel point in the largest connected region are used as point set data, and a point is selected from them as the starting point. For example, a point with the smallest or largest coordinates, or a point with a relatively uniform distance distribution from other points, can be selected as the starting point. Calculate the distance and angle between the starting point and other points, and select the point with the closest distance or the most reasonable angle change as the end point of the first side. Connect the starting point and the end point to form the first side. From the remaining point set, select the next point to expand the polygon according to certain rules. Select the point that is closest to the end point of the current side and forms an angle within a certain range with the current side as the next vertex. Take the newly selected point as the new vertex and connect it to the previous vertex to form a new side, and continuously repeat this process to gradually construct the polygon. During the construction process, the coordinates of each vertex and the connection relationship of the sides need to be recorded. When all points are traversed or a certain termination condition is reached, such as the number of sides of the polygon reaches a preset value, or the distance between the newly added point and the starting point is less than a certain threshold, etc., connect the last vertex to the starting point to form a closed polygon; and

[0112] Finally, in step 504, the resolution is adjusted. Since there may be differences in the resolution, bit depth, etc. between the imaging device and the display panel, and the resolution of img2 is related to the imaging device, in order to facilitate subsequent driving to light up the display panel to display the optimized positioning map, after extracting img2, it is also necessary to adjust its resolution to obtain a grayscale image img3 with the same resolution as the display panel. In some embodiments, the resolution of the extracted grayscale image is adjusted by an image interpolation algorithm. The image interpolation algorithm can be, for example, algorithms such as nearest neighbor interpolation, bilinear interpolation, and bicubic interpolation. Among them, for nearest neighbor interpolation, the target resolution needs to be determined first, that is, the number of rows and columns of the target image are determined. For each pixel (x, y) in the target image, its corresponding coordinates (x′, y′) in the original image are calculated. Since it is nearest neighbor interpolation, the corresponding coordinates (x′, y′) of the pixel (x, y) in the target image in the original image can be calculated through a simple proportional relationship. According to the calculated coordinates (x′, y′), the nearest pixel point in the original image is found, and the grayscale value or color value of this pixel point is assigned to the pixel (x, y) in the target image to complete the resolution adjustment. Bilinear interpolation is similar to nearest neighbor interpolation. First, the target resolution is determined, and then the corresponding coordinates of the target image pixels in the original image are calculated. In bilinear interpolation, the coordinates corresponding to the pixel (x, y) in the target image in the original image (x′, y′) may be non-integer. Then, the four neighboring pixel points around (x′, y′) are found, that is where represents rounding down, represents rounding up. According to the distances between (x′, y′) and the four neighboring pixel points, the weights of bilinear interpolation are calculated. Let then the weights of the four pixel points are w 11 =(1 - u)(1 - v), w 12 =(1 - u)v, w 21 =u(1 - v), w 22 =uv. According to the weights, the grayscale value or color value of the pixel (x, y) in the target image is calculated. The formula is I(x, y)=w 11 I 11 +w 12 I 12 +w 21 I 21 +w 22 I 22 , where I ijRepresents the grayscale values or color values of four adjacent pixel points. Bicubic interpolation determines 16 adjacent pixel points around (x′, y′), that is, with (x′, y′) as the center, 4 adjacent pixel points are taken in the horizontal and vertical directions respectively. Then a cubic polynomial function is used to fit the change of the image in the local area. For the horizontal direction, according to the relationship between x′ and the horizontal coordinates of these 16 pixel points, a cubic interpolation function f(x) is constructed; for the vertical direction, according to the relationship between y′ and the vertical coordinates of these pixel points, another cubic interpolation function g(y) is constructed. Substitute x′ into f(x) to get the interpolation result in the horizontal direction, substitute y′ into g(y) to get the interpolation result in the vertical direction, and multiply the two to get the grayscale value or color value of the pixel (x, y) in the target image.

[0113] In some embodiments, before image segmentation, step 530, image denoising, is also required. The pictures taken by the imaging device may contain some image noises such as salt-and-pepper noise. Therefore, before image segmentation, the image noise of picture img1 can be removed first through algorithms such as median filtering algorithm. The median filtering algorithm is based on the theory of sorting statistics and replaces the value of a pixel with the median value within the pixel's neighborhood, so as to achieve the purpose of removing noise. Specifically, it first needs to select a suitable filter window size. Usually, the window shape is square or rectangular, such as 3×3, 5×5, etc. The window size determines the range of pixels participating in the median calculation. The larger the window, the more obvious the filtering effect, but it may cause loss of image details; the smaller the window, the relatively weaker the ability to suppress noise, but it can better retain image details. Starting from the upper left corner of the image, traverse each pixel in the image row by row and column by column with the selected filter window. For the pixels at the image boundary, since their neighborhoods may be incomplete, various processing methods can be adopted, such as ignoring the boundary pixels without filtering, expanding the image boundary so that the filter window can completely cover, etc. For the pixel being processed currently, extract all the pixel values within its neighborhood according to the size of the filter window with it as the center. For example, for a 3×3 filter window, the neighborhood of the current pixel includes itself and the 8 surrounding pixels. Use sorting algorithms such as quicksort and bubble sort to sort the extracted neighborhood pixel values. Then take the middle value after sorting as the median. If the number of pixels in the neighborhood is odd, then the median is the pixel value at the middle position after sorting; if the number of pixels is even, usually take the average of the two middle pixel values as the median. Replace the original value of the current pixel with the calculated median, thus completing the filtering process of this pixel. According to the above steps, each pixel in the image is processed until the entire image is traversed. At this time, the image obtained is the image after removing noise by median filtering.

[0114] Based on the optimization method as before, Figure 7The flowchart shows a gamma correction compensation method according to an embodiment of the present invention. As Figure 7 shown, a gamma correction compensation method includes:

[0115] First, in step 701, optimize the positioning map. Use the optimization method as before to compensate and optimize the positioning map;

[0116] Next, in step 702, display the optimized positioning map. Drive the display panel to display the optimized positioning map. After optimization, the positioning map is displayed on the display panel, and the brightness of each positioning point is relatively uniform; and

[0117] Finally, in step 703, perform gamma correction. Based on the optimized positioning map, perform gamma correction on the display panel. The gamma correction can be operated using common processing methods in the art and will not be elaborated here. When performing gamma correction, directly display the optimized positioning map, which can skip the process of identifying the pixels of the display panel and the alignment between the image and the pixels in the captured image, that is, the gamma correction process will be decoupled from the pixels of the display panel, thereby avoiding the calibration error caused by inaccurate identification of the display panel pixels, greatly reducing the difficulty of gamma correction and improving the accuracy of gamma correction.

[0118] Figure 8 The structural diagram shows an optimization system for a positioning map for gamma correction compensation according to an embodiment of the present invention. As Figure 8 shown, an optimization system for a positioning map for gamma correction compensation includes an imaging device 801, a second filtering module 802, an image extraction module 803, a resolution adjustment module 804, a first filtering module 805, a compensation coefficient calculation module 806, and a compensation module 807.

[0119] The imaging device 801 is used to capture the image of the display panel, and it can be, for example, an industrial camera, an imaging luminance meter, an imaging colorimeter, etc.

[0120] The second filtering module 802 is communicatively connected to the imaging device 801 and is used to filter the image captured by the imaging device 801 to remove the noise in the image. In some embodiments, the second filtering module 802 uses a median filtering algorithm to eliminate salt-and-pepper noise in the image.

[0121] The image extraction module 803 is communicatively connected to the imaging device 801 and is used to extract the grayscale image displayed on the display panel from the image captured by the imaging device 801. In some embodiments, the image extraction module 803 is communicatively connected to the second filtering module 802, and it extracts the grayscale image displayed on the display panel from the filtered image.

[0122] In some embodiments, the image extraction module 803 includes an image segmentation sub-module 831 and a shape fitting sub-module 832. The image segmentation sub-module 831 is used for image segmentation, including performing adaptive local gray-scale threshold segmentation on the filtered image to generate a binary image, then setting operation parameters, and connecting the disconnected regions in the binary image through morphological closing operation to generate multiple connected regions, so as to extract the regions with higher brightness from the image captured by the imaging device 801. The shape fitting sub-module 832 is communicatively connected to the image segmentation sub-module 831 and is used for shape fitting of the connected regions. In some embodiments, the shape fitting sub-module 832 mainly calculates the area of each connected region through algorithms such as contour analysis algorithm, and adjusts the shape of the connected region with the largest area to be consistent with the display area of the display panel through algorithms such as point set polygon algorithm, etc.

[0123] The resolution adjustment module 804 is communicatively connected to the image extraction module 803 and is used to adjust the resolution of the image extracted by the image extraction module 803 to be consistent with the resolution of the display panel by using methods such as image interpolation algorithm, etc., so as to facilitate subsequent driving the display panel to display the optimized positioning map.

[0124] The first filtering module 805 is communicatively connected to the resolution adjustment module 804 and is used for low-pass filtering of the image to remove high-frequency noise in the image. In some embodiments, the first filtering module 805 includes a frequency domain conversion sub-module, a low-pass filter, and a spatial domain conversion sub-module. The frequency domain conversion sub-module is used to convert the image to the frequency domain by using discrete Fourier transform (DFT) or fast Fourier transform (FFT). Each point in the obtained frequency domain image corresponds to a specific frequency component, the amplitude of which represents the intensity of the frequency component, and the phase represents the relative position of the frequency component. The low-pass filter is used for low-pass filtering, multiplying the frequency domain representation of the gray image point by point with the transfer function of the low-pass filter, which is equivalent to weighting each frequency component of the image in the frequency domain. The high-frequency components are attenuated due to the action of the filter, while the low-frequency components remain basically unchanged. The low-pass filter can be, for example, an ideal low-pass filter, a Butterworth low-pass filter, and a Gaussian low-pass filter, etc. The spatial domain conversion sub-module is used to convert the filtered frequency domain image back to the spatial domain by using inverse Fourier transform (IDFT) or inverse fast Fourier transform (IFFT) to obtain the denoised image.

[0125] The compensation coefficient calculation module 806 is communicatively connected to the first filtering module 805 and is used to calculate the compensation coefficient based on the low-pass filtered image. In some embodiments, the compensation coefficient calculation module 806 is used to perform inverse gamma processing on the gray-scale image to obtain an inverse gamma image, and then determine the compensation coefficient according to the gray-scale mean value of the inverse gamma image.

[0126] The compensation module 807 is communicably connected to the compensation coefficient calculation module 806, which is used to compensate the positioning map based on the compensation coefficient. Specifically, the gray value of each pixel point of the positioning map is multiplied by the corresponding compensation coefficient to obtain an optimized positioning map.

[0127] Based on the optimization system as described above, Figure 9 FIG. shows a schematic structural diagram of a gamma correction compensation system according to an embodiment of the present invention. As Figure 9 shown, a gamma correction compensation system includes the optimization system 800 as described above and a gamma correction module 901. The optimization system 800 is used to generate an optimized positioning map. The gamma correction module 901 can be implemented by using common gamma correction devices or modules in the art, and its specific structure and working principle will not be elaborated herein. The gamma correction module 901 is communicably connected to the optimization system 800 and is used to perform gamma correction on the display panel based on the optimized positioning map.

[0128] It should be noted that in some embodiments, the display panel refers to a micro display panel, including micro light-emitting diode chips, and the size of each micro light-emitting diode chip does not exceed 1 cm, preferably does not exceed 20 microns. The micro light-emitting diode structure in the micro light-emitting diode chip is formed in an array form, and the resolution is, for example, 720*480, 640*480, 1920*1080, 1280*720, 2K or 4K. The diameter of the micro light-emitting diode structure is at the nanometer level, for example, 20 nm to 100 nm.

[0129] In some embodiments, the micro light-emitting diode array may include a single-layer micro light-emitting diode structure. In some embodiments, the pitch of the micro light-emitting diode array, that is, the minimum center-to-center distance between micro light-emitting diodes, may be between about 2 microns and about 50 microns. In some embodiments, the number of pixels on the micro light-emitting diode chip may be between several thousand and several million.

[0130] In some embodiments, the micro light-emitting diodes may be arranged on the driving backplane in a regular or irregular manner as the pixel points of the micro light-emitting diode chip.

[0131] In some embodiments, the driving backplane can be electrically connected to each micro light-emitting diode in the micro light-emitting diode array through separate metal interconnections. In some embodiments, each micro light-emitting diode can be electrically controlled separately by the driving backplane. In some embodiments, the driving backplane can be electrically connected to the electrodes of the micro light-emitting diode chip through metal interconnections. In some embodiments, the driving backplane is an IC backplane.

[0132] In some embodiments, the driving backplane includes a substrate, a driving circuit, and driving electrodes. The driving circuit is located in the substrate and controls the lighting and extinguishing of the micro light-emitting diodes; the driving electrodes are located in the substrate and at least the upper surface is exposed, and the driving electrodes are electrically connected to the driving circuit. Each micro light-emitting diode corresponds to one driving electrode, and the micro light-emitting diode is located on the driving electrode and electrically connected to the driving electrode.

[0133] In some embodiments, the material of the driving electrode is an alloy of one or more of the following metals: Ni, Al, Ti, Cu, Pt, and Au. In some embodiments, the substrate is a Si substrate. In some other embodiments, the substrate is a transparent substrate, such as a glass substrate. Examples of other substrates include GaAs, GaP, InP, SiC, ZnO, and sapphire substrates. In some embodiments, the substrate is about 700 micrometers thick. The driving circuit forms individual pixel drivers to control the operation of each individual pixel LED device. The driving circuit includes, for example, complementary metal oxide semiconductor (CMOS) devices or TFT devices, etc. In some embodiments, a dielectric layer can be formed in the gaps between the micro light-emitting diodes. In some embodiments, the dielectric layer can also be formed in the gaps between the interconnections.

[0134] The driving method of the micro light-emitting diodes is, for example, passive matrix (PM) driving, where the cathodes of all the micro light-emitting diodes in each array are commonly connected to the cathode line NL, and the micro light-emitting diodes with the same number in each array are respectively connected to the corresponding anode line PL. Thus, the on / off and light-emitting brightness of each light-emitting diode can be individually controlled by controlling the signals on the corresponding cathode line and anode line.

[0135] In some embodiments, the micro light-emitting diodes can be bonded to the surface of the driving backplane through a bonding layer. The driving electrode is electrically connected to the bonding layer, and the bonding layer includes a first metal layer and a second metal layer. In some embodiments, the material of the first metal layer is an alloy of one or more of the following metals: Cr, Al, Ti, Ni, Pt, Au, and Sn; and / or the material of the second metal layer is an alloy of one or more of the following metals: Cr, Al, Ti, Ni, Pt, Au, and Sn.

[0136] In some embodiments, the micro light-emitting diodes include: an epitaxial layer, an ohmic contact layer, a top conductive layer, and a passivation isolation layer. The ohmic contact layer is located on the bonding layer and is electrically connected to the bonding layer. The epitaxial layer is disposed on the ohmic contact layer. The passivation isolation layer at least partially covers the side surfaces of the epitaxial layer, and the passivation isolation layer is located between the epitaxial layer and the top conductive layer. The top conductive layer is located on the side surfaces and the top surface of the epitaxial layer.

[0137] In some embodiments, the material of the passivation isolation layer is, for example, a solid inorganic material or a plastic material. In some embodiments, the solid inorganic material includes SiO2, Al2O3, Si3N4, SiCN, HfO2, Ta2O5, TiO2, ZrO2, La2O3, MgO, phosphosilicate glass (PSG), borophosphosilicate glass (BPSG), or any combination thereof. In some embodiments, the plastic material includes polymers such as SU-8, PermiNex, benzocyclobutene (BCB), or a transparent plastic (resin) including spin-on glass (SOG), or a binder microresist BCL-1200, or any combination thereof. The passivation isolation layer is transparent to the light emitted by the epitaxial layer.

[0138] In some embodiments, the first metal layer of the bonding layer is in direct contact with the ohmic contact layer at the bottom of the epitaxial layer, and the second metal layer is located at the bottom layer of the bonding layer, away from the epitaxial layer, wherein the profile of the first metal layer is smaller than the profile of the second metal layer.

[0139] In some embodiments, the bottom lateral dimension of the epitaxial layer is larger than the top lateral dimension. In some embodiments, the optical mesa is stepped or trapezoidal.

[0140] In some embodiments, the epitaxial layer is trapezoidal, not limited to a regular trapezoid or an inverted trapezoid. In some embodiments, the inclination angle range of the side wall of the epitaxial layer is 60° to 85°. In one embodiment, the lateral dimension of the bonding layer is larger than the bottom lateral dimension of the epitaxial layer.

[0141] In some embodiments, the epitaxial layer includes a first-type epitaxial layer, a second-type epitaxial layer, and a light-emitting layer therebetween. The first-type epitaxial layer is located above the light-emitting layer, away from the driving backplane, and the second-type epitaxial layer is located below the light-emitting layer, close to the driving backplane.

[0142] In some embodiments, the light-emitting layer is formed by a plurality of stacked quantum well layers, especially superlattice-stacked quantum well layers. Preferably, the superlattice-stacked quantum well layers include multiple pairs of quantum well layers stacked with quantum barrier layers.

[0143] In one embodiment, the light-emitting layer includes multiple quantum well layers and an electron blocking layer, and the multiple quantum well layers are InGaN / GaN multiple quantum well layers or InGaN / AlGaN multiple quantum well layers or InGaAs / AlGaAs multiple quantum well layers. In another embodiment, the first-type epitaxial layer can also be a P-type GaN layer or a P-type AlGaN layer, and the second-type epitaxial layer is an N-type GaN layer or an N-type AlGaN layer.

[0144] In some embodiments, the first type of epitaxial layer is a semiconductor material of a first conductivity type and includes a plurality of semiconductor layers. The main matrix material of the first type of epitaxial layer can be, but is not limited to, composed of at least two or more elements among Ga, N, As, P, In, and Al. In addition, the first type of epitaxial layer may include, but is not limited to, a confinement layer and a waveguide layer from top to bottom; in addition, in some embodiments, an ohmic contact layer may be formed on the confinement layer.

[0145] In some embodiments, the second type of epitaxial layer is a semiconductor material of a second conductivity type and includes a plurality of semiconductor layers. The main matrix material of the second type of epitaxial layer can be, but is not limited to, composed of materials such as Ga, N, As, P, In, or Al. In addition, the second type of epitaxial layer may include, but is not limited to, a waveguide layer, a confinement layer, a transition layer, and a window layer from top to bottom; in addition, an ohmic contact layer may be formed below the window layer.

[0146] In some embodiments, the first type of epitaxial layer is an N-type GaN layer or an N-type AlGaN layer, and the second type of epitaxial layer is a P-type GaN layer or a P-type AlGaN layer, that is, the material of the second type of epitaxial layer can be a material layer of a second conductivity type composed of at least two or more elements among Ga, N, As, Al, In, and P, and the first type of epitaxial layer can be a material layer of a first conductivity type composed of at least two or more elements among Ga, N, As, Al, In, and P.

[0147] In some embodiments, the light-emitting layer includes at least one quantum well layer. The thickness of the quantum well layer is between 20 nm and 40 nm, for example, the thickness is 30 nm. In some embodiments, the material of the quantum well layer is GaInP / (Al x Ga 1-x ) y In 1-y P, where the range of x is 0.5 to 0.9, and the range of y is 0.3 to 0.5. For example, x is 0.8 and y is 0.5. In some embodiments, the relationship between x and y is that x is 1 to 2 times y. In some embodiments, the light-emitting layer is a multi-quantum well (MQW).

[0148] In some embodiments, one of the first type of epitaxial layer and the second type of epitaxial layer is an N-type semiconductor layer, and the other is a P-type semiconductor layer. In some embodiments, the N-type semiconductor layer further includes a doped N-type contact layer and an N-type cladding layer. The N-type cladding layer is formed on the doped N-type contact layer. The material of the N-type cladding layer is Al x In 1-x P, where the range of x is 0.1 to 0.5, for example, x is 0.5. In addition, in these embodiments, the thickness of the N-type cladding layer is not greater than 350 nm. For example, the thickness of the N-type cladding layer is 320 nm. The doping concentration of the N-type cladding layer is 5e17 cm -3 to 1e 18 cm -3 。In some embodiments, the N-type semiconductor layer further includes a doped N-type contact layer and an N-type cladding layer formed on the doped N-type contact layer. The material of the doped N-type contact layer is GaAs. In some embodiments, the thickness of the doped N-type contact layer is from 10 nm to 30 nm. In some embodiments, the doping concentration of the doped N-type contact layer is 2e 18 cm -3 to 1e 19 cm -3 。In some embodiments, the N-type semiconductor layer further includes an N-type spacer layer formed on the N-type cladding layer. The material of the N-type spacer layer is (Al x Ga 1-x ) y In 1-y P, where the range of x is from 0.5 to 0.9, and the range of y is from 0.1 to 0.5. For example, x is 0.8 and y is 0.5. In some embodiments, the relationship between x and y is that x is 1 to 2 times y. The thickness of the N-type spacer layer is from 50 nm to 75 nm, for example 65 nm. In some embodiments, the P-type semiconductor layer includes a P-type cladding layer and a doped P-type contact layer. The P-type cladding layer is formed on the light-emitting layer, and the doped P-type contact layer is formed on the P-type cladding layer.

[0149] In some embodiments, the material of the P-type cladding layer is Al x In 1-x P, where x is from 0.3 to 0.5, for example x is 0.5. In such embodiments, the thickness of the P-type cladding layer is not greater than 380 nm. For example, the thickness of the P-type cladding layer is 360 nm.

[0150] In some embodiments, the material of the doped P-type contact layer is GaAs. The thickness of the doped P-type contact layer is from 10 nm to 30 nm, for example 20 nm.

[0151] In some embodiments, the P-type semiconductor layer further includes a P-type spacer layer formed under the P-type cladding layer, a first doped P-type transition layer formed on the P-type cladding layer, and a second doped P-type transition layer formed on the first doped P-type transition layer. In some embodiments, the material of the P-type spacer layer is (Al x Ga 1-x ) y In 1-yP, where the range of x is from 0.5 to 0.9, and the range of y is from 0.3 to 0.5. For example, x is 0.8 and y is 0.5. In some embodiments, the relationship between x and y is that x is 1 to 2 times y. In some embodiments, the thickness of the P-type spacer layer is from 50 nm to 70 nm, such as 65 nm.

[0152] In some embodiments, the material of the first doped P-type transition layer is (Al x Ga 1-x ) y In 1-y P, where the range of x is from 0.1 to 0.3, and the range of y is from 0.3 to 0.5. For example, x is 0.17 and y is 0.5. In some embodiments, the relationship between x and y is that y is 1 to 5 times x. In some embodiments, the thickness of the first doped P-type transition layer is from 20 nm to 40 nm, such as 30 nm.

[0153] In some embodiments, the material of the second doped P-type transition layer is Al x Ga 1-x As, where the range of x is from 0.5 to 0.9, for example x is 0.6. In some embodiments, the thickness of the second doped P-type transition layer is from 10 nm to 30 nm, such as 20 nm.

[0154] In some embodiments, the doping concentration of the second doped P-type transition layer is greater than the doping density of the first doped P-type transition layer. The doping concentration of the doped P-type contact layer is 1 to 10 times the doping concentration of the second doped P-type transition layer.

[0155] In some embodiments, the doping concentration of the doped P-type contact layer is greater than the doping concentration of the second doped P-type transition layer. In addition, in some embodiments, the doping concentration of the second doped P-type transition layer is 2 to 4 times the doping concentration of the first doped P-type transition layer.

[0156] For example, the doping concentration of the first doped P-type transition layer is greater than 1e 18 cm -3 , the doping density of the second doped P-type transition layer is in the range of 2e 18 cm -3 -4e 18 cm -3 The doping density of the doped P-type contact layer is greater than 5e 18 cm -3。In some embodiments, the electrode polarity of the ohmic contact layer is opposite to that of the top conductive layer. For example, the ohmic contact layer can be a P electrode or an anode electrode, and the top conductive layer is an electrode with a polarity opposite to that of the ohmic contact layer, such as an N electrode or a cathode electrode. In one embodiment, the ohmic contact layer, the top conductive layer, and their connecting components can be a combination of one or more of, for example, graphene, indium tin oxide (ITO), antimony doped zinc oxide (AZO), fluorine doped tin oxide (FTO), or other transparent conductive oxides (TCO).

[0157] In one embodiment, adjacent top conductive layers are connected, and all top conductive layers are connected into a whole. In some embodiments, the top conductive layer can be shared by all the micro light-emitting diodes in the micro light-emitting diode array.

[0158] In some embodiments, the electrode polarity of the ohmic contact layer is opposite to that of the top conductive layer. For example, the ohmic contact layer can be a P electrode or an anode electrode, and the top conductive layer is an electrode with a polarity opposite to that of the ohmic contact layer, such as an N electrode or a cathode electrode. In one embodiment, the ohmic contact layer, the top conductive layer, and their connecting components can be a combination of one or more of, for example, graphene, indium tin oxide (ITO), antimony doped zinc oxide (AZO), fluorine doped tin oxide (FTO), or other transparent conductive oxides (TCO).

[0159] In some embodiments, adjacent passivation isolation layers are connected, and all passivation isolation layers are connected into a whole. In one embodiment, the material of the passivation isolation layer is one or more of silicon oxide, silicon oxynitride, aluminum oxide, and silicon nitride.

[0160] In some embodiments, the micro light-emitting diode chip further includes a current spreading structure, which is located between the micro light-emitting diodes. The current spreading structure is arranged to surround the micro light-emitting diodes, and the current spreading structure is configured to be in electrical contact with the micro light-emitting diodes and at least partially reflect the light emitted by the micro light-emitting diodes.

[0161] The current spreading structure surrounds the micro light-emitting diodes, and the current spreading structure is electrically connected to the micro light-emitting diodes.

[0162] The surface of the current spreading structure facing the micro light-emitting diode has light reflection ability. For example, it is made of metal, enabling the current spreading structure to at least partially reflect the light emitted by the light-emitting diode. The reflection process is as follows: The light emitted from the light-emitting layer of the light-emitting diode passes through the transparent layer thereon (such as the top conductive layer), and then the first part of this light (whose exit angle is small enough not to hit the side current spreading structure, within the preset light-emitting angle, such as within plus or minus 20°) is directly emitted. The second part of this light (whose exit angle is large enough to hit the side current spreading structure) hits the current spreading structure and is emitted after reflection, changing the light path direction to within the preset light-emitting angle, thereby effectively improving the light extraction efficiency. Preferably, the proportion of the light reflected by the current spreading structure in the light emitted by the light-emitting diode can be, for example, 10% to 60%. By providing a current spreading structure with light reflection ability, the amount of light absorbed by the sidewalls can be significantly reduced, thus significantly increasing the total light output. At the same time, the current spreading structure can also isolate light and prevent light crosstalk between adjacent light-emitting diodes.

[0163] By arranging the current spreading structure to electrically contact the top conductive layer of the micro light-emitting diode in a surrounding manner, the electrical contact area between the current spreading structure and the micro light-emitting diode can be significantly increased, enabling the active layer (light-emitting layer) of the micro light-emitting diode to emit light more uniformly, effectively avoiding the situation where only the electrically contacted part or its vicinity emits light or the light emission brightness in the electrically contacted part or its vicinity is too high.

[0164] The size of the bottom of the current spreading structure is larger than that of the top. Since the bottoms of adjacent current spreading structures are connected, the longitudinal cross-section of two adjacent current spreading structures presents a forked peak shape.

[0165] The bottoms of adjacent current spreading structures are connected, and all the current spreading structures are integrated into a whole. For the top view shape (i.e., cross-sectional shape) of the micro light-emitting diode being circular, the top view shape of the overall current spreading structure is the remaining grid shape after removing the circle. In other embodiments, the top view shape of the micro light-emitting diode may also be other appropriate shapes, such as rectangular, square, or regular polygon, etc. The top view shape of the overall current spreading structure can also be the remaining shape after removing other appropriate shapes, such as the remaining grid shape after removing a rectangle, square, or polygon.

[0166] In the embodiment of the present invention, the bottom of the current spreading structure is lower than the epitaxial layer of the micro light-emitting diode.

[0167] In an embodiment of the present invention, the top of the current spreading structure may be higher than the top of the epitaxial layer; the top of the current spreading structure may also be flush with the top of the epitaxial layer; the top of the current spreading structure may also be lower than the top of the epitaxial layer (for example, slightly lower than the top of the epitaxial layer by 0-1 micrometer). One, two, or three of the above situations may exist simultaneously in a chip.

[0168] Preferably, the top of the current spreading structure is higher than the top of the epitaxial layer of the micro light-emitting diode. By making the height of the top of the current spreading structure greater than the height of the top plane of the epitaxial layer of the micro light-emitting diode, a higher current spreading structure can be obtained, further increasing the chance of light reflection and the light extraction efficiency.

[0169] In other embodiments, the number of current spreading structures may also be 1 / 4 or 1 / 9 of the number of micro light-emitting diodes. Each current spreading structure surrounds 4 micro light-emitting diodes, or 9 micro light-emitting diodes, without limitation.

[0170] The current spreading structure can increase the current spreading between adjacent micro light-emitting diodes, reduce the resistance between adjacent micro light-emitting diodes, and reduce losses. The current spreading structure can quickly and evenly spread the current to all micro light-emitting diodes.

[0171] In an embodiment of the present invention, the current spreading structure may be a multi-layer structure, and the current spreading structure includes one or more main metal layers. In an embodiment of the present invention, the material of the main metal layer may be one or more of Pt, Au, Al, and Ag.

[0172] In some embodiments, the current spreading structure may further include: isolation layers corresponding to each layer of the main metal layer one by one; wherein, the isolation layers and the main metal layers are arranged in an alternating manner, and each layer of the main metal layer is located on the corresponding isolation layer.

[0173] By adopting isolation layers corresponding to each layer of the main metal layer one by one, and arranging the isolation layers and the main metal layers in an alternating manner, with each layer of the main metal layer located on the corresponding isolation layer, the influence of electromigration in the current spreading structure can be effectively suppressed by setting the isolation layers. Especially in the case of a relatively high density of micro light-emitting diodes in the micro light-emitting diode display chip, the possibility of increasing the height of the current spreading structure can be obtained by setting the isolation layers, and then the light extraction efficiency can be further improved through a higher current spreading structure. Further, the isolation layer may include: a titanium (Ti) metal layer. It should be noted that the material of the isolation layer may also include other suitable materials, such as titanium nitride (TiN).

[0174] In some embodiments, the current spreading structure may further include: an adhesion layer located at the bottommost layer of the current spreading structure, with the isolation layer and the main metal layer above the adhesion layer. An adhesion layer is formed between the micro light-emitting diodes of the adhesion layer, and the isolation layer and the main metal layer are located on the adhesion layer. Through the adhesion of the adhesion layer, the bottom stability of the current spreading structure can be effectively improved. Especially when the density of the micro light-emitting diodes in the micro light-emitting diode display chip is relatively large, by setting the adhesion layer, it is possible to increase the height of the current spreading structure, and then further improve the light extraction efficiency through a higher current spreading structure. Further, the adhesion layer may include: a chromium (Cr) metal layer. It should be noted that the material of the adhesion layer may also include other suitable materials, such as one or more of the following: titanium (Ti), titanium nitride (TiN), tungsten (W).

[0175] In an embodiment of the present invention, the current spreading structure may further include: an anti-diffusion layer corresponding to the isolation layer one by one, with each isolation layer located on the corresponding anti-diffusion layer. By forming an anti-diffusion layer corresponding to the isolation layer one by one and each isolation layer being located on the corresponding anti-diffusion layer, the stability of the current spreading structure can be improved due to the high hardness and good anti-corrosion effect of the anti-diffusion layer. Especially when the density of the micro light-emitting diodes in the micro light-emitting diode display chip is relatively large, by setting the anti-diffusion layer, it is possible to increase the height of the current spreading structure, and then further improve the light extraction efficiency through a higher current spreading structure. The anti-diffusion layer may include: a platinum (Pt) metal layer, a nickel (Ni) metal layer. It should be noted that the anti-diffusion layer may be a single-layer platinum metal layer, or a single-layer nickel metal layer, or a stack of a single-layer platinum metal layer and a single-layer nickel metal layer.

[0176] In some embodiments, the micro light-emitting diode chip further includes a microlens array. The microlens array is disposed above the micro light-emitting diode array, wherein at least one microlens is disposed on the surface of the conductive layer at the top of the micro light-emitting diode, and the horizontal profile of the microlens is larger than the maximum horizontal profile of the micro light-emitting diode. The microlens is mainly used to converge and / or collimate light. For example, by adjusting parameters such as the thickness and curvature of the microlens, the focal point of the microlens can be located in the epitaxial layer of the micro light-emitting diode. In some embodiments, the microlenses of the microlens array correspond to the epitaxial layer one by one. In some embodiments, examples of the microlens include a spherical microlens, an aspherical microlens, a Fresnal microlens, and a cylindrical microlens.

[0177] In an embodiment of the present invention, there is a gap between adjacent microlenses. In an embodiment of the present invention, the bottom of the gap is higher than the top of the epitaxial layer. In another embodiment of the present invention, the bottom of the gap is lower than the top of the epitaxial layer and higher than the bottom of the epitaxial layer. In another embodiment of the present invention, the bottom of the gap is located above the current spreading structure. Specifically, the gap is located between two adjacent current spreading structures (i.e., between the forked peaks).

[0178] In addition, an air gap may be present inside the microlens. There may be multiple air gaps in each lens, and the sizes and lengths of the respective air gaps may be the same or different. At the same time, in the same chip, the number and / or position and / or size of the air gaps in different microlenses may be the same or different. In some embodiments of the present invention, the air gap is located at the edge of the microlens. Specifically, for example, it may be located on both sides of the epitaxial layer. Preferably, it is located between the epitaxial layer and the current spreading structure. At the same time, in some embodiments, the top of the air gap is higher than the top of the epitaxial layer, and its bottom may be higher than the top of the epitaxial layer or lower than the top of the epitaxial layer. In some embodiments, the bottom of the air gap is higher than the top of the current spreading structure. In still other embodiments, the bottom of the air gap is lower than the top of the current spreading structure. It should be noted that in other embodiments of the present invention, there may be no air gap inside the microlens.

[0179] In some embodiments, the micro light-emitting diode chip includes a light-emitting region and a non-light-emitting region. The above-mentioned micro light-emitting diodes, current spreading structures, and microlens arrays are located in the light-emitting region. The non-light-emitting region surrounds the light-emitting region.

[0180] In some embodiments, the non-light-emitting region of the micro light-emitting diode chip has a wire bonding electrode, and the wire bonding electrode is electrically connected to the driving backplane. The wire bonding electrode is used to be electrically connected to a circuit board outside the chip.

[0181] Although the embodiments of the present invention have been described above, it should be understood that they are presented only as examples and not as limitations. It will be apparent to those skilled in the relevant art that various combinations, modifications, and changes can be made thereto without departing from the spirit and scope of the present invention. Therefore, the width and scope of the present invention disclosed herein should not be limited by the above-disclosed exemplary embodiments, but should be defined only by the appended claims and their equivalents.

Claims

1. An optimization method for a positioning map used for gamma correction compensation, characterized in that, Including: Obtaining a compensation coefficient image based on the grayscale image displayed on the display panel; And Compensating the positioning map based on the compensation coefficient image.

2. The optimization method according to claim 1, wherein The obtaining of the compensation coefficient image based on the grayscale image displayed on the display panel includes: Performing an inverse gamma processing on the grayscale image to obtain an inverse gamma image; Calculating the grayscale mean value of the inverse gamma image; and Determining the compensation coefficient image based on the grayscale mean value.

3. The optimization method according to claim 2, wherein Performing the inverse gamma processing on the grayscale image includes: Calculating the grayscale value x of each pixel point of the grayscale image according to the following formula to obtain the grayscale value y of the corresponding pixel point in the inverse gamma image: y = x 1 / γ , where γ is the gamma coefficient of the display panel.

4. The optimization method according to claim 2, characterized in that The determining of the compensation coefficient image based on the grayscale mean value includes: Calculating the grayscale value y of each pixel point of the inverse gamma image according to the following formula to obtain the compensation coefficient of the corresponding pixel point, and forming the compensation coefficient image: z = mean / y, where mean refers to the grayscale mean value of the inverse gamma image, and the grayscale mean value is equal to the sum of the grayscale values of all pixel points of the inverse gamma image divided by the total number of pixel points of the inverse gamma image.

5. The optimization method according to claim 2, characterized in that The obtaining of the compensation coefficient image based on the grayscale image displayed on the display panel further includes: Removing high-frequency noise in the grayscale image before performing the inverse gamma processing.

6. The optimization method according to claim 5, wherein The removing of the high-frequency noise in the grayscale image includes: Performing low-pass filtering on the grayscale image.

7. The optimization method according to claim 6, characterized in that, The performing of the low-pass filtering on the grayscale image includes: Converting the grayscale image to the frequency domain through discrete Fourier transform or fast Fourier transform to obtain a frequency domain image; Multiplying the frequency domain image point by point with the transfer function of the low-pass filter; and Converting the filtered frequency domain image back to the spatial domain through inverse Fourier transform or fast inverse Fourier transform to obtain a denoised image.

8. The optimization method according to claim 7, characterized in that, The low-pass filter includes an ideal low-pass filter, or a Butterworth low-pass filter, or a Gaussian low-pass filter.

9. The optimization method according to claim 1, wherein It further includes: Taking a picture of the display panel displaying a grayscale screen through an imaging device, and extracting the grayscale image from the picture.

10. The optimization method according to claim 9, wherein The imaging device includes an industrial camera, or an imaging luminance meter, or an imaging colorimeter.

11. The optimization method according to claim 9, characterized in that The extracting of the grayscale image from the picture includes: Extracting the grayscale screen displayed on the display panel from the picture to obtain an extracted image; and Adjusting the resolution of the extracted image to be consistent with the resolution of the display panel to obtain the grayscale image.

12. The optimization method according to claim 11, wherein, The extracting of the grayscale screen displayed on the display panel from the picture includes: Performing image segmentation on the picture to obtain at least one connected region; Obtaining the area of each connected region through a contour analysis algorithm, and taking the connected region with the largest area as the contour of the display region; and Fitting the contour of the display region to obtain the extracted image having the same shape as the display region of the display panel.

13. The optimization method according to claim 12, wherein The performing of the image segmentation on the picture includes: Performing adaptive local grayscale threshold segmentation on the picture to generate a binary image; Connect the disconnected regions in the binary image through morphological closing operation to generate the at least one connected region.

14. The optimization method according to claim 13, characterized in that The adaptive local gray threshold segmentation of the picture includes: For each pixel point in the picture, with this pixel point as the center, calculate the gray scale statistics within the local window around it; Determine the adaptive threshold for each pixel point according to the calculated local gray scale statistics; and Compare the gray value of each pixel point with the corresponding adaptive threshold. If the gray value is greater than or equal to the adaptive threshold, determine this pixel point as a target pixel, usually assign it a value of 1, otherwise determine this pixel point as a background pixel and assign it a value of 0.

15. The optimization method according to claim 14, characterized in that, The size of the local window is an odd size.

16. The optimization method according to claim 14, wherein The calculating the gray scale statistics within the local window around it includes: Add up the gray values of all pixels within the local window, and then divide by the total number of pixels within the local window to obtain the gray mean value of this local area as the local gray scale statistic.

17. The optimization method according to claim 16, wherein The calculating the gray scale statistics within the local window around it further includes: Calculate the square value of the difference between each pixel and the gray mean value; and Calculate the average value of the square values to obtain the gray variance of this local area as the local gray scale statistic.

18. The optimization method according to claim 16, wherein Take the product of the gray mean value and a constant as the adaptive threshold, where the constant is an empirical constant with a value between 0 and 1.

19. The optimization method according to claim 13, wherein The connecting the disconnected regions in the binary image through morphological closing operation to generate the at least one connected region includes: Select a structuring element, and align the center of the structuring element with each pixel in the binary image in turn. If there is a pixel with a value of 1 within the area covered by the structuring element, update the value of the current center pixel to 1 to obtain a dilated image; and Align the center of the structuring element with each pixel in the dilated image in turn. When all pixels within the area covered by the structuring element are 1, keep the value of the current center pixel as 1, otherwise set it to 0.

20. The optimization method according to claim 19, wherein The size and shape of the structuring element are determined according to the size and shape of the disconnected regions to be connected in the binary image.

21. The optimization method according to claim 13, characterized in that, The obtaining the area of each connected region through the contour analysis algorithm includes: Mark the connected regions in the binary image through the connected component labeling algorithm; Traverse the labeled binary image and count the number of pixel points included in each connected region; and Multiply the number of pixel points by the area of a single pixel to obtain the area corresponding to the connected region.

22. The optimization method according to claim 21, wherein, Marking the connected regions in the binary image through the four-connected region labeling algorithm includes: Traverse each pixel in the binary image. For a pixel with a value of 1, if its adjacent pixels have been marked and belong to the same connected region, mark the current pixel with the same region number; if the adjacent pixels have not been marked, assign a new region number to the current pixel to achieve the marking of all connected regions, where the adjacent pixels refer to the four pixels above, below, left, and right of a pixel.

23. The optimization method according to claim 13, wherein The obtaining the area of each connected region through the contour analysis algorithm includes: Detect the edges in the binary image using the Canny algorithm to obtain an edge image; Select a starting point from the edge image and track along the edge in a preset direction until returning to the starting point to obtain a complete contour; Represent the extracted contour using the chain code representation method or the polygon approximation method; and Calculate the area of the connected region according to the representation method of the contour.

24. The optimization method according to claim 23, wherein If the contour is represented by the chain code representation method, calculate the area of the connected region through Green's formula; And If the contour is represented by the polygon approximation method, divide the obtained polygon into multiple triangles, then calculate the area of each triangle and sum them to obtain the area of the connected region.

25. The optimization method according to claim 12, characterized in that, The shape of the display area of the display panel is square, rectangular, polygonal, circular or irregular.

26. The optimization method according to claim 12, characterized in that, Use the point set polygon algorithm to fit the display contour region.

27. The optimization method according to claim 26, wherein The use of the point set polygon algorithm to fit the display contour region includes: Take the gray scale values of each pixel point in the display contour region as point set data and select one point as the starting point from them; Calculate the distance and angle between the starting point and other points, and select the point with the closest distance or the most reasonable angle change as the end point of the first side, connect the starting point and the end point to form the first side; and From the remaining point set, select the point that is closest to the end point of the current side and forms an angle within a preset range with the current side as the next vertex, and connect it with the previous vertex to form a new side. Continuously repeat this process. When all points are traversed, or the number of sides of the polygon reaches the preset value, or the distance between the newly added point and the starting point is less than the preset threshold, connect the last vertex with the starting point to form a closed polygon.

28. The optimization method according to claim 27, wherein Select the point with the smallest or largest coordinates, or the point with a uniform distance distribution from other points, as the starting point.

29. The optimization method according to claim 12, wherein Extracting the gray scale image displayed by the display panel from the picture further includes: Before performing the image segmentation, remove the image noise of the picture.

30. The optimization method according to claim 29, wherein Remove the image noise of the picture through the median filtering algorithm, including: Starting from the upper left corner of the picture, traverse each pixel in the picture row by row and column by column with a selected filtering window. For the pixel being processed currently, take it as the center and extract all pixel values within its neighborhood according to the size of the filtering window, where the filtering window is square or rectangular; Use to sort the extracted neighborhood pixel values and take the middle value after sorting as the median; and Replace the original value of the current pixel with the median to implement the filtering process of the pixel.

31. The optimization method according to claim 11, wherein, Adjusting the resolution of the extracted image includes: Adjust the resolution of the extracted image through the image interpolation algorithm to obtain the gray scale image.

32. The optimization method according to claim 31, characterized in that, The adjustment of the resolution of the extracted image through the image interpolation algorithm includes: For each pixel (x, y) in the gray scale image, calculate its corresponding coordinates (x′, y′) in the extracted image through a proportional relationship; According to the calculated coordinates (x′, y′), find the nearest pixel point in the extracted image, and assign the grayscale value of this pixel point to the pixel (x, y) in the grayscale image.

33. The optimization method according to claim 31, characterized in that, The resolution adjustment of the extracted grayscale picture by the image interpolation algorithm includes: For each pixel (x, y) in the grayscale image, calculate its corresponding coordinates (x′, y′) in the extracted image through a proportional relationship; Determine the four neighboring pixel points of the coordinates (x′, y′) where represents rounding down, represents rounding up; According to the distances between the coordinates (x′, y′) and the four neighboring pixel points, calculate the weights of bilinear interpolation: w 11 = (1 - u)(1 - v), w 12 = (1 - u)v, w 21 = u(1 - v), w 22 = uv, Among them Calculate the grayscale value of the pixel (x, y) in the grayscale image according to the weights: I(x,y) = w 11 I 11 + w 12 I 12 + w 21 I 21 + w 22 I 22 , where I ij is the gray value of the four adjacent pixel points.

34. The optimization method according to claim 31, characterized in that, The resolution adjustment of the extracted grayscale picture by the image interpolation algorithm includes: For each pixel (x, y) in the grayscale image, calculate its corresponding coordinates (x′, y′) in the extracted image through a proportional relationship; Taking 4 adjacent pixel points in the horizontal and vertical directions respectively with the coordinates (x′, y′) as the center to determine 16 neighboring pixel points of the coordinates (x′, y′); For the horizontal direction, construct a cubic interpolation function f(x) according to the horizontal coordinate relationship between x′ and the 16 neighboring pixel points, and for the vertical direction, construct a cubic interpolation function g(y) according to the vertical coordinate relationship between y′ and the 16 neighboring pixel points; and Substitute x′ and y′ into f(x) and g(y) respectively to obtain the interpolation results in the horizontal and vertical directions, and multiply the two to obtain the grayscale value of the pixel (x, y) in the grayscale picture.

35. The optimization method according to claim 1, characterized in that, The compensation for the positioning map based on the compensation coefficient image includes: Multiply the grayscale value of each pixel point of the positioning map by the corresponding compensation coefficient to obtain an optimized positioning map.

36. The optimization method according to claim 35, wherein The positioning map includes multiple positioning points, and the grayscale values of each of the positioning points are the same.

37. The optimization method according to claim 36, wherein The compensation coefficient makes: The grayscale values of the positioning points in the first area of the positioning map are reduced; and The grayscale values of the positioning points in the second area of the positioning map are increased, where the imaging brightness of the first area when displayed on the display panel is greater than the imaging brightness in the second area, so that when the optimized positioning map is displayed on the display panel, the brightness difference between the positioning points is reduced.

38. An optimization system for a positioning map for gamma correction compensation, characterized in that, It includes: A compensation coefficient calculation module configured to calculate a compensation coefficient based on a grayscale image; And A compensation module configured to compensate the positioning map based on the compensation coefficient.

39. The optimization system according to claim 38, wherein The compensation coefficient calculation module is configured to perform the following actions: Perform an inverse gamma processing on the image to obtain an inverse gamma image; and Determine the compensation coefficient according to the grayscale mean value of the inverse gamma image.

40. The optimization system according to claim 38, wherein It further includes: A first filtering module communicatively connected to the compensation coefficient calculation module and configured to perform low-pass filtering on the grayscale image to remove high-frequency noise in the grayscale image.

41. The optimization system according to claim 38, wherein It further includes: An imaging device configured to acquire a picture of a display panel that is displaying a grayscale picture; And An image extraction module, which is communicably connected to the imaging device and is configured to extract the grayscale screen displayed on the display panel from the picture obtained by the imaging device.

42. The optimization system according to claim 41, wherein It further includes: A resolution adjustment module, which is communicably connected to the image extraction module and is configured to adjust the resolution of the image extracted by the image extraction module.

43. The optimization system according to claim 42, characterized in that, The image extraction module includes: An image segmentation sub-module, which is communicably connected to the imaging device and is configured to segment the picture obtained from the imaging device to obtain at least one connected region; and A shape fitting sub-module, which is communicably connected to the image segmentation sub-module and is configured to perform shape fitting on the connected region to adjust the shape of the connected region with the largest area to a shape consistent with the display area of the display panel.

44. The optimization system according to claim 41, characterized in that It further includes: A second filtering module, which is communicably connected to the imaging device and the image extraction module and is configured to perform median filtering on the picture obtained by the imaging device and then send it to the image extraction module.

45. A gamma correction compensation method, characterized in that, It includes: Adopting the optimization method according to any one of claims 1 to 37 to perform compensation optimization on the positioning map; Displaying the optimized positioning map through the display panel; And Based on the optimized positioning map, performing gamma correction on the display panel.

46. A gamma correction compensation system, characterized in that, It includes: The optimization system according to any one of claims 38 to 44, which is configured to generate an optimized positioning map; And A gamma correction module, which is communicably connected to the optimization system and is configured to perform gamma correction on the display panel based on the optimized positioning map.

Images