A display method and system based on adaptive block compensation

Abstract

The present application relates to the technical field of image processing, and more particularly to a display method and system based on adaptive block compensation, the display method comprising the following steps: based on local brightness difference analysis and local gray scale difference analysis, extracting a Mura area of image data; based on the Mura area, making an adaptive dynamic block decision to generate a block network; obtaining position information of the block network and calculating a Mura compensation value of the block network; according to the position information and the Mura compensation value, generating a register configuration; and according to the register configuration, making real-time driving compensation. The present application extracts the Mura area of image data based on local brightness difference analysis and local gray scale difference analysis, which can avoid single threshold deviation and automatically adjust sensitivity according to local characteristics; and then according to the actual Mura distribution characteristics of the current panel, the block network is dynamically and adaptively generated, which effectively overcomes the defect that the brightness difference of pixels within a block cannot be finely processed.

Description

Technical Field

[0001] This invention relates to the field of image processing technology, and in particular to a display method and system based on adaptive block compensation. Background Technology

[0002] With the continuous development of display technology, the application range of display panels is becoming increasingly wide. In particular, the display quality of display panels remains one of the important indicators for consumers and panel manufacturers to measure the quality of display panels. However, existing display panels suffer from poor display uniformity, meaning that when displaying the same image on the same monitor panel, different pixel areas will have different brightness, causing various artifacts, generally referred to as mura. Therefore, it is necessary to use the Demura method to extract the brightness of the display panel, obtain panel mura information, and then use algorithms to perform external compensation for mura, reducing or eliminating it.

[0003] The commonly used Demura method divides the effective display area of ​​a display panel into rectangular blocks of fixed size and position, measures the actual display brightness of each block, and obtains compensation data for each area. This compensation data is stored in external storage, and during the display phase, the monitor uses the compensation data for pixel compensation. Given that the Mura distribution characteristics of different individual panels vary, using a fixed partitioning method where all pixels within a block use the exact same compensation value cannot dynamically adjust based on position, size, or severity, and cannot finely handle pixel-level brightness differences within a block. Summary of the Invention

[0004] To address the problem that existing display technologies cannot precisely handle pixel-level brightness differences within blocks, this invention provides a display method based on adaptive block compensation. This method can dynamically adjust the row and column values ​​of blocks. While keeping the original storage space unchanged, the density of blocks is adjusted according to node positions, achieving flexible compensation from the pixel level to the region level. This solves the problem that existing display technologies cannot precisely handle pixel-level brightness differences within blocks.

[0005] The technical solution adopted by this invention to solve its technical problem is: A display method based on adaptive block compensation includes the following steps: S1: Obtain image data from the display panel; S2: Extract the Mura region of the image data based on local brightness difference analysis and local grayscale difference analysis; S3: Based on the Mura region, perform adaptive dynamic block segmentation decision-making to generate a block network; S4: Obtain the location information of the segmented network and calculate the Mura compensation value of the segmented network; S5: Generate register configuration based on the location information and the Mura compensation value; S6: Perform real-time drive compensation according to the register configuration.

[0006] Optionally, acquiring image data from the display panel includes: acquiring display information from the display panel and collecting initial brightness data from a reference image.

[0007] Optionally, based on local brightness difference analysis and local grayscale difference analysis, the Mura region of the image data is extracted as follows: S21: Generate a brightness difference matrix based on the local brightness mean of the local region image and the overall image data; A gray level difference matrix is ​​generated based on the gray level data of the local region image and the target gray level data; S22: Determine whether the absolute value of the brightness difference matrix is ​​greater than the brightness threshold and the absolute value of the gray level difference matrix is ​​greater than the gray level threshold. If so, proceed to step S24; otherwise, proceed to step S23. S23: The local area does not belong to the Mura region; S24: The local area belongs to the Mura region.

[0008] Optionally, the adaptive dynamic partitioning decision based on the Mura region includes: selecting a partitioning mode according to the type of the Mura region.

[0009] Optionally, selecting the segmentation mode based on the type of the Mura region includes: if the Mura region is dot-shaped, then a pixel-level segmentation mode is adopted.

[0010] Optionally, selecting the segmentation mode based on the type of the Mura region includes: if the Mura region is linear, then an adaptive strip segmentation mode is adopted.

[0011] Optionally, selecting the segmentation mode based on the type of the Mura region includes: if the Mura region is a gradient Mura, then a fixed segmentation mode is adopted.

[0012] Another object of the present invention is to provide a display system based on adaptive block compensation, which employs the display method based on adaptive block compensation as described above.

[0013] The beneficial effects of this invention are: The display method based on adaptive block compensation provided by this invention extracts the Mura region of image data based on local brightness difference analysis and local grayscale difference analysis. This avoids single threshold deviation and enhances dynamic adaptability by automatically adjusting sensitivity according to local characteristics. Furthermore, based on the actual Mura distribution characteristics of the current panel, a block network is dynamically and adaptively generated, effectively overcoming the defect that all pixels in the region use the exact same compensation value, which makes it impossible to finely process pixel-level brightness differences within the block. Attached Figure Description

[0014] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0015] Figure 1 This is a flowchart illustrating the adaptive block compensation display method of the present invention. Figure 2 This is a schematic diagram illustrating the Mura region extraction of the Demura table according to the present invention; Figure 3 This is a comparative diagram of the adaptive pixel compensation method in this invention and the traditional pixel compensation method; Figure 4 This is a schematic diagram of the selective compensation method in this invention. Detailed Implementation

[0016] The present invention will now be described in further detail. The embodiments described below are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0017] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0018] Currently, a fixed partitioning method is used, where all pixels within a block use the exact same compensation value. This method cannot dynamically adjust based on location, size, or severity, and it cannot adapt to the compensation needs of different mura patterns (dot-like, line-like, and area-like), nor can it finely handle pixel-level brightness differences within a block. This reduces the scale of compensation to some extent; severely mura areas require smaller partitions for accurate compensation. Furthermore, using the same partitioning precision for simple areas (such as uniform central areas) and complex areas (such as edge mura) leads to an unreasonable allocation of storage and computing resources. Therefore, how to scientifically segment the display area and adapt to individual panel differences and non-uniform mura distribution through dynamically variable size and position, while reducing storage space, is a pressing issue that needs to be addressed in current display applications using demura technology.

[0019] To address the limitation of existing display technologies in precisely handling pixel-level brightness differences within a block, this invention provides a display method based on adaptive block compensation. (See also...) Figure 1 As shown, the display method includes the following steps: S1: Obtain image data from the display panel; S2: Extract the Mura region of the image data based on local brightness difference analysis and local grayscale difference analysis; In this step, the range of the Mura region is extracted and defined based on the acquired image data, and the spatial distribution characteristics of the Mura are quantified. The brightness difference can be used to reflect local non-uniformity, and the gray level difference can be used to calculate the absolute deviation between the pixel gray level and the target value to assess the degree of color cast. This approach is also related to the human eye's sensitivity to gray levels. Finally, a visualization output is achieved, which can intuitively show the distribution and severity of the Mura. The joint judgment based on brightness difference and gray level difference can avoid single threshold bias and enhance dynamic adaptability, automatically adjusting the sensitivity according to local characteristics. S3: Based on the Mura region, adaptive dynamic block segmentation decision is made to generate a block network; In this step, the block size is selected and a block network is generated; in this step, dynamic block division is made for the extracted Mura regions. Specifically, in this invention, dynamic block division is a block division with dynamically adjustable row and column values. The number of rows and columns of the block can be configured independently, and resources can be dynamically allocated according to the severity of the Mura. S4: Obtain the location information of the segmented network and calculate the Mura compensation value of the segmented network; Obtain the location information and Mura compensation value of the segmented network, calculate and store them; S5: Generate register configuration based on location information and Mura compensation value; Based on the results of region extraction, determine the values ​​of each sampling point in the demura table and generate the register configuration that satisfies the region. S6: Perform real-time drive compensation based on register configuration; The display driver provides real-time compensation, applying compensation values ​​to adjust the input pixel data.

[0020] The display method based on adaptive block compensation provided by this invention extracts the Mura region of image data based on local brightness difference analysis and local grayscale difference analysis. This avoids single threshold deviation and enhances dynamic adaptability by automatically adjusting sensitivity according to local characteristics. Furthermore, based on the actual Mura distribution characteristics of the current panel, a block network is dynamically and adaptively generated, effectively overcoming the defect that all pixels in the region use the exact same compensation value, which makes it impossible to finely process pixel-level brightness differences within the block.

[0021] Specifically, acquiring image data of a display panel in the present invention includes: acquiring display information of the display panel, and collecting initial luminance data of a reference image.

[0022] In the present invention, the display panel is preferably a liquid crystal panel; in step S1, image information of the display panel is first acquired, and the initial luminance of the reference image displayed by the display screen at a target gray level collected by a luminance acquisition device is acquired. The luminance acquisition device may be a high-precision camera. Through the operation of the high-precision camera, a uniform light source and a console, a specific test pattern of the display panel can be displayed, the initial luminance data of the reference image can be collected, and multi-gray level sampling can be realized.

[0023] Next, in step S2, the defined Mura region is extracted from the acquired image data for feature analysis; specifically, in the present invention, based on local luminance difference analysis and local gray level difference analysis, extracting the Mura region of the image data includes: S21: generating a luminance difference matrix according to the local luminance mean of the local region image and the overall image data; generating a gray level difference matrix according to the gray level data of the local region image and the target gray level data; The local luminance difference mainly identifies potential Mura regions through the deviation degree of local luminance. A local mean value is calculated for the collected local original pixel data, which is generally calculated by a sliding window. In the present invention, the window size is preferably set to 15x15, and the local mean value is compared with the overall image data to generate a luminance difference matrix ΔL; The gray level difference mainly combines the visual characteristics of the human eye, and adopts differentiated thresholds under different gray levels; the target gray level target_gray is set, and the collected image data is divided into three gray level ranges. The first gray level range is that the gray level value G ≤ 50, the second gray level range is that the gray level value 50 < G ≤ 150, and the third gray level range is that the gray level value 150 < G ≤ 255; the gray level threshold gray_threshold corresponding to different gray level ranges is set according to the gray level sensitivity. Specifically, according to the principle of high low-gray level sensitivity, the gray level threshold of the first gray level range is set to 4; according to the principle of moderate medium-gray level sensitivity, the gray level threshold of the second gray level range is set to 6; according to the principle of low high-gray level sensitivity, the gray level threshold of the third gray level range is set to 8; the collected image gray level data is compared with the target gray level value to generate a gray level difference matrix ΔG; S22: comparing the absolute value of the luminance difference matrix with the luminance threshold, and simultaneously comparing the gray level difference matrix with the gray level threshold, determining whether the absolute value of the luminance difference matrix is greater than the luminance threshold and the absolute value of the gray level difference matrix is greater than the gray level threshold, if yes, proceeding to step S24, otherwise proceeding to step S23; The brightness difference matrix is ​​compared with a set brightness threshold. When the absolute value of the brightness difference matrix data being compared is greater than the preset brightness threshold, it is determined that the sub-pixel data unit corresponding to the matrix data being compared is contained within the Mura region. When the absolute value of the brightness difference matrix data being compared is less than or equal to the preset brightness threshold, it is determined that the sub-pixel data unit corresponding to the matrix data being compared is not contained within the Mura region. The above method can be used to initially filter the Mura region. The preset brightness threshold can be determined based on the display effect of the display panel, and a dynamic threshold can be set accordingly. For example, the mean value local_avg of the originally acquired local image is calculated, and a brightness threshold L_threshold is set. When |img-local_avg|>L_threshold, it is judged as a Mura region, and the Mura region is finally extracted. In this invention, the brightness threshold is preferably set to 0.5 nit (the lower limit of human eye visibility), and the threshold can be dynamically adjusted according to the local brightness fluctuation of ±5%. At the same time, the gray level difference matrix is ​​compared with the gray level threshold within the set corresponding range. When the gray level difference matrix data being compared is greater than the preset gray level threshold, it is determined that the sub-pixel data unit corresponding to the matrix data being compared is included in the Mura region. When the gray level difference matrix data being compared is less than or equal to the preset gray level threshold, it is determined that the sub-pixel data unit corresponding to the matrix data being compared is not included in the Mura region. For example, a grayscale threshold `gray_threshold` is set, and the grayscale data of the originally acquired image is calculated to determine the grayscale range `G`, thereby determining the selection of the corresponding grayscale threshold. When `|img-target_gray| ≥ gray_threshold`, it is determined to be a Mura region, and the Mura region is finally extracted. In this invention, the grayscale threshold can be determined according to the display effect of the display panel, and a dynamic threshold can be set accordingly. See also... Figure 2 As shown in the attached figure, this is a schematic diagram of Mura region extraction from a Demura table according to an embodiment of the present invention. In step S2, the spatial distribution characteristics of the Mura are also quantified. The above method can be used to merge and extract relevant Mura regions. The brightness difference can reflect local non-uniformity, and the gray level difference can be used to calculate the absolute deviation between the pixel gray level and the target value to evaluate the degree of color cast. This approach is also related to the sensitivity of the human eye to gray levels. Finally, a visual output is achieved, which can intuitively show the distribution and severity of Mura. Based on the joint judgment of brightness difference and gray level difference, single threshold deviation can be avoided, and dynamic adaptability can be enhanced by automatically adjusting the sensitivity according to local characteristics. It should be noted that the above Mura extraction and analysis method is only a preferred embodiment of the present invention and does not limit the implementation and protection scope of the present invention. It should be noted that in this judgment step, the absolute value of the brightness difference matrix must be greater than the brightness threshold, and the local area where the absolute value of the gray level difference matrix is ​​greater than the gray level threshold must belong to the Mura region. S23: The local area does not belong to the Mura region; S24: The local area belongs to the Mura region.

[0024] The present invention preferably uses adaptive dynamic segmentation decision based on the Mura region, which includes: selecting a segmentation mode according to the type of the Mura region; if the Mura region is dot-shaped, then a pixel-level segmentation mode is adopted; if the Mura region is line-shaped, then an adaptive strip segmentation mode is adopted; if the Mura region is gradient Mura, then a fixed segmentation mode is adopted.

[0025] Specifically, this invention mainly focuses on dynamically dividing region-shaped muras into rectangular blocks. Other common muras types include: point muras, line muras, gradient muras, and arbitrary muras. This invention also relates to a block division strategy selector that selects the block division mode based on the muras type. Specifically, although the above-mentioned method of dynamically adjusting the row and column values ​​of the blocks can balance accuracy and storage efficiency, it cannot accurately compensate for the boundary parts. Therefore, when this method cannot meet the requirements for accurate muras compensation, a new decision will be made on whether to adopt other partitioning strategies for muras compensation.

[0026] This invention proposes three additional optional solutions for other common Mura types. For example, for optional point-shaped Mura, when the input Mura region area is <5 pixels, pixel-level segmentation is used, i.e., Value=1. The row and column configuration is set to 32 in the area surrounding the defective pixel, and the segmentation parameters are output. This method can accurately compensate for individual defective pixels. For optional linear Mura, linear features need to be identified. When the aspect ratio of the input Mura region is >5:1, it indicates that it extends along a specific direction. Adaptive strip-shaped segmentation is used, with Value 1 along the Mura direction, row configuration 1 along the defect direction, and column configuration 32, and the segmentation parameters are output. This method can match linear structures and reduce invalid segments. For optional gradient Mura, mainly for large-area brightness gradients without sharp boundaries, this solution uses fixed segmentation. That is, for the size of the gradient Mura range, fixed segmentation row and column values ​​of 8, 16, and 32 are used, and the segmentation parameters are output. This method can effectively improve computational efficiency. For Option 4, the arbitrary Mura, the block selection method can be flexibly adjusted according to the above block decision scheme. The region will then be divided, i.e., the block size will be selected to generate a block network. The block size is dynamically adjusted as a power of 2, and can be specifically set according to compensation requirements, ranging from large to small. In this invention, the preferred block size spacing coefficient is set to 0~5, corresponding to different spacing combinations of 1 / 2 / 4 / 8 / 16 / 32. Different spacing combinations include the generated block size identifier.

[0027] The partitioning method is determined based on Mura characteristics. For example, the brightness change is gradual within a region, but there are obvious brightness jumps at the boundaries. Figure 3 As shown, the original compensation required 25 pixels, but now only 16 pixels are needed (the nodes are dynamically adjusted based on the mura characteristics). Specifically, based on mura feature analysis, software can be used to generate the optimal LUT table, and smaller, more efficient, or more accurate LUT compensation can achieve the same effect. Based on the above adaptive matrix partitioning scheme, another embodiment is proposed: a flexible and selective compensation method. This method mainly relies on the mura regions extracted from the display panel. For example, if only a portion of the mura area is involved in the display screen, such as... Figure 4As shown, based on this, the optional scheme described in step 3 above can be directly applied to the relevant Mura-related areas to perform feature analysis on the Mura areas. No compensation is applied to other normal image parts to maintain the color accuracy of the original image and avoid over-compensation of normal areas leading to new defects. Based on this flexible selective compensation method, there are the following advantages: Advantage 1: It can optimize resources, processing only the Mura areas and avoiding redundant calculations on normal areas; it can reduce the computational load by at least 50%, which varies depending on the Mura coverage. Advantage 2: It can improve storage efficiency, storing only the compensation parameters for abnormal areas. Advantage 3: It can enhance real-time performance; the driver IC only needs to apply the compensation algorithm to the Mura coverage area. Specifically, as described in this scheme, the color accuracy of non-Mura areas can be improved, and the final display output is determined by both local compensation and direct pass-through of original information.

[0028] Accordingly, based on the block decision scheme, the block size is selected. Next, as described in step S4, the position information of the block rectangular regions can be determined according to the block division. For each generated adaptive block, the deviation of the brightness of each region relative to the target value is calculated, and the Mura compensation value of the corresponding rectangular region can be obtained. The compensation value of each block is stored. The compensation accuracy is determined by the number of partitions; the more partitions, the higher the accuracy. As described above, as in step S5, the values ​​of each sampling point in the demura LUT table can be determined based on the region extraction results. To adapt to the corresponding sampling point values, a register configuration that satisfies the region's corresponding value is generated.

[0029] Finally, as described in step 6, real-time drive compensation is performed. For each pixel on the screen, block location is performed, and the corresponding rectangular block and adjacent blocks are quickly found based on the pixel coordinates. The compensation value of the corresponding block is obtained, and then the final compensation value is calculated using interpolation. The calculated final compensation value is then used to adjust the input pixel data.

[0030] This invention provides a method for dynamically adjusting the row and column values ​​of blocks. While keeping the original storage space constant (but not limited to this), the density of blocks is adjusted according to node position, achieving flexible compensation from the pixel level to the region level. Block parameters are optimally selected based on characteristics such as Mura brightness and local grayscale. Small blocks are used in areas with severe Mura (high gradient), while large blocks are used in uniform areas, maximizing resource utilization. It is also compatible with multiple scenarios, dynamically switching the block strategy for the same panel at different usage stages (early aging / stable period).

[0031] The adaptive matrix partitioning method proposed in this invention dynamically and adaptively generates rectangular blocks of variable size and position based on the actual Mura distribution characteristics of the current panel, including position, size, and severity. Smaller partitions are used in areas with severe Mura, effectively overcoming the limitation of using the exact same compensation value for all pixels within a block, which fails to finely process pixel-level brightness differences. This invention achieves adaptive block size, automatically using smaller rectangular areas for fine compensation in areas with severe and rapidly changing Mura, and larger rectangular blocks for relatively coarse and uniform compensation in areas with mild and gently changing Mura. Simultaneously, this invention also achieves adaptive block position; the block boundaries are no longer fixed to grid lines but can float according to the Mura shape, ensuring that a block covers areas with relatively uniform Mura features as much as possible. This invention can achieve compensation at any point, solving to some extent the shortcomings of traditional fixed partitioning. Finally, the optional scheme described in this invention effectively adapts to the principle of on-demand resource allocation, significantly optimizing storage space by prioritizing allocation to the most critical severe Mura areas requiring fine compensation within limited storage and computational resources. While ensuring the overall compensation effect, the total number of blocks required is significantly reduced. Furthermore, the adaptive block partitioning proposed in this invention maximizes resource utilization to compensate for the unique Mura defect of the panel, specifying different partitioning schemes for different panels, thereby improving yield and display consistency.

[0032] Another object of the present invention is to provide a display system based on adaptive block compensation, which employs the display method based on adaptive block compensation as described above.

[0033] The display system based on adaptive block compensation provided by this invention employs an adaptive block compensation-based display method. It extracts the Mura region of image data based on local brightness difference analysis and local grayscale difference analysis, which can avoid single threshold deviation and enhance dynamic adaptability by automatically adjusting sensitivity according to local characteristics. Furthermore, based on the actual Mura distribution characteristics of the current panel, it dynamically and adaptively generates a block network, effectively overcoming the defect that all pixels in the region use the same compensation value, which cannot finely process the pixel-level brightness differences within the block.

[0034] Based on the above-described preferred embodiments of the present invention, and through the foregoing description, those skilled in the art can make various changes and modifications without departing from the inventive concept. The technical scope of this invention is not limited to the contents of the specification, but must be determined according to the scope of the claims.

Claims

1. A display method based on adaptive block compensation, characterized in that, Includes the following steps: S1: Obtain image data from the display panel; S2: Extract the Mura region of the image data based on local brightness difference analysis and local grayscale difference analysis; S3: Based on the Mura region, perform adaptive dynamic block segmentation decision-making to generate a block network; S4: Obtain the location information of the segmented network and calculate the Mura compensation value of the segmented network; S5: Generate register configuration based on the location information and the Mura compensation value; S6: Perform real-time drive compensation according to the register configuration; Based on local brightness difference analysis and local grayscale difference analysis, the Mura region of the image data is extracted as follows: S21: Generate a brightness difference matrix based on the local brightness average of the local region image and the brightness value of each pixel in the overall image; generate a gray level difference matrix based on the gray level value of each pixel in the local region image and the target gray level data. S22: Compare the absolute value of the brightness difference matrix with the brightness threshold, and at the same time, compare the gray level difference matrix with the gray level threshold. When the absolute value of the brightness difference matrix data being compared is greater than the preset brightness threshold, and when the gray level difference matrix data being compared is greater than the preset gray level threshold, it is determined that the sub-pixel data unit corresponding to the matrix data being compared is contained within the Mura region; otherwise, it is determined that the sub-pixel data unit corresponding to the matrix data being compared is not contained within the Mura region.

2. The display method based on adaptive block compensation as described in claim 1, characterized in that, Acquiring image data from the display panel includes: acquiring display information from the display panel and collecting initial brightness data from a reference image.

3. The display method based on adaptive block compensation as described in claim 1 or 2, characterized in that, Adaptive dynamic partitioning decisions based on the Mura region include: selecting a partitioning mode according to the type of the Mura region.

4. The display method based on adaptive block compensation as described in claim 3, characterized in that, Selecting a segmentation mode based on the type of the Mura region includes: if the Mura region is dot-shaped, then a pixel-level segmentation mode is used.

5. The display method based on adaptive block compensation as described in claim 3, characterized in that, Selecting a segmentation mode based on the type of the Mura region includes: if the Mura region is linear, then an adaptive strip segmentation mode is used.

6. The display method based on adaptive block compensation as described in claim 3, characterized in that, Selecting a partitioning mode based on the type of the Mura region includes: if the Mura region is a gradient Mura, then a fixed partitioning mode is used.

7. A display system based on adaptive block compensation, characterized in that, The display method based on adaptive block compensation as described in any one of claims 1-6 is adopted.

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