A display screen light and dark line adjustment optimization method and system and LED display screen

Abstract

The application discloses a display screen light and dark line adjustment optimization method and system and an LED display screen, belongs to the technical field of display, obtains the luminance response data of a target display screen under multi-gray scale input, and constructs an initial response distribution map of light and dark lines; target gray stages are identified based on luminance fluctuation characteristics, and a feature set is constructed by extracting luminance deviation slope characteristics; a local response model of light and dark lines is constructed based on the feature set; a driving adjustment vector is generated by combining the model, luminance expected values and current adjustment ranges; the adjustment vector is applied to the display screen, and luminance response is reacquired after adjustment, and an adjusted response matrix is constructed; the luminance difference before and after adjustment is compared, if the preset tolerance is not reached, the model is updated and adjustment is repeated until the luminance consistency requirement is met; finally, an adjustment scheme is output and used for batch optimization; the application realizes high-precision identification and iterative adjustment of light and dark line defects, improves display uniformity, and has good industrial application value.

Description

Technical Field

[0001] This invention relates to the field of display technology, specifically to a method, system, and LED display screen for adjusting and optimizing bright and dark lines. Background Technology

[0002] As electronic devices evolve towards higher resolution, narrower bezels, and greater flexibility, display manufacturing processes are becoming increasingly complex. This is especially true in emerging display technologies such as OLED, Mini-LED, and Micro-LED, where controlling the consistency of driving circuit layout and inter-pixel electro-optical response is becoming increasingly difficult. Due to a combination of factors, including pixel driving nonlinearity, aging of luminescent materials, and insufficient compensation algorithms, displays are highly susceptible to mura defects in grayscale display mode. These mura defects are faint, unevenly bright, but perceptible linear stripes within a column or row of pixels. While not functional malfunctions, these defects severely impact the user's visual experience, particularly in low-brightness environments, becoming a critical quality bottleneck in the mass production of high-end display products.

[0003] Current methods for adjusting brightness and darkness mainly rely on single-point brightness mean correction or global pixel compensation. However, due to the lack of in-depth modeling of column / row response characteristics and actual driving parameters, the adjustment effect is unstable and may even introduce new image artifacts. In addition, existing methods generally ignore the nonlinear characteristics of brightness and darkness response in different grayscale regions, making it impossible to achieve fine-grained regional adjustment. Summary of the Invention

[0004] The purpose of this invention is to provide a method, system, and LED display screen for adjusting and optimizing bright and dark lines in a display screen, in order to address the shortcomings in the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for optimizing the adjustment of bright and dark lines on a display screen, comprising:

[0006] Obtain the pixel column / row brightness response data matrix of the target display screen under multi-grayscale input, and construct the initial response distribution map M0 of the bright and dark lines;

[0007] Based on M0, the grayscale range is partitioned to identify the target grayscale stage with high response fluctuation, and the target region feature set P is constructed according to the brightness deviation slope.

[0008] Based on the position coordinates of the bright and dark lines and their grayscale response curves contained in the feature set P, a local response model F for the bright and dark lines is constructed.

[0009] Based on the response model F, and combined with the preset brightness expectation value and the driving current adjustment range, the driving parameter adjustment vector D is generated;

[0010] The adjustment vector D is applied to the target display screen to obtain the adjusted brightness response matrix M1.

[0011] Calculate the difference in response before and after adjustment ΔM, and determine whether ΔM is lower than the preset tolerance threshold T. If not, update model F until ΔM≤T.

[0012] The final driving parameter adjustment scheme is output and used for batch bright and dark line defect optimization operations.

[0013] Preferably, the step of acquiring the pixel column / row brightness response data matrix of the target display screen under multi-grayscale input and constructing the initial response distribution map M0 of the bright and dark lines includes:

[0014] Multiple preset grayscale voltage values ​​are sequentially input into the target display screen to cover the entire working brightness range;

[0015] Under each gray level voltage input, pixel brightness values ​​are collected column by column or row by row to form a gray level response data sequence.

[0016] After normalizing the data sequence, a gray-scale-brightness two-dimensional response matrix is ​​constructed, organized by pixel columns or rows, and the brightness deviation curve is extracted.

[0017] Discrete Fourier analysis is performed on each column or row based on the brightness deviation curve to extract the periodic brightness and darkness fluctuation characteristics and generate the corresponding initial response distribution map M0 of the brightness and darkness lines.

[0018] Preferably, the step of partitioning the grayscale range based on M0, identifying target grayscale stages with high response volatility, and constructing a target region feature set P based on the brightness deviation slope includes:

[0019] The brightness fluctuation amplitude of each gray level in the initial response distribution map M0 of the bright and dark lines is sorted according to the gray level, and the gray level range is divided into multiple gray level sub-segments based on the sorting results using a fixed threshold division method.

[0020] The variance of the brightness fluctuation amplitude in each gray level sub-segment is calculated, gray level sub-segments with variance values ​​greater than the preset fluctuation variance threshold are identified, and gray level sub-segments are marked as target gray levels with high response volatility.

[0021] In the target gray stage, the slope of brightness change between adjacent gray levels is calculated point by point for the brightness deviation curve corresponding to the initial response distribution map M0 of the bright and dark lines, and a slope sequence is obtained to describe the trend of brightness change.

[0022] The grayscale positions in the slope sequence where the absolute value of the slope is greater than the preset slope threshold and their corresponding brightness deviation values ​​are extracted and combined in order of grayscale position to form the target region feature set P, which is used to characterize the feature region of abrupt changes in bright and dark line response in the target gray stage.

[0023] Preferably, the step of constructing a local response model F for the bright and dark lines based on the position coordinates of the bright and dark lines and their grayscale response curves contained in the feature set P includes:

[0024] Extract the grayscale response curve corresponding to each bright and dark line position in the feature set P, and obtain the brightness deviation data of the corresponding position under multiple grayscale inputs.

[0025] Curve fitting is performed on the grayscale response curve, and a continuous response function is established using a cubic spline interpolation algorithm to construct a fitting model that reflects the relationship between grayscale input and brightness deviation.

[0026] By jointly modeling the brightness deviation function with column or row coordinates, a bivariate local response function of grayscale and spatial location is formed.

[0027] The local response function is defined as the local response model F for bright and dark lines.

[0028] Preferably, the step of generating a drive parameter adjustment vector D based on the response model F, combined with a preset brightness expectation value and a drive current adjustment range, includes:

[0029] Set up a brightness expectation matrix E corresponding to each target gray level, where each element represents the target brightness output at the set gray level and position;

[0030] Based on the local response model F of the bright and dark lines, the predicted brightness output matrix P under the current driving parameter conditions is calculated, and it is compared point by point with the brightness expectation matrix E to obtain the brightness deviation matrix ΔL.

[0031] Gradient mapping is performed on the brightness deviation matrix ΔL, and the partial derivative information of the gray level and current relationship in the response model F is combined to construct the driving current adjustment function.

[0032] The adjustment function is constrained and optimized within the preset driving current adjustment range. The optimal adjustment vector D is solved by linear least squares method so that the predicted brightness approaches the expected brightness value.

[0033] Preferably, applying the adjustment vector D to the target display screen to obtain the adjusted brightness response matrix M1 includes:

[0034] Based on the current adjustment value in the adjustment vector D, the driving current of the pixel column or pixel row corresponding to the bright and dark line areas is adjusted, and the adjustment value is written into the display control register.

[0035] Multiple preset grayscale voltage values ​​are sequentially re-input to the target display screen, and the display output is driven by the adjustment current;

[0036] Under each grayscale input, the display area is sampled column by column or row by row to obtain the adjusted brightness response data;

[0037] The collected brightness response data is matrixed according to gray level and spatial location to construct the adjusted brightness response matrix M1.

[0038] Preferably, the step of calculating the difference in response ΔM before and after adjustment, and determining whether ΔM is lower than a preset tolerance threshold T, if not, then updating model F until ΔM ≤ T, includes:

[0039] Subtract the brightness response matrix M0 before adjustment from the brightness response matrix M1 after adjustment element by element at corresponding positions to obtain the response difference matrix ΔM, which is used to characterize the degree of brightness change.

[0040] The response difference matrix ΔM is normalized, and its maximum or average value is calculated as an indicator of adjustment deviation.

[0041] The adjustment deviation index is compared with the preset tolerance threshold T. If the maximum value or average value is greater than T, it is determined that the current adjustment has not met the brightness uniformity requirement.

[0042] In the event of adjustment failure, the luminance deviation data is re-extracted based on the latest luminance response matrix M1, and the local response model F is updated until ΔM is less than or equal to the tolerance threshold T.

[0043] The present invention also provides a display screen brightness and darkness line adjustment and optimization system, comprising:

[0044] The data acquisition module acquires the pixel column / row brightness response data matrix of the target display screen under multi-grayscale input and constructs the initial response distribution map M0 of bright and dark lines.

[0045] The grayscale fluctuation recognition module partitions the grayscale range based on M0, identifies the target grayscale stage with high response fluctuation, and constructs the target region feature set P based on the brightness deviation slope.

[0046] The response modeling module constructs a local response model F for bright and dark lines based on the position coordinates of the bright and dark lines and their grayscale response curves contained in the feature set P.

[0047] The drive adjustment generation module generates a drive parameter adjustment vector D based on the response model F and the preset brightness expectation value and drive current adjustment range.

[0048] The driving module applies the adjustment vector D to the target display screen to obtain the adjusted brightness response matrix M1.

[0049] The model update module calculates the difference in response before and after adjustment ΔM, and determines whether ΔM is lower than the preset tolerance threshold T. If not, the model F is updated until ΔM≤T.

[0050] The adjustment scheme output module outputs the final driving parameter adjustment scheme and is used for batch bright and dark line defect optimization operations.

[0051] The present invention also provides an LED display screen for implementing the aforementioned method for adjusting and optimizing the brightness and darkness of a display screen.

[0052] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0053] 1. This invention achieves high-precision localization and response behavior characterization of bright and dark line defect areas by constructing a bright and dark line identification and modeling mechanism based on grayscale response data. Compared with traditional methods that rely on manually setting compensation parameters or simple averaging correction, this invention introduces multi-grayscale distribution analysis, brightness deviation slope feature extraction, and bivariate response model construction, enabling precise quantification of the variation characteristics of bright and dark lines in spatial and grayscale dimensions. This provides a stable mathematical foundation for subsequent driving adjustment and effectively improves the adaptability of the adjustment algorithm.

[0054] 2. This invention generates an adjustment vector by comparing it with the desired brightness and combines iterative optimization with an error feedback mechanism. This allows the invention to quickly converge to the optimal adjustment result within the driving current adjustment range, ensuring brightness uniformity is within visually acceptable limits. The final output adjustment scheme is portable and batch-adaptable, significantly improving the brightness consistency of high-resolution display panels at low and medium grayscale levels and effectively reducing product scrap rates caused by bright and dark line defects. Attached Figure Description

[0055] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0056] Figure 1 This is a flowchart of the method of the present invention.

[0057] Figure 2 This is a flowchart of the system modules of the present invention. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0059] Example 1, please refer to Figure 1 As shown in this embodiment, a method for adjusting and optimizing the brightness and darkness of a display screen includes:

[0060] Obtain the pixel column / row brightness response data matrix of the target display screen under multi-grayscale input, and construct the initial response distribution map M0 of the bright and dark lines.

[0061] In this step, the preset grayscale voltage values ​​are multiple discrete voltage values ​​covering the working brightness range of the target display screen, with no fewer than 16 levels, preferably 64 levels or more of equally spaced grayscale distribution. By controlling the display control circuit, grayscale voltage values ​​V1, V2, ..., Vn are input to the display screen, where n represents the grayscale level, and the voltage value range is between the minimum and maximum brightness driving voltage supported by the display screen.

[0062] After each grayscale voltage value is input and the display stabilizes, a high-precision brightness detection device is used to measure the brightness output of each pixel column or row under constant ambient light conditions. The measurement results are recorded unit by pixel column or row, forming a brightness data set corresponding to the current grayscale. This measurement process is repeated until all n grayscale levels are input, ultimately forming a brightness response sequence for each column or row at n grayscale levels. This sequence is used to reflect whether there are differences in the consistency of pixel arrangement response under different input brightness levels.

[0063] To eliminate the interference caused by the absolute brightness difference between different gray levels to subsequent analysis, the gray-level brightness response sequence is normalized. Specifically, let the measured brightness value of the j-th column (or j-th row) at the i-th gray level be Lij. Then, the brightness values ​​in each column or row are normalized according to the following formula: subtract the mean of the brightness sequence of that column (or row) from Lij, and then divide by the standard deviation of the brightness of that column (or row) to obtain a standardized brightness response sequence with zero mean and unit variance. Organized by columns or rows, all standardized data are constructed into a gray-level-brightness two-dimensional response matrix W. The vertical axis of the matrix represents the gray level, and the horizontal axis represents the pixel column or pixel row number. Each element in the matrix corresponds to the normalized brightness deviation value. By vertically scanning the brightness deviation values ​​of each column or row in the W matrix, the brightness deviation curve corresponding to each column or row is extracted.

[0064] The aforementioned brightness deviation curves are input into the Fourier analysis module, and the Discrete Fourier Transform (DFT) method is used to perform frequency domain decomposition on each brightness deviation curve. If a brightness deviation curve is a one-dimensional sequence Wj of length n, it is converted into n complex frequency components using the DFT, and its dominant frequency and corresponding amplitude are extracted to identify the periodic fluctuation characteristics. If the brightness frequency domain amplitude of a certain column or row is significantly higher than the average amplitude of the entire column / row in the low-frequency region, it can be determined that the column or row has a periodic bright-dark line defect.

[0065] Finally, the Fourier transform main frequency amplitude values ​​of all columns or rows are spatially mapped according to the column (or row) position index to generate a two-dimensional grayscale image, defined as the initial response distribution map M0 of the bright and dark lines. The grayscale value in the image represents the brightness fluctuation amplitude of the corresponding column or row, and the higher the grayscale, the more severe the bright and dark defects.

[0066] Based on M0, the grayscale range is partitioned to identify the target grayscale stage with high response volatility, and the target region feature set P is constructed based on the brightness deviation slope.

[0067] First, extract the brightness fluctuation amplitude corresponding to each gray level from the initial response distribution map M0 of the bright and dark lines. The amplitude represents the average brightness deviation of all pixel columns or pixel rows under the input condition of that gray level. Let the total number of gray levels be N, and represent the gray level levels as G1 to Gn, and the corresponding brightness fluctuation amplitudes as V1 to Vn. Sort the gray levels in the order of G1 to Gn, and divide the entire gray level range into several gray level sub-segments of equal width. Each sub-segment contains no less than 4 gray level levels, preferably 8 gray level levels. The specific number of sub-segments is determined by dividing N by the width of the sub-segment.

[0068] Within each grayscale sub-segment, calculate the variance of all brightness fluctuation amplitudes. Specifically, suppose a segment contains k grayscale levels with brightness fluctuation amplitudes from V1′ to Vk′, then the corresponding fluctuation variance... The calculation method is as follows: average all V values, then square the difference between each V and the average, and finally calculate the average. Compare the calculated S² with the preset fluctuation variance threshold T1. If... If the value is greater than T1, the grayscale sub-segment is determined to be a region with drastic fluctuations in the bright and dark line response, and its corresponding grayscale range is marked as the target grayscale stage. The fluctuation variance threshold T1 is a fixed value set empirically based on experimental data, typically ranging from 0.03 to 0.08 normalized brightness units.

[0069] Within the identified target grayscale range, the mean brightness deviation of each grayscale level in that range is extracted, and the slope of the brightness change between adjacent grayscale levels is calculated. Specifically, assuming a target grayscale range contains m grayscale levels from Ga to Gb, the ratio of the brightness deviation difference ΔL between every two adjacent grayscale levels to the grayscale level spacing ΔG is calculated sequentially, and the resulting slope is... This forms a slope sequence K1 to K(m-1) of length m-1. This slope sequence is used to measure the degree of abrupt change in the brightness deviation trend during the grayscale transition process.

[0070] The absolute values ​​of all elements in the slope sequence are processed and compared with a preset slope abrupt change threshold T2. If the absolute value of a certain slope Ki is greater than T2, it is considered that there is a brightness response abrupt change at the corresponding gray level. The gray level Gi that meets this condition and its corresponding brightness deviation from the mean Li are extracted as feature points, recorded sequentially, and combined in order of gray level to form the final target area feature set P. The slope abrupt change threshold T2 is an empirical parameter set according to the brightness response characteristics of the display screen, preferably a normalized brightness slope unit ranging from 0.1 to 0.25.

[0071] Based on the position coordinates of the bright and dark lines and their grayscale response curves contained in the feature set P, a local response model F for the bright and dark lines is constructed.

[0072] First, for each grayscale position contained in the target region feature set P, based on its column or row coordinates, the brightness deviation value of that position under all grayscale inputs is retrieved from the initial response distribution map M0 of the bright and dark lines. Let the coordinates of a certain bright and dark line position be (x, y), and its brightness deviation values ​​under grayscales G1 to Gn be L1 to Ln respectively, forming a set of one-dimensional response data point pairs. This constitutes the grayscale response curve at that location. This data sequence is used for subsequent fitting processing.

[0073] To improve the continuity of the representation of brightness response trends, a function fitting is performed on the above grayscale response data points. A cubic spline interpolation algorithm is preferably used for fitting, that is, a cubic polynomial function is constructed between every two adjacent data points, ensuring that the entire fitted function has continuity of first and second derivatives at the data points. Let the i-th spline function be... This forms a set of spline functions across the entire grayscale range. The data are then stitched together to form a continuous fitted curve. The fitted function is used to represent the grayscale-brightness deviation response relationship at the location of the bright and dark line.

[0074] After obtaining the fitted function for each bright and dark line position, a location attribution modeling process is introduced to characterize the trend of response variation with spatial distribution. The column or row coordinate X is used as the spatial variable, forming a bivariate input space together with the grayscale value G. Difference analysis is performed on the fitted functions for multiple bright and dark line positions. A weighted fitting method is used to establish a bivariate function of the form F(G, X), where G is the grayscale input value, X is the pixel column or row position, and F(G, X) represents the brightness deviation from the predicted value at that spatial position under a specified grayscale. The weighting coefficients are set inversely based on the spline function fitting residuals; the smaller the error, the higher the weight, thereby enhancing the model's fitting accuracy in high-quality data regions.

[0075] The constructed bivariate function F(G, X) is used as the local response model F for bright and dark lines, serving as a mathematical expression model describing the brightness deviation trend of specific bright and dark lines under different grayscale input conditions. This model has continuity and differentiability, and can predict the brightness deviation of arbitrary grayscale values ​​and arbitrary pixel positions, serving as the objective function input and error control basis in the subsequent generation of driving adjustment parameters.

[0076] Based on the response model F, and combined with the preset brightness expectation value and the driving current adjustment range, the driving parameter adjustment vector D is generated.

[0077] First, a luminance expectation matrix E is defined for the identified target gray levels and the pixel columns or rows containing the bright and dark lines. Each row of this matrix corresponds to a gray level input, and each column corresponds to a spatial location (column or row number). Each element E(i, j) in the matrix represents the expected target luminance of the pixel in the j-th column or row at the i-th gray level. The luminance expectation value can be set based on the average luminance of the normal area or a reference ideal luminance model in the display panel, preferably represented by a normalized luminance value, with a value ranging from 0 to 1.

[0078] The previously constructed local response model F for bright and dark lines is invoked, using the current driving current parameter set I as input variables. The predicted brightness output value corresponding to each gray level and position is calculated sequentially, resulting in the predicted brightness output matrix P. This matrix maintains the same dimension as the brightness expectation matrix E, representing the actual brightness response of the bright and dark line areas on the display screen under the current driving conditions. Subsequently, matrix P is subtracted element-wise from E to obtain the brightness deviation matrix ΔL, where... This matrix is ​​used to quantify the brightness error of each location at a specific gray level.

[0079] To determine the impact of driving current variations on brightness output, first-order partial derivatives are calculated based on the response model F, taking the first derivative with respect to the joint response of each grayscale input and current input. Let F(G, I) be the joint response function of grayscale G and driving current I. Then, taking the partial derivative with respect to the current yields... F / I represents the sensitivity of brightness to the drive current. Based on each deviation value in ΔL and its corresponding response sensitivity, the ratio of these two values ​​is calculated to construct the drive current adjustment function. Function A represents the required adjustment range of the driving current at each position and gray level.

[0080] Using the aforementioned current adjustment function A as the initial reference for the drive current correction, a constraint interval [R1, Rh] is set for the drive current adjustment range, where R1 is the minimum allowable current adjustment and Rh is the maximum allowable current adjustment, typically set to fluctuate within ±10% of the current current. A linear least squares optimization algorithm is then used to construct the objective function. The algorithm solves for each element in the adjustment vector D, minimizing the squared error between the predicted brightness and the target brightness. Under the constraint of satisfying the current adjustment range, it outputs the optimal adjustment vector D, which is used to drive the control circuit to adjust the pixel column or row current in the bright and dark line regions.

[0081] The adjustment vector D is applied to the target display screen to obtain the adjusted brightness response matrix M1.

[0082] First, the current adjustment values ​​corresponding to each element in the adjustment vector D are extracted and mapped one-to-one with the pixel column or row where the bright and dark lines on the target display screen are located. Each current adjustment value is added to the original drive current value to obtain a new target drive current value, which is then written to the corresponding drive current control register via the display control interface. The writing operation is performed column-by-column or row-by-row to ensure that the adjusted current parameters only affect areas with bright and dark line responses, thereby avoiding interference with normal areas.

[0083] The previously set grayscale voltage values ​​are sequentially re-inputted to the display screen, covering the entire operating brightness range. At each grayscale input, the electro-optical response behavior of the display screen changes because the driving current of the pixel column or pixel row has been updated according to the adjustment vector D, thus affecting its output brightness. This step aims to observe the changes in brightness response under the influence of the adjustment current to verify whether the response in the bright and dark line areas is approaching the expected target.

[0084] After each grayscale input is stabilized, the same high-precision brightness detection device as before adjustment is used to sample the brightness of the display area column by column or row by row to obtain the actual brightness output data of each pixel column or pixel row at each grayscale level after adjustment. In order to maintain consistency with the brightness response matrix M0 before adjustment, the sampling process should maintain the same spatial resolution and ambient lighting conditions to ensure the comparability of the collected data.

[0085] Using the collected brightness data as row indices based on grayscale levels and column indices based on spatial location (pixel column or row number), a two-dimensional brightness response matrix M1 is constructed. Each element M1(i,j) in this matrix represents the actual brightness output of the pixel in the j-th column or row under the i-th grayscale input. The adjusted brightness response matrix M1 will serve as the basis for subsequent error analysis and adjustment effect evaluation, used for difference calculation and optimization feedback compared with the unadjusted response matrix M0.

[0086] Calculate the difference in response before and after adjustment, ΔM, and determine whether ΔM is lower than the preset tolerance threshold T. If not, update model F until ΔM ≤ T.

[0087] The difference between the unadjusted luminance response matrix M0 and the adjusted luminance response matrix M1 is calculated by performing a one-to-one comparison based on the same gray level and spatial position, forming the response difference matrix ΔM. Specifically, let the luminance values ​​of the j-th column or row pixel at the i-th gray level be M0(i,j) and M1(i,j) respectively before and after adjustment. Each element in the response difference matrix ΔM represents the magnitude of brightness change at the corresponding gray level and position, serving as a direct indicator of the adjustment effect.

[0088] To eliminate the impact of differences in brightness ranges across different gray levels on the adjustment evaluation results, the response difference matrix ΔM is normalized. The normalization method is as follows: each ΔM(i,j) value is divided by the expected target brightness value E(i,j) at the corresponding gray level to obtain the normalized difference matrix ΔM′, where... Based on the normalized result, the maximum value of all elements in the ΔM′ matrix is ​​extracted. or average , as an indicator of adjustment deviation. The above indicators are used to characterize the overall adjustment error level, among which... ΔM_avg represents the most severe response deviation, while ΔM_avg represents the overall balance error.

[0089] The calculated adjustment deviation index is compared with the tolerance threshold T. The tolerance threshold T is a pre-set allowable range for brightness error, set according to the display product grade and visual inspection standards, preferably a normalized brightness unit of 0.03 to 0.06. or If any value in the above values ​​is greater than the threshold T, it indicates that the current adjustment has failed to bring the bright and dark line responses to converge to the target brightness range, and the accuracy of the response model needs to be re-evaluated.

[0090] If the adjustment deviation exceeds the tolerance threshold, the model update mechanism is triggered. Using the adjusted luminance response matrix M1 as the new reference data source, the luminance deviation curve under the target grayscale is re-extracted, and the grayscale response function is reconstructed based on the latest data points. The update process uses the cubic spline interpolation and spatial location joint modeling method previously used to construct model F, replacing the original response function. After model F is updated, the driving parameter adjustment vector D is regenerated, and the adjustment execution process is repeated. The above steps will continue to iterate until the adjusted luminance response difference ΔM is less than or equal to the tolerance threshold T, at which point the adjustment process ends.

[0091] The final driving parameter adjustment scheme is output and used for batch bright and dark line defect optimization operations.

[0092] After the response difference converges to the target tolerance range, the final drive current adjustment value corresponding to each pixel column or pixel row in the adjustment vector D is recorded, and a mapping relationship is formed with its original drive current parameters to construct the final drive parameter adjustment scheme S. The adjustment scheme S includes the correspondence between grayscale input, current output, and spatial position, and can be directly used for display driver chip programming control or drive control register writing operations. The adjustment scheme is output in the form of a data table, which facilitates integration into the mass production process.

[0093] The output drive parameter adjustment scheme S is applied to display panels of the same model or batch, preferably to display modules that exhibit similar bright and dark line response characteristics after screening. During batch optimization, scheme S can be used as a base template, and the adjustment parameters can be fine-tuned according to the initial brightness distribution of different panels to achieve rapid iterative correction. By batch writing the adjustment parameters, production efficiency is effectively improved and the defect rate caused by bright and dark line defects is significantly reduced.

[0094] Example 2, please refer to Figure 2 As shown in the figure, the display screen brightness and darkness line adjustment and optimization system described in this embodiment includes:

[0095] The data acquisition module acquires the pixel column / row brightness response data matrix of the target display screen under multi-grayscale input and constructs the initial response distribution map M0 of bright and dark lines.

[0096] The grayscale fluctuation recognition module partitions the grayscale range based on M0, identifies the target grayscale stage with high response fluctuation, and constructs the target region feature set P based on the brightness deviation slope.

[0097] The response modeling module constructs a local response model F for bright and dark lines based on the position coordinates of the bright and dark lines and their grayscale response curves contained in the feature set P.

[0098] The drive adjustment generation module generates a drive parameter adjustment vector D based on the response model F and the preset brightness expectation value and drive current adjustment range.

[0099] The driving module applies the adjustment vector D to the target display screen to obtain the adjusted brightness response matrix M1.

[0100] The model update module calculates the difference in response before and after adjustment ΔM, and determines whether ΔM is lower than the preset tolerance threshold T. If not, the model F is updated until ΔM≤T.

[0101] The adjustment scheme output module outputs the final driving parameter adjustment scheme and is used for batch bright and dark line defect optimization operations.

[0102] Example 3: This example describes an LED display screen used to implement the aforementioned method for adjusting and optimizing the brightness and darkness of a display screen.

[0103] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for display screen dimming line adjustment optimization, characterized in that: The method comprises the following steps: obtaining the pixel column / row luminance response data matrix of the target display screen under multi-gray scale input, and constructing an initial bright-dark line response distribution map M0, specifically comprising: sequentially inputting a plurality of preset gray scale voltage values to the target display screen, covering the entire working luminance range; under each gray scale voltage input, the pixel luminance value is collected column by column or row by row to form a gray scale response data sequence; after normalization processing, a gray scale-luminance two-dimensional response matrix is constructed, which is organized by pixel column or row, and the luminance deviation curve is extracted; based on the luminance deviation curve, discrete Fourier analysis is performed on each column or row to extract the periodic bright-dark fluctuation characteristics, and the corresponding initial bright-dark line response distribution map M0 is generated; based on M0, the gray scale range is partitioned, the target gray stage with high response fluctuation is identified, and the target region feature set P is constructed according to the luminance deviation slope; wherein the luminance deviation slope is the ratio of the luminance deviation difference ΔL between two adjacent gray scales and the gray scale interval ΔG; according to the bright-dark line position coordinates and the gray scale response curve contained in the feature set P, a bright-dark line local response model F is constructed; according to the response model F, the preset luminance expectation value and the driving current adjustment range are combined to generate a driving parameter adjustment vector D; the adjustment vector D is applied to the target display screen to obtain the adjusted luminance response matrix M1; the difference ΔM between the adjusted and unadjusted responses is calculated, and it is judged whether ΔM is lower than the preset tolerance threshold T, if not, the model F is updated until ΔM≤T; output the final driving parameter adjustment scheme and use it for batch bright-dark line defect optimization operation.

2. The method of claim 1, wherein: The method comprises the following steps: the gray scale range is partitioned based on M0, the target gray stage with high response fluctuation is identified, and the target region feature set P is constructed according to the luminance deviation slope, which comprises: the luminance fluctuation amplitude corresponding to each gray scale in the initial bright-dark line response distribution map M0 is sorted according to the gray scale level, and based on the sorting result, the gray scale range is divided into a plurality of gray scale sub-sections by using a fixed threshold division method; the luminance fluctuation amplitude in each gray scale sub-section is calculated, and the gray scale sub-section with a variance value greater than a preset fluctuation variance threshold is identified, and the gray scale sub-section is marked as a target gray stage with high response fluctuation; in the target gray stage, the luminance change slope between adjacent gray scales is calculated point by point for the luminance deviation curve corresponding to the initial bright-dark line response distribution map M0, and a slope sequence for describing the luminance change trend is obtained; 3. The method of claim 2, wherein: the gray scale position with a slope absolute value greater than a preset slope threshold and the corresponding luminance deviation value in the slope sequence are extracted, and combined in the order of gray scale position to form the target region feature set P, which is used to represent the feature region of the bright-dark line response mutation in the target gray stage. The method comprises the following steps: in the feature set P, the gray scale response curve corresponding to each bright-dark line position is extracted, the luminance deviation data of the corresponding position under multiple gray scale inputs is obtained; the gray scale response curve is processed by curve fitting, a continuous response function is established by using a cubic spline interpolation algorithm, and a fitting model reflecting the relationship between gray scale input and luminance deviation is constructed; The luminance deviation function is combined with the column coordinates or the row coordinates to form a gray scale and spatial position double variable local response function; The local response function is defined as a bright and dark line local response model F.

4. The method of claim 3, wherein: According to the response model F, the preset luminance expectation value and the driving current adjustment range are combined to generate a driving parameter adjustment vector D, including: Set the luminance expectation value matrix E corresponding to each target gray scale, wherein each element represents the target luminance output under the set gray scale and position; Based on the bright and dark line local response model F, the predicted luminance output matrix P under the current driving parameter condition is calculated, and it is compared with the luminance expectation value matrix E point by point to obtain the luminance deviation matrix ΔL; The luminance deviation matrix ΔL is subjected to gradient mapping processing, and the driving current adjustment function is constructed in combination with the partial derivative information of the gray scale and current relationship in the response model F; The adjustment function is constrained and optimized in the preset driving current adjustment range, and the optimal adjustment vector D is solved by using the linear least square method, so that the predicted luminance tends to approach the luminance expectation value.

5. The method of claim 4, wherein: The adjustment vector D is applied to the target display screen to obtain an adjusted luminance response matrix M1, including: According to the current adjustment value in the adjustment vector D, the pixel column or pixel row driving current corresponding to the bright and dark line region is adjusted, and the adjustment value is written into the display control register; A plurality of preset gray scale voltage values are sequentially input to the target display screen, and the display output is driven under the action of the adjustment current; Under each gray scale input, the display region is sampled column by column or row by row to obtain the adjusted luminance response data; The collected luminance response data is matrix processed according to the gray scale level and the spatial position to construct the adjusted luminance response matrix M1.

6. The method of claim 5, wherein: The response difference ΔM before and after adjustment is calculated, and whether ΔM is lower than the preset tolerance threshold T is judged, if not, the model F is updated until ΔM≤T, including: The luminance response matrix M0 before adjustment and the luminance response matrix M1 after adjustment are subtracted element by element according to the corresponding positions to obtain the response difference matrix ΔM, which is used to represent the degree of luminance change; The response difference matrix ΔM is normalized, and its maximum value or average value is calculated as the adjustment deviation index; The adjustment deviation index is compared with the preset tolerance threshold T, if the maximum value or the average value is greater than T, it is determined that the current adjustment does not meet the requirement of luminance uniformity; In the case of adjustment failure, the luminance deviation data is extracted based on the latest luminance response matrix M1, the local response model F is updated until ΔM is less than or equal to the tolerance threshold T.

7. A display screen light-dark line adjustment optimization system for implementing a display screen light-dark line adjustment optimization method according to any one of claims 1-6, characterized in that: Including: The data acquisition module acquires the pixel column / row luminance response data matrix of the target display screen under multiple gray scale inputs to construct a bright and dark line initial response distribution graph M0; The gray scale fluctuation identification module partitions the gray scale range based on M0, identifies the target gray scale with high response fluctuation, and constructs a target region feature set P according to the luminance deviation slope; The response modeling module constructs a bright and dark line local response model F according to the bright and dark line position coordinates and the gray scale response curve contained in the feature set P; The driving adjustment generation module generates a driving parameter adjustment vector D according to the response model F, in combination with the preset luminance expectation value and the driving current adjustment range. The driving module applies the adjustment vector D to the target display screen to obtain an adjusted luminance response matrix M1; The model updating module calculates the difference ΔM between the pre-adjustment and post-adjustment responses, and determines whether ΔM is lower than a preset tolerance threshold T. If not, the model F is updated until ΔM≤T; The adjustment scheme output module outputs a final driving parameter adjustment scheme, and is used for batch light and dark line defect optimization operation.

8. An LED display screen used to implement the display screen light and dark line adjustment optimization method according to any one of claims 1-6.

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