A display compensation method and system of a curved display panel

Abstract

The present application relates to curved surface display technical field, disclose a kind of display compensation method and system of curved surface display panel.The method includes collecting the original luminance data under different viewing angles of curved surface display panel, establish the compensation reference library containing the correlation of luminance attenuation gradient and radius of curvature;Again, reference library is divided into main compensation area, transition compensation area and edge compensation area, and the compensation intensity grade of each area is determined;Dynamic compensation strategy containing luminance attenuation prediction model and color coordinate offset correction model is configured for main compensation area;After generating initial compensation parameter set based on the strategy, its effectiveness is verified by curved surface deformation simulation;If verification is passed, parameter set is loaded to display driver chip, and compensation is completed.The present application is suitable for various curved surface display equipment by accurate reference library construction, fine regional division, dynamic compensation strategy and comprehensive simulation verification, effectively improves the display consistency of curved surface display panel, improves the display quality under different viewing angles.

Description

Technical Field

[0001] This invention relates to the field of curved display technology, specifically to a display compensation method and system for curved display panels. Background Technology

[0002] With the continuous evolution of display technology, curved display panels, with their unique visual immersion, wide field of view, and excellent design, have been widely used in smartphones, smart TVs, automotive displays, virtual reality devices, and many other fields. Compared to traditional flat display panels, curved display panels, by bending the screen at a specific curvature, can better conform to the physiological characteristics of human vision, reduce distortion at visual edges, and provide users with an immersive viewing experience. However, while the curved structure gives display panels many advantages, it also introduces new display quality problems, the most prominent of which are brightness attenuation and color coordinate shift at different viewing angles.

[0003] Flat panel displays exhibit relatively uniform brightness distribution and their optical characteristics change relatively regularly under different viewing angles. Existing compensation technologies can effectively address display deviations. However, curved panel displays, due to their fixed radius of curvature, experience complex changes in the light propagation path within the panel and the emission angle of pixel units as the viewing angle changes. When a user shifts from a frontal view to a side view, the emitted light from some pixels is subjected to additional absorption or refraction by the panel substrate material, encapsulation layer, and polarizer, resulting in a significant decrease in observed brightness and forming a brightness attenuation gradient from the center to the edge of the screen. This brightness attenuation is not uniformly distributed but is closely related to the radius of curvature of the surface; the greater the curvature, the more significant the rate and magnitude of brightness attenuation.

[0004] Brightness attenuation is accompanied by color coordinate shift issues. This is because different wavelengths of light experience varying propagation losses within curved structures, resulting in inconsistent brightness attenuation ratios for the red, green, and blue primary colors. Consequently, this leads to color casts in the displayed image. For example, at the edges of curved display panels, the attenuation of blue light is typically greater than that of red and green light, causing the image to appear warmer. These display defects severely impact the display consistency and viewing experience of curved display panels, becoming a key bottleneck restricting their further expansion into high-end applications.

[0005] Currently, most compensation technologies for curved display panels are simply improvements on flat display compensation technologies, lacking specific design tailored to the characteristics of curved surfaces. Some technologies use fixed compensation parameters to uniformly compensate the entire curved panel, failing to consider the brightness attenuation gradient caused by differences in curvature in different areas. This results in poor compensation effects, either with overcompensation in the central area causing glare or undercompensation in the edge areas leaving noticeable dark areas. Other technologies attempt to divide compensation areas, but the criteria for area division are too simplistic, failing to establish a clear correlation between brightness attenuation and the radius of curvature, making the determination of compensation intensity lack a scientific basis. Furthermore, most existing compensation strategies use static compensation methods, unable to adjust in real time according to the dynamic changes in the user's viewing angle during actual use. When the user views while moving, the display quality still fluctuates. Additionally, in the verification of compensation parameters, most technologies only test by actually lighting up the panel. This method is not only time-consuming and labor-intensive but also fails to simulate the deformation of curved panels under different usage scenarios, causing some potential compensation defects to go undetected in time, thus affecting the final display effect. Summary of the Invention

[0006] The purpose of this invention is to provide a display compensation method and system for curved display panels to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides a display compensation method for a curved display panel, the method comprising:

[0008] Collect raw brightness data of curved display panels from different viewing angles and establish a compensation benchmark library that includes the relationship between brightness attenuation gradient and radius of curvature;

[0009] The compensation areas are divided and displayed according to the compensation benchmark library, and the corresponding compensation intensity level of each area is determined. The compensation intensity level includes the main compensation area, the transition compensation area and the edge compensation area.

[0010] A dynamic compensation strategy is configured for the main compensation area. The dynamic compensation strategy includes a brightness attenuation prediction model and a color coordinate offset correction model.

[0011] An initial compensation parameter set is generated based on a dynamic compensation strategy, and the effectiveness of the initial compensation parameter set is verified through surface deformation simulation.

[0012] When the verification is successful, the initial compensation parameter set is loaded into the display driver chip to complete the display compensation of the curved display panel.

[0013] Preferably, the specific method for establishing the compensation benchmark library is as follows:

[0014] Under standard lighting conditions, the axial brightness distribution of the curved display panel was measured at fixed intervals.

[0015] Extract the critical curvature point in the axial brightness distribution where the brightness value drops beyond a threshold.

[0016] Establish a mapping table between the radius of curvature and the brightness attenuation gradient based on the critical curvature point;

[0017] The mapping table is cross-validated with the preset color temperature attenuation coefficient to form the final compensation benchmark library.

[0018] Preferably, determining the compensation intensity level includes:

[0019] The maximum brightness deviation value is obtained by using a bidirectional gradient scanning method in the main compensation area;

[0020] The average brightness fluctuation amplitude is calculated using a ring sampling method in the transition compensation zone;

[0021] The discrete point detection method is used to identify the critical value of brightness jump in the edge compensation area;

[0022] The regional compensation weight coefficient is generated based on the maximum brightness deviation value, the average brightness fluctuation amplitude, and the brightness jump threshold value.

[0023] Preferably, the execution process of the dynamic compensation strategy includes:

[0024] Input the current ambient light intensity and panel operating temperature into the brightness decay prediction model;

[0025] The process of constructing the brightness attenuation prediction model is as follows: the brightness output value of the curved display panel under different combinations of ambient light intensity and panel operating temperature is measured in a controllable environment chamber. The multivariate nonlinear regression equation of brightness attenuation with ambient light intensity and operating temperature is fitted by the least squares method, and the radius of curvature is introduced as a correction factor to generate the brightness attenuation prediction function.

[0026] The gamma-corrected proportions of the three primary colors are received through a color coordinate offset correction model.

[0027] The construction process of the color coordinate offset correction model is as follows: under standard color environment, the color coordinate data of the three primary colors after gamma correction in different areas of the curved display panel are collected, the color coordinate offset vector is calculated based on the CIE colorimetry standard, and a polynomial interpolation algorithm is used to establish a mapping relationship model between the color coordinate offset and the viewing angle and curvature.

[0028] The ambient light intensity, operating temperature and the proportion of the three primary colors are combined to generate the compensation parameter adjustment amount;

[0029] The luminance gain factor and color gamut mapping matrix in the initial compensation parameter set are updated based on the adjustment amount of the compensation parameters.

[0030] Preferably, the surface deformation simulation verification adopts the following steps:

[0031] A virtual projection surface with the same curvature as the physical panel is constructed. After loading the initial compensation parameter set onto the virtual projection surface, the color uniformity at different viewing distances is simulated.

[0032] When the color uniformity deviation exceeds the tolerance value, the failure compensation node is marked and fed back to the dynamic compensation strategy;

[0033] Regenerate and adjust the compensation parameters until the color uniformity of the virtual projection surface meets the preset standard.

[0034] Preferably, the iterative optimization process of the compensation parameter adjustment amount includes:

[0035] Establish an optimization priority queue that includes the location and deviation of failure compensation nodes;

[0036] Configure and optimize the parameter correction step size for the priority queue in the dynamic compensation strategy;

[0037] The parameter correction of the main compensation zone and the transition compensation zone is processed sequentially in a layered and gradual manner.

[0038] An adaptive filtering algorithm is enabled for the edge compensation area to eliminate high-frequency brightness noise.

[0039] Preferably, the following steps are performed before loading the data into the display driver chip:

[0040] The final compensation parameter set is converted into a binary instruction format supported by the display driver chip;

[0041] Verify the consistency between the binary instruction format and the chip register address;

[0042] Hardware-in-the-loop testing was used to verify whether the instruction execution timing met the clock cycle requirements of the driver chip.

[0043] Preferably, the hardware-in-the-loop test is implemented as follows:

[0044] Inject timing constraints of the display driver chip into the simulation environment;

[0045] Monitor the transmission delay of compensation parameter commands during the line scan cycle and frame refresh cycle;

[0046] When transmission delays cause data loss, insert a blank clock cycle to resynchronize the instruction stream;

[0047] Record the synchronized instruction stream checksum and compare it with the expected result.

[0048] Preferably, the method further includes a dynamic update mechanism for the compensation parameter set:

[0049] Real-time monitoring of the temperature rise curve and ambient light intensity change rate of the curved display panel;

[0050] When the rate of temperature rise or the change in light intensity exceeds the threshold, an incremental update of the compensation benchmark library is triggered.

[0051] Use a sliding window method to filter the compensation areas that need to be recalculated;

[0052] Local parameter optimization is performed only on the selected compensation regions to reduce computational load.

[0053] Preferably, the present invention also includes a display compensation system for a curved display panel, the system including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor, when executing the computer program, implements the steps of the display compensation method for the curved display panel as described above.

[0054] Compared with the prior art, the beneficial effects of the present invention are:

[0055] By collecting raw brightness data from different viewpoints, a compensation benchmark library is established that includes the relationship between brightness attenuation gradient and radius of curvature. This process overcomes the limitation of traditional compensation techniques that ignore the influence of curvature. The establishment of this benchmark library is not based on theoretical speculation but on actual measured optical data, ensuring that the relationship between brightness attenuation and curvature is highly objective and specific. Subsequent compensation area division and parameter determination based on this benchmark library allow the compensation scheme to closely fit the structural characteristics of the specific curved display panel, avoiding deviations between generalized compensation strategies and actual display requirements, thus laying the foundation for the accuracy of subsequent compensation work.

[0056] In the compensation area division stage, the display panel is clearly divided into a main compensation area, a transition compensation area, and an edge compensation area, and the corresponding compensation intensity level for each area is determined. This refined area division method overcomes the shortcomings of single compensation or coarse partitioning in traditional technologies. Different areas exhibit significant differences in curvature changes and brightness attenuation characteristics. The main compensation area typically corresponds to regions with relatively gentle brightness attenuation, the transition compensation area bridges the gap between the main and edge compensation areas, and the edge compensation area is where brightness attenuation is most severe. Configuring differentiated compensation strategies for different areas ensures that each area receives compensation tailored to its characteristics, avoiding over-compensation in some areas and under-compensation in others. This results in a more uniform brightness distribution across the entire display panel and effectively weakens the brightness attenuation gradient from the center to the edge.

[0057] The dynamic compensation strategy configured for the main compensation area includes a brightness attenuation prediction model and a color coordinate shift correction model. This design enables the compensation scheme to be dynamically adaptable. The brightness attenuation prediction model can predict the trend of brightness attenuation in advance based on real-time changes in the user's viewing angle, while the color coordinate shift correction model makes targeted corrections for the attenuation differences of different wavelengths of light. Traditional static compensation strategies cannot adjust compensation parameters in a timely manner when the user's viewing angle changes, resulting in fluctuations in display quality. The dynamic compensation strategy, however, can update compensation parameters in real time, ensuring that the user can view a stable brightness and accurate color image regardless of their viewing angle. Especially in application scenarios such as automotive displays and virtual reality devices where the user's viewing angle frequently changes, this dynamic adaptability can fully leverage its advantages and improve the user experience.

[0058] After generating the initial compensation parameter set based on the dynamic compensation strategy, its effectiveness is verified through surface deformation simulation. This verification step provides strong assurance for the reliability of the compensation scheme. In actual use, curved display panels may undergo slight deformations due to factors such as temperature changes and minor external pressure, which can affect their optical properties. Traditional physical illumination tests are difficult to simulate these complex deformation scenarios. However, surface deformation simulation can use computer simulation technology to simulate the optical performance of the panel under different deformation states, thereby verifying the adaptability of the initial compensation parameter set under various extreme conditions. This verification method allows for the early identification of loopholes and deficiencies in the compensation parameters, enabling targeted optimization and preventing the application of defective compensation parameters to actual products, thus reducing subsequent rework costs and quality risks.

[0059] Once verification is successful, the initial compensation parameter set is loaded into the display driver chip, completing the entire compensation process. The entire method forms a complete closed-loop system, from data acquisition and benchmark library establishment to region partitioning, strategy configuration, parameter generation, and simulation verification. Each step is closely linked, with each step providing precise data support for the next, and the next optimizing and verifying the results of the previous step, ensuring that the final output compensation parameter set accurately matches the characteristics of the curved display panel. Through the application of this method, the brightness attenuation and color coordinate shift issues of curved display panels under different viewing angles are effectively alleviated, significantly enhancing the consistency and stability of the displayed image. This better meets users' demands for high-quality display effects and further promotes the popularization and development of curved display technology in various high-end application scenarios. Attached Figure Description

[0060] Figure 1 This is a schematic diagram illustrating the working principle of the display compensation method for curved display panels according to the present invention.

[0061] Figure 2 A flowchart for establishing a compensation benchmark library;

[0062] Figure 3 The flowchart for iterative optimization of compensation parameter adjustment;

[0063] Figure 4 This is a diagram showing the instruction transmission delay analysis of the display driver chip. Detailed Implementation

[0064] 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, and 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.

[0065] Please see Figure 1 This invention provides a display compensation method for curved display panels. The method includes: establishing a precise compensation benchmark library and dividing differentiated compensation regions, combining dynamic strategies and simulation verification to achieve effective correction of viewing angle color deviation. Raw brightness data of the curved display panel under standard lighting conditions at different viewing angles is collected, the correlation between brightness attenuation gradient and radius of curvature is analyzed, and a compensation benchmark library is constructed. Based on the compensation benchmark library, the display area is divided into a main compensation region, a transition compensation region, and an edge compensation region, and the compensation intensity level corresponding to each region is determined. A dynamic compensation strategy is configured for the main compensation region, which includes a brightness attenuation prediction model and a color coordinate offset correction model. An initial compensation parameter set is generated based on the dynamic compensation strategy, and the effectiveness of the initial compensation parameter set is verified through curved surface deformation simulation. After successful verification, the initial compensation parameter set is loaded into the display driver chip to complete the display compensation of the curved display panel.

[0066] Example 1: See Figure 2The axial luminance distribution of a curved display panel was measured at fixed intervals under standard lighting conditions. The measurement process was conducted in an optical darkroom to eliminate ambient light interference. The walls, ceiling, and floor of the darkroom were covered with light-absorbing materials, and the ambient illuminance was maintained below 0.1 lux. A high-precision imaging luminance meter was used, with a minimum measurement angle of 0.1 degrees and a luminance measurement range covering 0.01 to 10,000 nits. The fixed interval angle was set to 1 degree, and the measurement range covered a viewing angle extending 85 degrees to both sides of the center normal of the curved display panel. The imaging luminance meter was mounted on a precision electronically controlled rotating platform, driven by a stepper motor for angle positioning with an accuracy of 0.01 degrees. The curved display panel was fixed stationary on a dedicated fixture, and a full white field image was input from a standard video signal generator. The panel's driving conditions were maintained at standard operating voltage and frequency. At the start of the measurement, the rotating platform scans from -85 degrees to +85 degrees in 1-degree increments, pausing at each angle for sufficient time to allow the luminance meter to complete data acquisition. The measurement data is transmitted in real-time to the host computer via a data acquisition card. Axial luminance distribution data is recorded as a function of viewing angle, with each measurement point corresponding to a radius of curvature value. The data processing unit converts the raw measurement data into a luminance-angle curve, employing a smoothing algorithm to eliminate random measurement noise. The smoothing process uses a moving average filter with a window width of 5 data points. Critical curvature points where the luminance value drops beyond a preset threshold are extracted from the processed luminance-angle curve. The preset threshold is set to 10% of the peak luminance, and the criterion for determining the critical curvature point is the location where the first derivative of the luminance curve reaches an extreme value. The extraction process uses a gradient detection algorithm to calculate the gradient rate of change at each point on the luminance curve. When the gradient value exceeds a set threshold, the corresponding radius of curvature and viewing angle coordinates are recorded. The coordinate information of the critical curvature points is stored in a temporary buffer for subsequent mapping relationship construction.

[0067] A mapping table between the radius of curvature and the brightness attenuation gradient is established based on the critical curvature point. This table uses a two-dimensional matrix to store the correspondence between the radius of curvature values ​​and the corresponding brightness attenuation rates. The row index represents the radius of curvature value, and the column index represents the viewing angle parameter. Each matrix element stores the brightness attenuation gradient value under the corresponding combination of radius of curvature and viewing angle. The brightness attenuation gradient value is calculated based on the rate of change of the brightness-angle curve near the critical curvature point, using numerical differentiation to obtain the brightness attenuation rate caused by curvature changes. The construction process of the mapping table includes a data interpolation step. For curvature radius-viewing angle combinations not covered in the measurement data, bilinear interpolation is used to estimate their brightness attenuation gradient values, ensuring the continuity and completeness of the mapping table. The mapping table is cross-validated with a preset color temperature attenuation coefficient, which is obtained by measuring the color coordinate changes under different curvatures using a spectroradiometer. The spectroradiometer measures the color coordinate values ​​of the curved display panel at the same angular intervals, maintaining the same measurement conditions as the brightness distribution measurement. The color temperature attenuation coefficient is calculated as the proportion of the deviation between the relevant color temperature value and the standard color temperature value under different curvatures. The cross-validation process compares the consistency between brightness attenuation and color temperature change. The validation logic checks whether the brightness attenuation gradient and color temperature attenuation coefficient show the same trend under the same radius of curvature. When there is a significant difference between the brightness attenuation gradient and the color temperature attenuation coefficient, a remeasurement process is initiated until the trends of the two are consistent. The mapping relationship table obtained through cross-validation is merged with the color temperature attenuation coefficient data to form the final compensation benchmark library. The compensation benchmark library is stored in a database format and contains four data fields: radius of curvature index, viewing angle index, brightness attenuation gradient value, and color temperature attenuation coefficient.

[0068] The maximum brightness deviation value is obtained by using a bidirectional gradient scanning method in the main compensation area, which is defined as the part of the curved display panel where the curvature changes gently in the center. The bidirectional gradient scan traverses the brightness value of each pixel in the main compensation area in both horizontal and vertical directions, following the raster scanning sequence. The horizontal scan line spacing is set to one pixel width, and the vertical scan line spacing is also set to one pixel width. During the scanning process, an imaging luminance meter sequentially measures the brightness value of each pixel, and the measurement data is uploaded to the processing unit in real time. The maximum brightness deviation value is calculated as the maximum difference between the actual brightness recorded during the scanning process and the ideal brightness. The ideal brightness value is determined based on the brightness uniformity standard of the flat panel display under the same driving conditions. The calculation process compares the actual brightness value and the ideal brightness value of each pixel, recording the maximum absolute value of the brightness deviation among all pixels. The maximum brightness deviation value serves as a key indicator for evaluating the brightness uniformity of the main compensation area. For the transition compensation area, an annular sampling method is used to calculate the average brightness fluctuation amplitude. The transition compensation area is located in the annular region between the main compensation area and the edge compensation area. Circular sampling uses the panel center as the origin, setting sampling rings at different radii. The number of rings is determined by the panel size, and the width of each ring is set to 5% of the panel's shorter side length. Sampling points are evenly distributed on each ring, with an angular interval of 10 degrees between adjacent sampling points. The total number of sampling points ensures that it can represent the brightness characteristics of the transition compensation zone. The average brightness fluctuation amplitude is obtained by statistically analyzing the standard deviation of the brightness values ​​within each ring. The calculation process first calculates the average brightness value of all sampling points on each ring, then calculates the deviation of each sampling point's brightness value from the ring's average value, and finally calculates the root mean square of all deviations as the brightness fluctuation amplitude of that ring. The arithmetic mean of the brightness fluctuation amplitudes of all rings is then used as the average brightness fluctuation amplitude index of the transition compensation zone.

[0069] Discrete point detection is used to identify the brightness jump threshold in the edge compensation area, which corresponds to the outer edge of the curved display panel where the curvature changes most drastically. Discrete point detection selects representative pixels in the edge area to monitor brightness abrupt changes. The selection of sampling points is based on the rate of curvature change, with the sampling point distribution being densified at the location of the largest rate of curvature change. The brightness jump threshold is identified by analyzing the first-order difference of the brightness values ​​of the sampling points. When the brightness difference between adjacent sampling points exceeds a set threshold, that location is recorded as a brightness jump point. The point with the largest brightness difference among all brightness jump points is determined as the brightness jump threshold, which characterizes the severity of brightness non-uniformity in the edge compensation area. Regional compensation weight coefficients are generated based on the maximum brightness deviation, average brightness fluctuation amplitude, and brightness jump threshold. These coefficients are then combined using a weighted average algorithm to integrate the characteristic values ​​of the three regions. The weighting factors of the weighted average algorithm are determined based on the proportion of each region in the overall display area, with the main compensation area having the highest area weight, followed by the transition compensation area, and the edge compensation area having the lowest area weight. The calculation process first normalizes the maximum brightness deviation, average brightness fluctuation amplitude, and brightness jump threshold to the same numerical range. Then, it multiplies these values ​​by their respective area weighting coefficients, and finally sums them to obtain the overall regional compensation weighting coefficient. The regional compensation weighting coefficient is used to adjust the resource allocation of each region in subsequent compensation strategies. Regions with higher weighting coefficients will receive more refined compensation processing.

[0070] Example 2: Execution Process of Dynamic Compensation Strategy. The current ambient light intensity and panel operating temperature are input into the brightness attenuation prediction model. The ambient light intensity data comes from a high-precision ambient light sensor installed on the display device's bezel. The spectral response curve of the ambient light sensor matches the human eye's visual function, and the measurement range covers 1 lux to 100,000 lux. The panel operating temperature is obtained through multiple temperature sensors distributed on the back panel of the curved display panel. The temperature sensors use negative temperature coefficient thermistors, achieving a measurement accuracy of ±0.5 degrees Celsius, with a sampling frequency of 10 times per second. The ambient light intensity and panel operating temperature data are transmitted to the main processing unit via an I2C digital interface. Data preprocessing is performed in the main processing unit, including outlier removal and moving average filtering to eliminate the influence of transient interference on the measurement data. The brightness attenuation prediction model is constructed by measuring the brightness output value of the curved display panel under different combinations of ambient light intensity and panel operating temperature in a controlled environment chamber. The internal space dimensions of the controlled environment chamber are 2 meters × 2 meters × 2 meters, and the inner walls are covered with diffuse reflective material to avoid specular reflection affecting measurement accuracy. Ambient light intensity control is achieved through a programmable LED light source array. The color temperature of the light source array is stabilized at 6500K, and the brightness adjustment range covers 0 to 2000 lux with an adjustment accuracy of 1 lux. Panel operating temperature control is achieved through a semiconductor cooling chip and a heating film, with a temperature control range of 0 to 80 degrees Celsius and a control accuracy of ±0.1 degrees Celsius. During measurement, the curved display panel displays a standard grayscale image with grayscale levels uniformly distributed from 0 to 255. An imaging colorimeter is used to measure the brightness output value at each grayscale level. The measurement data is stored in a multidimensional array, with dimensions including ambient light intensity, panel operating temperature, and grayscale level.

[0071] A multivariate nonlinear regression equation for brightness decay in relation to ambient light intensity and operating temperature was fitted using the least squares method. The independent variables of the regression equation included ambient light intensity, panel operating temperature, and their interaction term, while the dependent variable was the brightness decay rate. The brightness decay rate was calculated as the ratio of the brightness value measured under current conditions to the brightness value under standard conditions, defined as an ambient light intensity of 500 lux and a panel operating temperature of 25 degrees Celsius. The least squares fitting process used the Levenberg-Marquardt optimization algorithm to solve for the regression coefficients. The regression equation was a quadratic polynomial, containing quadratic terms for ambient light intensity, panel operating temperature, and the product of ambient light intensity and panel operating temperature. The goodness-of-fit index R-squared value was required to be greater than 0.95; otherwise, the form of the regression equation was readjusted or additional measurement data points were added. The radius of curvature was introduced as a correction factor to generate a brightness decay prediction function. This correction factor adjusted the decay rate in different curvature regions using coefficients. The radius of curvature data was obtained from the design drawings of the curved display panel, and the panel surface was discretized into a grid, with each grid point corresponding to a radius of curvature value. The brightness decay prediction function adds a radius of curvature term to the multivariate nonlinear regression equation. The radius of curvature term is multiplied by the original regression equation, and the product coefficient is calibrated using experimental data. The calibration process involves repeatedly measuring brightness decay data at multiple typical radius of curvature values, and determining the coefficient value of the radius of curvature term through curve fitting. The resulting brightness decay prediction function can simultaneously reflect the combined effects of ambient light intensity, panel operating temperature, and radius of curvature on brightness decay.

[0072] The color coordinate offset correction model receives gamma-corrected primary color ratio data from the gamma correction module in the image processing pipeline. The gamma correction module performs a non-linear transformation on the RGB components of the input image according to a standard gamma curve, resulting in floating-point numbers between 0 and 1. The primary color ratio data is transmitted to the color coordinate offset correction model via a high-speed data bus in 32-bit floating-point format, with a transmission bandwidth sufficient for real-time processing. The color coordinate offset correction model has an internal input buffer with a depth of 8 lines of image data for temporary storage of pixel information to be processed. The model is constructed by acquiring gamma-corrected color coordinate data for different areas of the curved display panel under a standard colorimetric environment. This environment conforms to the CIE 1931 standard colorimetric observer conditions, with an observation geometry of 0 / 45 degrees. A high-precision spectroradiometer is used for measurement, covering a wavelength range of 380 nm to 780 nm with a 1 nm wavelength interval and a 1-degree measurement field of view. During the data acquisition process, the curved display panel sequentially displays red, green, and blue monochrome images, with the grayscale level of each monochrome image increasing from 0 to 255 in increments of 16. At each grayscale level, the color coordinate values ​​of different areas on the panel are measured. The measurement points are evenly distributed across the panel surface, and the spacing between the measurement points is adjusted according to the curvature of the panel, with a denser distribution of measurement points in areas with greater curvature changes.

[0073] The color coordinate offset vector is calculated based on the CIE colorimetric standard, representing the difference between the actual color coordinates and the standard color coordinates. The standard color coordinates are taken from measurements of flat panel displays under identical driving conditions. The calculation of the color coordinate offset vector includes three components: lightness difference, chromaticity difference, and hue difference. The lightness difference component is calculated as the difference between the actual lightness value and the standard lightness value; the chromaticity difference component is calculated as the Euclidean distance between the actual chromaticity coordinates and the standard chromaticity coordinates; and the hue difference component is calculated as the angular difference between the actual hue angle and the standard hue angle. The magnitude of the color coordinate offset vector represents the magnitude of the color difference, and the direction represents the color shift trend. A polynomial interpolation algorithm is used to establish a mapping relationship model between the color coordinate offset and the viewing angle and curvature. Cubic spline functions are used in the polynomial interpolation to ensure curve smoothness. The independent variables of the mapping relationship model are the viewing angle and curvature, and the dependent variables are the three components of the color coordinate offset vector. The modeling process divides the measurement data into a training set and a test set. The training set is used to fit the polynomial coefficients, and the test set is used to verify the model accuracy. The polynomial order is determined based on the goodness-of-fit index, typically choosing a 3rd to 5th order polynomial. Too high an order can lead to overfitting. Interpolation nodes are set at the measurement points, and the node spacing is dynamically adjusted according to the data density. Virtual nodes are added in sparse data regions to improve interpolation accuracy.

[0074] The compensation parameter adjustment amount is generated by fusing ambient light intensity, operating temperature, and the proportions of the three primary colors. The fusion process employs a data fusion algorithm to calculate the contribution weight of each factor. Based on fuzzy logic, the algorithm defines membership functions for ambient light intensity, panel operating temperature, and the proportions of the three primary colors, and calculates the degree of influence of each factor on the compensation parameter through fuzzy inference rules. The compensation parameter adjustment amount includes three parts: brightness adjustment, contrast adjustment, and chromaticity adjustment. The brightness adjustment is mainly affected by ambient light intensity, the contrast adjustment by panel operating temperature, and the chromaticity adjustment by the proportions of the three primary colors. The fusion algorithm outputs a comprehensive adjustment vector, where each component corresponds to a change in a compensation parameter. The initial compensation parameter set's brightness gain factor and color gamut mapping matrix are updated based on the compensation parameter adjustment amount. The brightness gain factor directly adjusts the backlight drive voltage, and the color gamut mapping matrix corrects the color conversion lookup table. The brightness gain factor is updated incrementally; the new brightness gain factor equals the original brightness gain factor multiplied by the brightness adjustment amount. The brightness adjustment amount is a proportional coefficient ranging from 0.5 to 2.0. The color gamut mapping matrix is ​​updated via matrix multiplication. The new color gamut mapping matrix is ​​equal to the original color gamut mapping matrix multiplied by the chroma adjustment matrix. The chroma adjustment matrix is ​​a 3×3 linear transformation matrix, and its elements are determined by the chroma adjustment amount. The updated set of compensation parameters is stored in non-volatile memory for the display driver chip to access in real time.

[0075] Example 3: See Figure 3The virtual projection surface, with curvature consistent with the physical panel, was constructed and verified through surface deformation simulation. The geometric model of the virtual projection surface was based on the mathematical representation of a non-uniform rational B-spline surface. Key geometric parameters, including the radius of curvature distribution, surface equation coefficients, and boundary conditions, were extracted from the computer-aided design file of the curved display panel. The control point mesh density of the non-uniform rational B-spline surface maintained a proportional relationship with the panel resolution. The node vector sequence was non-uniformly divided according to the curvature gradient, with denser node distribution corresponding to regions of drastic curvature changes. The material properties of the virtual projection surface were configured using a physical optics model, with surface reflection characteristics including diffuse and specular components. The refractive index parameter was set according to the panel's protective layer material. The constructed virtual projection surface was imported into optical simulation software configured to use a Monte Carlo ray tracing algorithm, with the tracing depth of each ray set to 10 reflections and the number of sampled rays reaching 1000 per pixel. After loading the initial compensation parameter set onto the virtual projection surface, color uniformity at different viewing distances was simulated. The initial compensation parameter set was loaded into the graphics processor's memory in binary data stream format. The observation distance simulation is achieved by moving the virtual camera, with the camera lens focal length fixed at 50mm, corresponding to the standard human eye perspective. Color uniformity evaluation uses a standard color difference calculation module, which implements the ΔE*ab color difference formula in the CIELAB color space, with the sampling point grid covering the entire virtual projection surface. The simulation engine generates a high dynamic range image at each observation distance point. The image data includes the XYZ tristimulus values ​​for each pixel, and the color uniformity calculation results are stored as a color difference distribution map.

[0076] When the color uniformity deviation exceeds the tolerance value, a failure compensation node is marked and fed back to the dynamic compensation strategy. The tolerance value is set based on the industry standard color uniformity specification. The marking algorithm for failure compensation nodes traverses each pixel of the color difference distribution map, comparing the pixel color difference value with the tolerance threshold. The marking process generates a failure node bitmap, where each bit corresponds to the failure state of a pixel. The coordinate information of the failure node is extracted using a boundary tracking algorithm. The dynamic compensation strategy interface receives a failure node descriptor containing node coordinates, color difference value, and local curvature. The descriptor data structure includes header information and payload. The compensation parameters are regenerated and adjusted until the color uniformity of the virtual projection surface meets the preset standard. The regeneration process uses an iterative reweighted least squares optimization algorithm. The objective function of the optimization algorithm is defined as the weighted sum of squares of the global color difference values ​​of the virtual projection surface, with the weight coefficients positively correlated with the failure degree of the node. In each iteration, the optimal increment of the compensation parameters is solved. The increment value is obtained by solving a system of normal equations, and the coefficient matrix of the system of equations is inverted using the Choliski decomposition method. The iteration termination condition is set to the rate of change of the objective function value being less than a set threshold or reaching the maximum number of iterations. The preset standard requires the peak signal-to-noise ratio of the color difference distribution map to be greater than 40 dB.

[0077] The iterative optimization process for adjusting compensation parameters establishes an optimization priority queue containing the location and deviation degree of failure compensation nodes. This queue is implemented using a Fibonacci heap data structure. Node priority is determined by both the color difference value and local curvature, with a weighting coefficient of 0.7 for the color difference and 0.3 for curvature. Queue operations include three basic operations: inserting a node, extracting the highest priority node, and adjusting node priority. The time complexity of these operations is logarithmic. The persistent storage of the optimization priority queue uses a binary serialization format, and the serialized data includes queue structure information and node metadata.

[0078] In the dynamic compensation strategy, the parameter correction step size corresponding to the priority queue is configured and optimized. The parameter correction step size is calculated by linear interpolation based on the priority position of the node in the queue. The highest priority node corresponds to the maximum correction step size, which is determined by the product of the basic step size parameter and the priority coefficient. The mathematical relationship of the parameter correction step size is defined by the following formula:

[0079]

[0080] in: This represents the parameter correction step size of the nth node in the m-th iteration. This represents the priority score of the nth node in the m-th iteration. This indicates the highest priority score in the current iteration. Indicates the step size scaling factor. This represents the basic step size constant.

[0081] A layered, progressive approach is adopted to sequentially address parameter correction in the main compensation region and the transition compensation region. The layered, progressive framework divides the optimization process into three progressive stages. The first stage focuses on improving the global color uniformity of the main compensation region, allowing for a relatively large fluctuation in the parameter correction step size to quickly reduce the overall color difference level. The second stage introduces a local optimization objective for the transition compensation region, narrowing the parameter correction step size range to balance color consistency across different regions. The third stage implements fine-tuning, fixing the parameter correction step size to a smaller value to eliminate local color difference fluctuations. A stage transition mechanism monitors the convergence of color difference values ​​in each region, triggering a stage transition when the standard deviation of the color difference value in the main compensation region is less than a threshold. An adaptive filtering algorithm is used to eliminate high-frequency luminance noise in the edge compensation region. This adaptive filtering algorithm is based on the wavelet transform threshold denoising principle. The algorithm performs multi-resolution analysis on the luminance signal of the edge compensation region, using the Debussy wavelet basis function for five-level decomposition. A soft threshold function is used for threshold processing, with the threshold size adaptively adjusted according to the sub-band noise variance, resulting in larger thresholds for high-frequency coefficients and smaller thresholds for low-frequency coefficients. The filtered signal is then reconstructed using wavelet reconstructing to recover the time-domain waveform. The reconstruction process preserves the main luminance characteristics of the edge compensation region while suppressing random noise. The computational complexity of the adaptive filtering algorithm is linearly related to the signal length, which meets the requirements of real-time processing.

[0082] Example 4: Before being loaded into the display driver chip, the final compensation parameter set is converted into a binary instruction format supported by the display driver chip. The conversion process relies on the instruction set architecture document provided by the display driver chip manufacturer. The instruction set architecture document defines in detail the bit width and layout of the opcode, address field, and data field of each control instruction. The brightness gain factor, color gamut mapping matrix coefficients, and gamma correction table data contained in the final compensation parameter set are mapped to the chip's internal register address space. The conversion algorithm traverses each parameter item in the final compensation parameter set, quantizing it into a fixed-width binary number according to the data type and value range of the parameter. The quantization process adopts uniform quantization, and the quantization level is determined according to the precision of the chip registers. The brightness gain factor is quantized into an 8-bit unsigned integer, the color gamut mapping matrix coefficients are quantized into a 10-bit signed integer, and the gamma correction table entries are quantized into 12-bit unsigned integers. The quantized binary data is packaged according to the instruction format. The instruction packet consists of an instruction header, a register address, parameter data, and a cyclic redundancy check (CRC) code. The instruction header identifies the write register operation, the register address points to the target register, the parameter data carries the quantized compensation parameters, and the CRC code is used for transmission error detection.

[0083] The consistency between the binary instruction format and the chip register addresses is verified on a dedicated instruction verification platform. This platform simulates the memory mapping interface of the display driver chip and loads the display driver chip's register mapping table file. The register mapping table file defines the name, address, access permissions, reset value, and function description of each register in XML format. The verification algorithm parses each generated binary instruction, extracts the register address field, and queries the register mapping table to verify the address's validity. For write operations, the algorithm further checks whether the target register has write permissions and whether the bit width of the parameter data matches the register definition. A consistency report is generated, listing all successfully matched instructions and detected inconsistencies, including address out-of-bounds errors, permission conflicts, and bit width errors. Hardware-in-the-loop testing verifies whether the instruction execution timing meets the driver chip's clock cycle requirements. The hardware-in-the-loop test system includes a field-programmable gate array (FPGA) emulator and a real display driver chip. The FPGA emulator programs the digital logic of the display controller, generating horizontal synchronization signals, vertical synchronization signals, data enable signals, and pixel clock signals. The display driver chip is mounted on a test fixture and connected to a field-programmable gate array (FPGA) emulator via a board-to-board connector. The test system is configured with an adjustable power supply to provide the display driver chip with its core voltage and input / output voltages, the values ​​of which are set according to the chip's datasheet. During testing, the FPGA emulator embeds a binary instruction stream into the blanking interval of the video data and transmits it to the display driver chip. Simultaneously, a logic analyzer captures the waveforms on the signal lines and measures the setup and hold times of the instruction transmission.

[0084] Referring to Table 1, the specific implementation of hardware-in-the-loop testing involves injecting timing constraints of the display driver chip into the simulation environment. These constraints are extracted from the display driver chip's datasheet. Key timing parameters include instruction data setup time, instruction data hold time, instruction latch signal pulse width, and instruction frame interval time, defined in nanoseconds. The simulation environment uses a hardware description language to write a test platform, which instantiates the behavioral model of the display driver chip. The behavioral model simulates the delay of the signal path within the chip. Timing constraints are injected into a static timing analysis tool as timing exceptions. The static timing analysis tool analyzes signal timing margins based on the netlist after routing. The transmission delay of compensation parameter instructions during the line scan cycle and frame refresh cycle is monitored, with monitoring points set at the instruction receive port of the display driver chip. Monitoring during the line scan cycle covers the line forward time and line blanking time, while monitoring during the frame refresh cycle covers the frame forward time and frame blanking time. A high-precision digital oscilloscope is used for monitoring, with a sampling rate set to 10GSa / s and a bandwidth of 1GHz. The measurement results record the offset of the command data relative to the pixel clock edge, and the transmission delay is calculated as the time difference between the valid edge of the command data and the valid edge of the clock. The measurement data is automatically recorded in a table, which includes the command index, theoretical transmission time, actual transmission time, and delay value.

[0085] Table 1: Measurement Record of Compensation Parameter Command Transmission Delay

[0086] Instruction Index Line period phase (μs) Frame period phase (ms) Theoretical transmission time (ms) Actual transmission time (ms) Transmission delay (ns) 0x0010 12.5 1.245 1.2575 1.2578 +300 0x0011 15.2 1.248 1.2632 1.2630 -200 0x0012 18.7 2.491 2.5097 2.5102 +500 0x0013 21.3 2.494 2.5153 2.5151 -200 0x0014 24.8 3.737 3.7618 3.7625 +700

[0087] When transmission delays cause data loss, a blank clock cycle is inserted to resynchronize the instruction stream. Data loss is determined when the setup or hold time of the instruction data does not meet the timing requirements of the display driver chip. The blank clock cycle is inserted in the idle interval between instruction frames, and the number of inserted clock cycles is dynamically adjusted according to the degree of delay. After detecting three consecutive instruction transmission errors, the resynchronization mechanism initiates a synchronization recovery process. This process first sends a synchronization prefix code, then inserts a variable number of blank clock cycles, and finally retransmits the erroneous instruction. The synchronization prefix code uses Manchester encoding, with "01" representing logic 0 and "10" representing logic 1, and a fixed prefix code length of 16 bits. The synchronized instruction stream checksum is recorded and compared with the expected result. The checksum calculation uses a 32-bit cyclic redundancy check (CRC) algorithm. The CRC polynomial uses the generator polynomial of the IEEE 802.3 standard, with an initial register value set to 0xFFFFFFFF, input data reflection enabled, output data reflection enabled, and a final XOR value set to 0xFFFFFFFF. The expected result is pre-calculated during the instruction stream generation stage and stored in a checksum lookup table. The comparison process is executed after the instruction stream transmission is complete. The actual checksum value is read from the display driver chip's status register and XORed with the expected value. A comparison result of zero indicates that the instruction stream transmission was complete and error-free, while a non-zero result indicates the location and number of transmission errors. An erroneous instruction stream triggers a retransmission mechanism, which retransmits all subsequent instructions starting from the last correct instruction packet.

[0088] See Figure 4 This paper presents the timing performance analysis results of the display driver chip instruction transmission in a curved display compensation system. The results are presented in an intuitive bar chart format, showing the time delay variations of different instructions during transmission. The chart clearly shows the arrival time of instruction data relative to the theoretical transmission time, with positive values ​​representing delayed arrival and negative values ​​representing early arrival. This timing fluctuation directly reflects the complexity of the chip's internal signal processing path and the accuracy of the system clock synchronization. The chart also includes key statistical indicators and timing constraint parameters, providing important basis for evaluating system stability and transmission reliability. These timing characteristics directly affect the accuracy of the compensation parameter loading on the curved display panel and the uniformity of the final display effect.

[0089] Example 5: A dynamic update mechanism for the compensation parameter set monitors the temperature rise curve and ambient light intensity change rate of the curved display panel in real time. Temperature rise curve monitoring is achieved through an array of sixteen temperature sensors distributed on the back panel of the curved display panel. The temperature sensor array uses digital temperature sensors, each with a unique I2C bus address, a measurement accuracy of ±0.25 degrees Celsius, and a sampling frequency of four times per second. Ambient light intensity change rate monitoring is achieved through a three-channel ambient light sensor mounted on the bezel of the curved display panel. The ambient light sensor measures the illuminance within the visible light range, with a dynamic range covering 0.01 lux to 100,000 lux, and a data update rate of sixty times per second. The monitoring data is transmitted to the main processor via a serial peripheral interface bus, where the temperature rise rate and ambient light intensity change rate are calculated. The temperature rise rate is calculated as the difference between the current temperature value and the temperature value one minute ago, divided by the time interval. The ambient light intensity change rate is calculated as the difference between the current light intensity value and the light intensity value five hundred milliseconds ago, divided by the time interval.

[0090] When the rate of temperature rise or change in ambient light intensity exceeds a threshold, an incremental update of the compensation benchmark library is triggered. The threshold for the rate of temperature rise is set to five degrees Celsius per minute, and the threshold for the rate of change in ambient light intensity is set to one thousand lux per second. The threshold comparison circuit uses a window comparator design, and the upper and lower thresholds of the window comparator are programmable. After the trigger signal is generated, the system interrupts the current task and starts the incremental update process of the compensation benchmark library. The incremental update process first backs up the current compensation benchmark library to a backup area of ​​non-volatile memory. The backup data includes the timestamp and version information of the compensation parameter set. Then, the system reads the latest monitoring data from the temperature sensor array and the ambient light sensor, which serves as the input parameters for the incremental update.

[0091] A sliding window approach is used to filter out compensation areas requiring recalculation. The sliding window size is set to 25% of the total area of ​​the curved display panel. The sliding window is rectangular and moves in a fixed step size within the two-dimensional coordinate system of the curved display panel. The window movement step size is set to 50% of the window width to ensure that each area is covered by the window. The filtering algorithm calculates the temperature rise difference and light intensity change difference values ​​of all pixels within the window. Areas with difference values ​​exceeding a set threshold are marked as requiring recalculation. The difference threshold is set based on the material properties of the curved display panel: the temperature rise difference threshold is set to 3 degrees Celsius, and the light intensity change difference threshold is set to 500 lux. The filtering process generates a region marker bitmap, where each bit represents whether a region needs recalculation. Local parameter optimization is performed only on the filtered compensation areas to reduce computational load. The local parameter optimization uses a restricted least squares optimization algorithm. The objective function of the optimization algorithm only considers pixels within the marked areas, and the constraint condition keeps the compensation parameters of the unmarked areas unchanged. The optimization process calculates new compensation parameters for the marked areas, which optimize the color difference value and brightness uniformity indices within the marked areas. After calculation, the system merges the new compensation parameters with the original compensation parameters for the unlabeled regions to form a complete updated compensation parameter set. The merged compensation parameter set is then format-converted and written to the compensation baseline library, while simultaneously updating the version number and timestamp. The computational load of the entire local parameter optimization process is approximately 30% of that of the global optimization, significantly reducing the processor's computational burden.

[0092] The display compensation system for the curved display panel includes a memory, a processor, and a computer program stored in the memory and running on the processor. The memory employs a multi-layered architecture, including static random access memory (SRAM), dynamic random access memory (DRAM), and flash memory. SRAM stores intermediate data for real-time calculations, DRAM stores the compensation benchmark library and the current compensation parameter set, and flash memory stores the computer program code and system configuration parameters. The processor is a multi-core architecture with four processing cores: one dedicated to sensor data acquisition and preprocessing, a second responsible for compensation algorithm calculations, a third for graphics rendering, and a fourth for system scheduling and communication interfaces. The processor operates at 2.4 GHz, with each core having an independent L2 cache, and the four cores sharing an 8-megabyte L3 cache. The processor implements the steps of the display compensation method for the curved display panel when executing the computer program, which uses a modular design. The main modules include a sensor data acquisition module, a compensation parameter calculation module, a region marking module, a parameter optimization module, and a driver interface module. The sensor data acquisition module is responsible for reading data from the temperature sensor array and ambient light sensor, and performing digital filtering and outlier removal. The compensation parameter calculation module calculates new compensation parameters based on the input sensor data, using a floating-point accelerator to improve computational efficiency. The region marking module implements a sliding window algorithm to generate a bitmap of regions that need updating. The parameter optimization module executes a local parameter optimization algorithm to calculate new compensation parameters for the marked regions. The driver interface module is responsible for converting the updated compensation parameters into an instruction format recognizable by the display driver chip and transmitting it to the display driver chip via the mobile industrial processor interface.

[0093] During system operation, the sensor data acquisition module continuously monitors changes in environmental conditions. For example, when a user moves the curved display device from an indoor environment to an outdoor environment under direct sunlight, the ambient light sensor detects a rapid increase in light intensity from 500 lux to 80,000 lux within a short period, with a rate of change of 15,000 lux per second, exceeding the set threshold. The system immediately triggers an incremental update process for the compensation benchmark library. The sliding window filtering algorithm identifies the most significant change in light intensity in the central area of ​​the curved display panel and marks this area as requiring recalculation. The local parameter optimization algorithm calculates new brightness compensation parameters for the central area, increasing the backlight brightness in this area to offset the decrease in contrast caused by the enhanced ambient light. The updated compensation parameters are transmitted to the display driver chip through the driver interface module, achieving real-time optimization of the display effect. The entire processing is completed within 200 milliseconds, ensuring a stable and consistent display effect perceived by the user. The system architecture supports multi-task parallel processing; while the local parameter optimization algorithm is performing calculations, the sensor data acquisition module can continue to monitor changes in environmental parameters. The internal message queue mechanism of the processor ensures data synchronization between different modules, avoiding data contention issues. The system also features a watchdog timer to automatically restart the system in case of program crashes or deadlocks. The power management unit dynamically adjusts the processor's operating frequency and voltage based on system load, optimizing energy efficiency while maintaining performance. All critical data undergoes error detection and correction coding when written to memory to ensure data storage reliability. The system uses a hardware encryption module to protect the security of compensation algorithms and configuration parameters, preventing unauthorized access and modification.

[0094] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0095] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A display compensation method for a curved display panel, characterized in that, Includes the following steps: Collect raw brightness data of curved display panels from different viewing angles and establish a compensation benchmark library that includes the relationship between brightness attenuation gradient and radius of curvature; The compensation areas are divided and displayed according to the compensation benchmark library, and the corresponding compensation intensity level of each area is determined. The compensation intensity level includes the main compensation area, the transition compensation area and the edge compensation area. A dynamic compensation strategy is configured for the main compensation area. The dynamic compensation strategy includes a brightness attenuation prediction model and a color coordinate offset correction model. An initial compensation parameter set is generated based on a dynamic compensation strategy, and the effectiveness of the initial compensation parameter set is verified through surface deformation simulation. When the verification is successful, the initial compensation parameter set is loaded into the display driver chip to complete the display compensation of the curved display panel; The surface deformation simulation verification adopts the following steps: A virtual projection surface with the same curvature as the physical panel is constructed. After loading the initial compensation parameter set onto the virtual projection surface, the color uniformity at different viewing distances is simulated. When the color uniformity deviation exceeds the tolerance value, the failure compensation node is marked and fed back to the dynamic compensation strategy; Regenerate and adjust the compensation parameters until the color uniformity of the virtual projection surface meets the preset standard; The iterative optimization process for the adjustment of the compensation parameter includes: Establish an optimization priority queue that includes the location and deviation of failure compensation nodes; Configure and optimize the parameter correction step size for the priority queue in the dynamic compensation strategy; The parameter correction of the main compensation zone and the transition compensation zone is processed sequentially in a layered and gradual manner. An adaptive filtering algorithm is enabled for the edge compensation area to eliminate high-frequency brightness noise.

2. The display compensation method for a curved display panel according to claim 1, characterized in that, The specific method for establishing the aforementioned compensation benchmark library is as follows: Under standard lighting conditions, the axial brightness distribution of the curved display panel was measured at fixed intervals. Extract the critical curvature point in the axial brightness distribution where the brightness value drops beyond a threshold. Establish a mapping table between the radius of curvature and the brightness attenuation gradient based on the critical curvature point; The mapping table is cross-validated with the preset color temperature attenuation coefficient to form the final compensation benchmark library.

3. The display compensation method for a curved display panel according to claim 2, characterized in that, Determining the compensation intensity level includes: The maximum brightness deviation value is obtained by using a bidirectional gradient scanning method in the main compensation area; The average brightness fluctuation amplitude is calculated using a ring sampling method in the transition compensation zone; The discrete point detection method is used to identify the critical value of brightness jump in the edge compensation area; The regional compensation weight coefficient is generated based on the maximum brightness deviation value, the average brightness fluctuation amplitude, and the brightness jump threshold value.

4. The display compensation method for a curved display panel according to claim 3, characterized in that, The execution process of the dynamic compensation strategy includes: Input the current ambient light intensity and panel operating temperature into the brightness decay prediction model; The process of constructing the brightness attenuation prediction model is as follows: the brightness output value of the curved display panel under different combinations of ambient light intensity and panel operating temperature is measured in a controllable environment chamber. The multivariate nonlinear regression equation of brightness attenuation with ambient light intensity and operating temperature is fitted by the least squares method, and the radius of curvature is introduced as a correction factor to generate the brightness attenuation prediction function. The gamma-corrected proportions of the three primary colors are received through a color coordinate offset correction model. The construction process of the color coordinate offset correction model is as follows: under standard color environment, the color coordinate data of the three primary colors after gamma correction in different areas of the curved display panel are collected, the color coordinate offset vector is calculated based on the CIE colorimetry standard, and a polynomial interpolation algorithm is used to establish a mapping relationship model between the color coordinate offset and the viewing angle and curvature. The ambient light intensity, operating temperature and the proportion of the three primary colors are combined to generate the compensation parameter adjustment amount; The luminance gain factor and color gamut mapping matrix in the initial compensation parameter set are updated based on the adjustment amount of the compensation parameters.

5. The display compensation method for a curved display panel according to claim 1, characterized in that, Before loading the display driver chip, the following steps also need to be performed: The final compensation parameter set is converted into a binary instruction format supported by the display driver chip; Verify the consistency between the binary instruction format and the chip register address; Hardware-in-the-loop testing was used to verify whether the instruction execution timing met the clock cycle requirements of the driver chip.

6. The display compensation method for a curved display panel according to claim 5, characterized in that, The specific implementation method of the hardware-in-the-loop test is as follows: Inject timing constraints of the display driver chip into the simulation environment; Monitor the transmission delay of compensation parameter commands during the line scan cycle and frame refresh cycle; When transmission delays cause data loss, insert a blank clock cycle to resynchronize the instruction stream; Record the synchronized instruction stream checksum and compare it with the expected result.

7. The display compensation method for a curved display panel according to claim 6, characterized in that, The method also includes a dynamic update mechanism for the compensation parameter set: Real-time monitoring of the temperature rise curve and ambient light intensity change rate of the curved display panel; When the rate of temperature rise or the change in light intensity exceeds the threshold, an incremental update of the compensation benchmark library is triggered. Use a sliding window method to filter the compensation areas that need to be recalculated; Local parameter optimization is performed only on the selected compensation regions to reduce computational load.

8. A display compensation system for a curved display panel, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the display compensation method for the curved display panel as described in any one of claims 1 to 7.

Images