White field chroma compensation coefficient prediction, optimization method and LED screen display control method

Abstract

The application relates to a white field chroma compensation coefficient prediction, optimization method and LED screen display control method, relates to the technical field of LED display, and discloses a white field chroma compensation coefficient prediction, optimization method and LED screen display control method. The existing white field color deviation correction technology has the problems of display difference between real pixels and virtual pixels, optical drift under long-term operation, high cost and inaccurate color deviation repair. The chroma distribution of each region in the white field display state is measured and analyzed, the compensation function type is selected according to the region characteristics, the internal parameters of the corresponding compensation function are judged according to the color deviation type, and then the local chroma compensation model is established; the compensation coefficient is optimized based on the feedback control closed-loop optimization method, the chroma compensation model continuously matches the actual optical state, and self-learning type display consistency control is realized; the display control of the LED screen is chroma compensation in the signal domain, and no additional hardware current adjustment module is needed. The method disclosed by the application is suitable for white field color deviation correction in Micro / Mini and other LED display systems.

Description

Technical Field

[0001] This invention relates to the field of LED display technology, and also to the field of LED display calibration technology. Background Technology

[0002] LED displays are affected by factors such as differences in driving current and aging of packaging materials. In particular, the light decay and response lag of blue sub-pixels lead to insufficient blue light components and inconsistent luminous intensity. As a result, whether it is a real pixel driving system or a virtual pixel mapping system, LED displays will exhibit a yellowish color under white field display conditions, making the display unclear.

[0003] To address the aforementioned white point color cast issue, current correction techniques mainly include the following categories:

[0004] The first type is the global white balance correction method. This method sets a global RGB gain coefficient in the control system to make the average color temperature of the entire screen close to the target standard point. This type of method is simple and easy to implement, but it can only make overall corrections and cannot adjust for subtle deviations caused by local color casts or virtual pixel remapping, resulting in display differences between real pixels and virtual pixels.

[0005] The second category is point-by-point luminance and chromaticity correction methods. This method collects the luminance and chromaticity data of each pixel on the screen, generates an RGB correction coefficient matrix for static compensation, and then calls the coefficients according to the coordinates to perform point-by-point correction during display. The limitations of this type of method are: the correction coefficients are mostly static data, which cannot be dynamically updated according to the running time and aging status, leading to optical drift; and regardless of whether it is a real pixel or a virtual pixel, obvious color shift is still likely to occur after repair or pixel mapping adjustment.

[0006] The third category is automatic white balance control based on optical feedback. This method uses sensors to monitor the screen's color temperature or spectral changes in real time and dynamically adjusts the RGB channel drive current to achieve a certain degree of automatic correction. The limitations of this method are: it relies on additional hardware, is costly, and can only perform regional-level adjustments, unable to accurately correct color shifts at the pixel level.

[0007] In summary, existing white point color shift correction technologies suffer from problems such as display differences between real and virtual pixels, optical drift under long-term operation, high cost, and inaccurate color shift repair. Summary of the Invention

[0008] Existing white point color shift correction technologies suffer from problems such as display differences between real and virtual pixels, optical drift under long-term operation, high cost, and inaccurate color shift repair. This invention provides the following solution:

[0009] Option 1: A method for predicting colorimetric compensation coefficients, comprising the following steps:

[0010] Step S01: Perform gamma linearization on the original input signal to obtain the input signal; input the input signal onto the LED display screen to be compensated, and collect the chromaticity distribution of the LED display screen; use the standard light source D65 as the white point reference to calibrate the color temperature of the chromaticity distribution to obtain a standardized chromaticity distribution;

[0011] Step S02: Based on the white point reference, perform a difference operation on the standardized chromaticity distribution to obtain the chromaticity offset distribution;

[0012] Step S03: Divide the region into different areas based on the chromaticity offset distribution to obtain the divided regions and their corresponding features; select the compensation function type based on the region features;

[0013] Step S04: Based on the chromaticity offset distribution, obtain the color cast type distribution; based on the color cast type distribution, determine the internal parameters of the corresponding compensation function type;

[0014] Step S05: Based on the compensation function type and internal parameters, obtain the compensation function distribution;

[0015] Step S06: Based on the chromaticity offset distribution and the compensation function distribution, obtain the compensation coefficient distribution of the LED display screen to be compensated.

[0016] Furthermore, in one embodiment of the present invention, the regional characteristics described in step S03 are mild color cast, moderate color cast, significant color cast, or complex color cast.

[0017] Furthermore, in one embodiment of the present invention, the selection of the compensation function type based on the regional characteristics in step S03 is as follows:

[0018] If the region's characteristics are a slight color cast, then choose the quadratic smoothing function;

[0019] If the region has a moderate color cast, then choose the quadratic smoothing function;

[0020] If the region's characteristics are significantly color-biased, then a perceptual nonlinear function should be selected;

[0021] If the region features complex color bias, then a piecewise blending function should be selected.

[0022] Furthermore, in one embodiment of the present invention, the color shift type mentioned in step S04 is yellowish, bluish, reddish, greenish, or no shift.

[0023] Option 2: A method for optimizing the chromaticity compensation coefficient, comprising the following steps:

[0024] Step S11: Perform gamma linearization on the original input signal to obtain the processed signal; input the signal on the LED display screen to obtain the chromaticity distribution; perform color temperature calibration on the chromaticity distribution to obtain the processed chromaticity distribution; map the chromaticity distribution based on the white point reference to obtain the standardized chromaticity distribution.

[0025] Step S12: Perform a difference operation on the white point reference and the normalized chromaticity distribution to obtain the color difference distribution. ;

[0026] Step S13: Obtain the average color difference based on the color difference distribution. ;

[0027] Step S14, when If the compensation coefficient distribution is updated, the standardized chromaticity distribution is compensated based on the updated compensation coefficient distribution to obtain the compensated standardized chromaticity distribution, and steps S12 to S14 are executed; otherwise, the compensation coefficient distribution is used as the optimized compensation coefficient distribution.

[0028] in, It is a value ranging from 1 to 1.5;

[0029] The compensation coefficient distribution is the compensation coefficient distribution obtained by the chromaticity compensation coefficient prediction method described in Scheme 1.

[0030] Furthermore, in one embodiment of the present invention, updating the compensation coefficient distribution in step S14 includes the following steps:

[0031] Step S141, Initialize the cycle number =0;

[0032] Step S142, through

[0033]

[0034] Obtain the The distribution of compensation coefficients after +1 update, where... The learning rate, with a value ranging from 0.1 to 0.3; g (x) For adjustment function, x Indicates the independent variable;

[0035] Step S143: Compensate the standardized chromaticity distribution based on the compensation coefficient to obtain the compensated chromaticity distribution; perform difference and averaging operations on the white point reference and the standardized chromaticity distribution to obtain the average color difference. ;

[0036] Step S144: If each iteration within 10 iteration cycles satisfies... If the last iteration's compensation coefficient distribution is not found, then the compensation coefficient distribution of the last iteration will be used as the updated compensation coefficient distribution; otherwise, steps S141 to S143 will be executed.

[0037] in This is the convergence threshold.

[0038] Furthermore, in one embodiment of the present invention, step S142 described above... g(x) Can be

[0039] .

[0040] Furthermore, in one embodiment of the present invention, the following steps are performed before step S11:

[0041] Periodically monitor the LED chip's luminous flux decay, changes in ambient temperature or humidity, and changes in drive current stability.

[0042] Step S11 is initiated when the luminous flux of the LED chip decreases, the ambient temperature or humidity changes, or the stability of the drive current changes beyond a threshold.

[0043] Option 3: A display control method for an LED screen, comprising an image rendering step and a display output step, wherein in the image rendering step and the display output step, chromaticity compensation processing is performed on each pixel in the image of the input frame, through...

[0044]

[0045] Obtain the signal grayscale value of the pixel after color compensation ,in, For the first in the input frame The original signal grayscale value of each pixel; For the first The chromaticity compensation coefficient for each pixel;

[0046] The chromaticity compensation coefficient is the chromaticity compensation coefficient obtained from Scheme 1 or Scheme 2.

[0047] The white field chromaticity compensation coefficient prediction method, optimization method, and LED display system described in this invention effectively alleviate the problems of existing white field chromaticity correction technologies, such as display differences between real and virtual pixels, optical drift under long-term operation, high cost, and inaccurate chromaticity correction. Specific beneficial effects include:

[0048] 1. The white field chromaticity compensation coefficient prediction method of the present invention measures and analyzes the chromaticity distribution of each region under the white field display state, selects the compensation function type according to the regional characteristics, determines the internal parameters of the corresponding compensation function according to the color cast type, and then establishes a local chromaticity compensation model to perform targeted compensation and achieve precise regional adjustment.

[0049] 2. The optimization method described in this invention is a closed-loop optimization method based on feedback control. It utilizes color difference feedback to achieve automatic iterative updates of compensation parameters, maintaining long-term color consistency. Furthermore, the closed-loop optimization mechanism not only corrects color unevenness in the initial production stage but also automatically compensates for color deviation problems caused by LED chip luminous flux attenuation, changes in ambient temperature or humidity, and decreased driving current stability during long-term operation. Through periodic detection and dynamic updates, the color compensation model continuously conforms to the actual optical state, achieving self-learning display consistency control. Compared with traditional static calibration methods, this invention eliminates the need for manual recalibration or external adjustments, automatically maintaining white field consistency and color temperature stability during operation.

[0050] 3. The LED screen display control method described in this invention is suitable for LED screens that perform color compensation in the signal domain. In traditional solutions, the LED display system driver chip adjusts the current amplitude or PWM duty cycle of each sub-pixel through a DAC to correct brightness. However, its control accuracy is limited by the hardware resolution (usually 10-12 bits) and cannot achieve independent correction at the logical pixel level. This invention combines the white field color compensation coefficient prediction and optimization method proposed in this invention into the grayscale driving module of the display control system to predict, optimize, and perform color compensation of the compensation coefficient. It does not require an additional hardware current adjustment module, is applicable to Micro, Mini, and conventional LED displays, supports rapid color matching after partial repairs, and can achieve intelligent color maintenance by combining existing point-by-point correction data.

[0051] The method described in this invention is applicable to white field color deviation correction in Micro / Mini and other LED display systems. Attached Figure Description

[0052] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0053] Figure 1 This is the color compensation coefficient prediction method described in Implementation Method 1.

[0054] Figure 2 This is the color compensation coefficient optimization method described in Implementation Method 5. Detailed Implementation

[0055] Various embodiments of the present invention will now be clearly and completely described with reference to the accompanying drawings. The embodiments described with reference to the drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0056] Implementation Method 1: The colorimetric compensation coefficient prediction method described in this implementation method is as follows... Figure 1 As shown, it includes the following steps:

[0057] Step S01: Perform gamma linearization on the original input signal to obtain the input signal; input the input signal onto the LED display screen to be compensated, and collect the chromaticity distribution of the LED display screen; use the standard light source D65 as the white point reference to calibrate the color temperature of the chromaticity distribution to obtain a standardized chromaticity distribution;

[0058] Step S02: Based on the white point reference, perform a difference operation on the standardized chromaticity distribution to obtain the chromaticity offset distribution;

[0059] Step S03: Divide the region into different areas based on the chromaticity offset distribution to obtain the divided regions and their corresponding features; select the compensation function type based on the region features;

[0060] Step S04: Based on the chromaticity offset distribution, obtain the color cast type distribution; based on the color cast type distribution, determine the internal parameters of the corresponding compensation function type;

[0061] Step S05: Based on the compensation function type and internal parameters, obtain the compensation function distribution;

[0062] Step S06: Based on the chromaticity offset distribution and the compensation function distribution, obtain the compensation coefficient distribution of the LED display screen to be compensated.

[0063] In this embodiment, the original signal mentioned in step S01 is preferably a standard white field image signal.

[0064] The preferred digital drive value for the standard white field image signal is RGB = (255, 255, 255).

[0065] In this embodiment, the collection range mentioned in step S01 can be the entire screen or part of the LED display screen.

[0066] In this embodiment, the LED display screen is an LED display screen with different modules. By performing gamma linearization processing on the original signal, the influence of the nonlinear brightness response of the LED display screen on the measurement accuracy can be eliminated.

[0067] In this embodiment, it is preferable to use a high-precision camera or colorimeter for data acquisition in step S01.

[0068] In this embodiment, step S01 preferably uses the standard light source D65 in the CIE1931 color space as the white point reference.

[0069] In this embodiment, the chromaticity shift distribution mentioned in step S02 ( , Preferred

[0070]

[0071] Obtain, among which, ( , () is the white point reference, ( , ) is the location ( , The normalized chromaticity of the pixels.

[0072] In this embodiment, step S02 preferably involves performing a local weighted average on the chromaticity offset distribution to obtain a new chromaticity offset distribution. This technique is used to suppress the influence of isolated abnormal pixels and improve the stability of recognition.

[0073] In this embodiment, step S04,

[0074] When the color cast is yellowish. Increase the output of the blue channel and moderately reduce the red and green components;

[0075] When the color cast type is bluish, increase and Reduce the brightness of the corresponding channel;

[0076] For areas that are too red or too green, the system uses directional weighting to achieve reverse compensation.

[0077] This process is equivalent to dynamically weighting and balancing the luminous energy of LED sub-pixels within the signal domain, thereby achieving white field equalization without changing the hardware structure.

[0078] The color compensation coefficient prediction method described in this embodiment is a method that can predict the color deviation of different regions, thereby achieving refined compensation.

[0079] Gamma linearization is used to eliminate the impact of the nonlinear brightness response of the LED display on measurement accuracy. Color temperature calibration ensures that subsequent color cast analysis and compensation calculations are based on a unified physical standard, and unifies measurement data from different modules and regions into the same reference system, thereby improving compensation accuracy.

[0080] By acquiring the chromaticity of each pixel in the white field display state, the chromaticity offset of each pixel relative to the target white point is obtained, thus achieving pixel-level color deviation recognition.

[0081] Based on the recognition results, the system determines the type of color cast and regional characteristics, and then selects the appropriate compensation strategy to achieve adaptive color orientation repair.

[0082] Implementation Method 2: This implementation method further defines the color compensation coefficient prediction method described in Implementation Method 1. In this implementation method, the regional characteristics described in step S03 are mild color cast, moderate color cast, significant color cast, or complex color cast.

[0083] In this embodiment, the regional features are specifically:

[0084] For areas with slight color cast, the chromaticity shift is small, indicating a limited color cast relative to the target white point. Typically, the blue shift is less than a set threshold (e.g., 0.05), and the shifts in other color channels are also within acceptable ranges. Chromaticity compensation in such areas can be handled using a quadratic smoothing function, aiming to achieve fine-tuning of the chromaticity.

[0085] In the moderate color cast region, the chromaticity shift is relatively large, but still within a reasonable range, typically manifesting as varying degrees of shift across multiple color channels. In this case, an adaptive compensation function, such as a quadratic smoothing function or a perceptual nonlinear function, is required to achieve smoother and more adaptive chromaticity correction.

[0086] For regions with significant color cast, the chromaticity shift deviates markedly from the target white point, typically exhibiting significant deviations across multiple color channels, particularly with a noticeable attenuation in the blue channel. In such cases, a perceptual nonlinear function is more suitable because it can simulate the nonlinear perceptual characteristics of color difference in the human eye, thereby effectively correcting the chromaticity deviation.

[0087] For complex color-skewed regions, the distribution of chromaticity shift is quite intricate, potentially containing multiple localized color-skewed points, and the overall color shift is difficult to correct using simple linear or nonlinear compensation functions. In such cases, piecewise blending functions are recommended, which perform staged compensation based on the degree of color shift in different regions to ensure the uniformity and accuracy of chromaticity correction.

[0088] This embodiment further defines step S03 and provides an example of step S03. This method supports local processing by judging regional characteristics of different regions, making the calculation of compensation coefficients more accurate and efficient.

[0089] Implementation Method 3: This implementation method further defines the chromaticity compensation coefficient prediction method described in Implementation Method 1. In this implementation method, the compensation function type is selected based on the regional characteristics in step S03. The selection process is as follows:

[0090] If the region's characteristics are a slight color cast, then choose the quadratic smoothing function;

[0091] If the region has a moderate color cast, then choose the quadratic smoothing function;

[0092] If the region's characteristics are significantly color-biased, then a perceptual nonlinear function should be selected;

[0093] If the region features complex color bias, then a piecewise blending function should be selected.

[0094] In this embodiment, the linear function refers to Compensation coefficient of the pixel at the location pass

[0095]

[0096] Obtain, where the compensation coefficient middle, b For color channels, b When r is used, it represents the red channel. b When it is 'g', it represents the green channel. b When the value is 'b', it represents the blue channel; For vertical weights, Horizontal weights.

[0097] In this embodiment, the quadratic smoothing function refers to Compensation coefficient of the pixel at the location pass

[0098]

[0099] Obtain, among which, It is a quadratic proportionality coefficient used to control the overall compensation gain.

[0100] In this embodiment, the perceptual nonlinear function refers to Compensation coefficient of the pixel at the location pass

[0101]

[0102] Obtain, among which, This is the kurtosis coefficient of the function, used to determine the response speed; For the perception threshold; This represents the color difference amplitude.

[0103] In this embodiment, the piecewise hybrid function refers to Compensation coefficient of the pixel at the location pass

[0104]

[0105] Obtain, among which To achieve the perceptible color difference threshold, The threshold for the intermediate transition zone. This is the segmentation ratio parameter.

[0106] In this embodiment, the compensation function type can also employ a nonlinear compensation model, a neural network prediction model, and a lookup table (LUT) method, specifically:

[0107] The nonlinear compensation model adjusts the compensation coefficients using a quadratic or piecewise function based on the color deviation, making the restoration effect more consistent with the characteristics of human eye perception.

[0108] The neural network prediction model learns from different pixel aging states and color cast trends using an AI model, predicts the optimal compensation coefficient, and achieves intelligent dynamic correction.

[0109] The LUT method involves pre-establishing a correspondence table between chromaticity offset and compensation coefficients, and directly looking up the table to obtain compensation parameters during runtime, thereby improving computational efficiency.

[0110] This embodiment further defines step S03 and provides an example of it. The linear function is used for rapid correction of areas with slight color cast; the quadratic smoothing function is used for areas with uniform brightness and smooth color cast distribution, avoiding overcompensation at the edges; it is suitable for areas with slight and moderate color cast. In such areas, the chromaticity shift is small or moderate, the deviation is relatively uniform, and the compensation requirement is relatively stable. The quadratic smoothing function effectively avoids overcompensation during the chromaticity compensation process by performing a second smoothing process on the chromaticity shift, especially in areas with slight color cast, it can maintain a natural color transition through smooth transition and avoid causing obvious color abrupt changes.

[0111] The perceptual nonlinear function described above is used in scenarios with significant color cast. It simulates the nonlinear perceptual characteristics of the human eye regarding color differences and is suitable for areas with significant color cast. In such areas, the chromaticity shift deviates significantly from the target white point, typically manifested as a significant attenuation of a certain color channel (such as blue), and this color cast usually exhibits a nonlinear distribution characteristic. The perceptual nonlinear function can adjust according to the nonlinear perceptual characteristics of the human eye regarding color differences, making the compensation more consistent with the laws of visual perception, thereby improving the realism of the compensation effect. The beneficial effect of this function lies in its ability to simulate the nonlinear response of the human eye to color differences, making the chromaticity correction process more natural and avoiding the visual discomfort caused by linear correction. Especially in areas with significant blue light attenuation or large changes in color saturation, it can effectively avoid abrupt color changes and ensure the overall consistency of the image.

[0112] The piecewise blending function is used in regions with complex color casts. It maintains stability under low color cast conditions and enhances compensation intensity under high color cast conditions, making it suitable for complex color cast areas. In complex color cast areas, the distribution of chromaticity shift is usually uneven, potentially containing multiple localized areas with significant color casts, and the overall color cast is difficult to correct with a single compensation function. The piecewise blending function divides the entire region into multiple sub-regions and applies different compensation strategies to the chromaticity shift characteristics of each sub-region, thereby achieving refined region-level compensation. The beneficial effect of this function lies in its high adaptability, allowing for flexible segmented adjustments based on the degree of color cast in different regions, ensuring the uniformity and accuracy of the compensation effect, especially showing significant advantages in large-screen displays and complex color correction scenarios.

[0113] By selecting appropriate compensation functions based on the chromaticity offset and color cast type of different regions, the problems of uneven chromaticity, inaccurate compensation, and poor display consistency in existing technologies can be effectively solved.

[0114] Implementation Method 4: This implementation method further defines the color compensation coefficient prediction method described in Implementation Method 1. In this implementation method, the color cast type in step S04 is yellowish, bluish, reddish, greenish, or no cast.

[0115] In this embodiment, it is preferable to base it on the chromaticity shift distribution ( , To obtain the color cast type distribution, the determination method is as follows:

[0116] like and If so, the offset type is yellowish;

[0117] like and If so, the offset type is bluish;

[0118] like If so, the offset type is reddish;

[0119] like If so, the offset type is greenish;

[0120] like and If the offset is close to zero, the offset type is no offset.

[0121] In this embodiment, it is preferable to use multi-grayscale sampling method, spectral feature analysis method, and camera visual detection method for color cast type identification, specifically as follows:

[0122] The multi-grayscale sampling method: collects the color change trend of pixels under different grayscale inputs, uses a multi-point fitting model to identify the color deviation direction, and more accurately reflects the spectral attenuation law;

[0123] The spectral feature analysis method: directly measures the energy distribution of the RGB channels using a narrowband spectrometer to identify the blue light attenuation ratio from a physical perspective;

[0124] The camera visual detection method: uses a high-resolution imaging device to capture the displayed image and uses a color mapping algorithm to extract the yellowish areas;

[0125] The above methods can be used independently or in combination to adapt to different accuracy requirements and hardware configurations.

[0126] This embodiment further defines step S04 and provides an example of step S04. The chromaticity shift reflects the relative difference in luminous efficiency of the blue, green, and red channels. The yellowish tint is generally due to insufficient blue light caused by blue light attenuation or reduced driving current. The bluish tint is generally due to excessive red light caused by overdriving of the red and yellow channels or light leakage from the encapsulation. The redish tint is caused by uneven red light. The greenish tint is caused by excessive blue light. By judging the above-mentioned shift types and their causes, a basis is provided for subsequent compensation.

[0127] While reddish or greenish tints are rare, they can occur when splicing different batches of modules, so they are categorized to maintain uniform white field across the entire screen.

[0128] Implementation Method 5: A method for optimizing the chromaticity compensation coefficient as described in this implementation method, such as... Figure 2 As shown, it includes the following steps:

[0129] Step S11: Perform gamma linearization on the original input signal to obtain the processed signal; input the signal on the LED display screen to obtain the chromaticity distribution; perform color temperature calibration on the chromaticity distribution to obtain the processed chromaticity distribution; map the chromaticity distribution based on the white point reference to obtain the standardized chromaticity distribution.

[0130] Step S12: Perform a difference operation on the white point reference and the normalized chromaticity distribution to obtain the color difference distribution. ;

[0131] Step S13: Obtain the average color difference based on the color difference distribution. ;

[0132] Step S14, when If the compensation coefficient distribution is updated, the standardized chromaticity distribution is compensated based on the updated compensation coefficient distribution to obtain the compensated standardized chromaticity distribution, and steps S12 to S14 are executed; otherwise, the compensation coefficient distribution is used as the optimized compensation coefficient distribution.

[0133] in, It is a value ranging from 1 to 1.5;

[0134] The compensation coefficient distribution is the compensation coefficient distribution obtained by any one of the chromaticity compensation coefficient prediction methods in Implementation Methods 1 to 4.

[0135] In this embodiment, the color distribution in step S11 is preferably acquired by a built-in or external optical sensor.

[0136] In this embodiment, the color difference distribution in step S12 Preferred Pass

[0137]

[0138] Obtain, among which, , , The white point serves as the reference. , , This refers to the standardized chromaticity distribution in Lab space.

[0139] The chromaticity compensation coefficient optimization method described in this embodiment is a closed-loop optimization method based on feedback control. Compared with the traditional static correction method, this embodiment does not require manual recalibration or external debugging, and can automatically maintain white field consistency and color temperature stability during operation. It can not only correct the chromaticity unevenness in the initial production stage, but also compensate for color deviation problems caused by LED chip luminous flux attenuation, changes in ambient temperature or humidity, and decreased stability of driving current during long-term operation.

[0140] By linearizing the original signal and chromaticity distribution with gamma and calibrating the color temperature, the color difference results obtained during the differential operation are ensured to have objective physical meaning. This avoids systematic errors caused by differences in measurement equipment, optical paths, or gamma response, and provides an accurate data basis for subsequent compensation parameter calculations.

[0141] Implementation Method Six: This implementation method further defines the chromaticity compensation coefficient optimization method described in Implementation Method Five. In this implementation method, updating the compensation coefficient distribution in step S14 includes the following steps:

[0142] Step S141, Initialize the cycle number =0;

[0143] Step S142, through

[0144]

[0145] Obtain the The distribution of compensation coefficients after +1 update, where... The learning rate, with a value ranging from 0.1 to 0.3; g (x) For adjustment function, x Indicates the independent variable;

[0146] Step S143: Compensate the standardized chromaticity distribution based on the compensation coefficient to obtain the compensated chromaticity distribution; perform difference and averaging operations on the white point reference and the standardized chromaticity distribution to obtain the average color difference. ;

[0147] Step S144: If each iteration within 10 iteration cycles satisfies... If the last iteration's compensation coefficient distribution is not found, then the compensation coefficient distribution of the last iteration will be used as the updated compensation coefficient distribution; otherwise, steps S141 to S143 will be executed.

[0148] in This is the convergence threshold.

[0149] This embodiment further defines step S14 and provides an example of step S14. The method is a parameter self-updating mechanism based on iterative learning, which makes the chromaticity compensation coefficient continuously fit the actual optical state and realizes self-learning display consistency control.

[0150] Implementation Method Seven: This implementation method further defines the chromaticity compensation coefficient optimization method described in Implementation Method Five. In this implementation method, step S142... g(x) Can be

[0151] .

[0152] This embodiment further defines step S142, and provides an example of the adjustment function in step S142. It is a linear incremental adjustment function. This function has low computational cost and the adjustment process is stable and predictable. It is suitable for maintaining steady state when the chromaticity offset length is small and changes slowly.

[0153] This is a gradient descent type adjustment function. This function has a clear direction and high efficiency, and is suitable for complex chromaticity shifts caused by multiple factors.

[0154] As an exponentially decreasing function, this function adjusts quickly and is highly stable, making it suitable for rapid recovery of sudden chromaticity shifts.

[0155] The above three adjustment functions can achieve fast, accurate and stable optimization of compensation coefficients, which is beneficial for color compensation.

[0156] Implementation Method Eight: This implementation method further defines the colorimetric compensation coefficient optimization method described in Implementation Method Five. In this implementation method, the following steps are performed before step S11:

[0157] Periodically monitor the LED chip's luminous flux decay, changes in ambient temperature or humidity, and changes in drive current stability.

[0158] Step S11 is initiated when the luminous flux of the LED chip decreases, the ambient temperature or humidity changes, or the stability of the drive current changes beyond a threshold.

[0159] This embodiment further defines the color compensation coefficient optimization method. This embodiment adds conditions for the optimization parameters, namely: the application does not require real-time parameter optimization, and the process of starting the optimization parameters is determined based on the operating status of the LED, thereby reducing the amount of data processing and improving work efficiency.

[0160] Implementation Method Nine: The LED screen display control method described in this implementation method includes an image rendering step and a display output step. In the image rendering step and the display output step, chromaticity compensation processing is performed on each pixel in the image of the input frame.

[0161]

[0162] Obtain the signal grayscale value of the pixel after color compensation ,in, For the first in the input frame The original signal grayscale value of each pixel; For the first The chromaticity compensation coefficient for each pixel;

[0163] The chromaticity compensation coefficient is the chromaticity compensation coefficient obtained by any one of the methods described in Embodiments 1 to 8.

[0164] In this embodiment, the display control method of the LED screen preferably adopts a parallel pipeline structure and a pixel-by-pixel caching method, so that the compensation calculation does not introduce perceptible delay, which is suitable for high refresh rate display scenarios and allows the calculation process to be completed in real time within the image frame refresh cycle (generally 16.7 ms or less).

[0165] In this embodiment, the current channel fine-tuning method, the time domain modulation method, and the color mapping transformation method can be used, specifically:

[0166] The current channel fine-tuning method achieves color separation enhancement by changing the channel current of the LED driver chip;

[0167] The time-domain modulation method: Under the same grayscale input conditions, dynamic blue light supplementation is achieved by extending the blue light emission time period or shortening the red and yellow channel timing.

[0168] The color mapping transformation method involves performing a color space transformation (e.g., from RGB to Lab space) on the displayed image data layer and then performing compensation operations in the perceptually uniform space.

[0169] The LED screen display control method described in this embodiment is suitable for LED display systems that perform color compensation in the signal domain. Corresponding to the grayscale driving stage of the display control system, it does not require an additional hardware current adjustment module. In traditional solutions, the LED display system driver chip adjusts the current amplitude or PWM duty cycle of each sub-pixel through DAC to correct the brightness, but its control accuracy is limited by the hardware resolution (usually 10~12 bits) and cannot achieve independent correction at the logical pixel level.

[0170] This implementation method predicts, optimizes, and performs color compensation by using the grayscale driving module of the display control system. This is equivalent to performing color compensation on the signal, which is fundamentally different from the traditional current domain adjustment method. It realizes real-time compensation processing of the input signal during image rendering and display output without the need for an additional hardware current adjustment module.

[0171] This implementation achieves equivalent current control through digital correction of the input grayscale signal: increasing the input signal value is equivalent to extending the effective light emission time of the channel or increasing its driving current; decreasing the input signal value is equivalent to compressing the light emission period or reducing the current amplitude. This method can achieve precise control of the light emission energy of LED sub-pixels through algorithms without changing the hardware structure.

[0172] Inside the controller, the correction signal for each logical pixel is mapped to a physical pixel channel via a mapping matrix. For displays using virtual pixel or sub-pixel grouping drive structures, the system performs interpolation or weighted allocation during the mapping stage to smoothly distribute the logical compensation amount to adjacent physical pixels, avoiding boundary artifacts.

Claims

1. A method of chroma compensation coefficient prediction, the method comprising: The method comprises the following steps: Step S01, performing gamma linearization processing on a to-be-input original signal to obtain an input signal; inputting the input signal on an LED display screen to be compensated, and collecting a chromaticity distribution of the LED display screen; Taking standard light source D65 as a white point reference, performing color temperature calibration on the chromaticity distribution to obtain a standardized chromaticity distribution; Step S02, performing difference operation on the standardized chromaticity distribution based on the white point reference to obtain a chromaticity offset distribution; Step S03, dividing different regions based on the chromaticity offset distribution to obtain divided regions and corresponding region characteristics; and selecting a compensation function type based on the region characteristics; Step S04, obtaining a color cast type distribution based on the chromaticity offset distribution; and judging internal parameters of the corresponding compensation function type based on the color cast type distribution; Step S05, obtaining a compensation function distribution based on the compensation function type and the internal parameters; Step S06, obtaining a compensation coefficient distribution of the LED display screen to be compensated based on the chromaticity offset distribution and the compensation function distribution.

2. The chroma compensation coefficient prediction method of claim 1, wherein, The region characteristics in step S03 are slight color cast, moderate color cast, significant color cast or complex color cast.

3. The chroma compensation coefficient prediction method of claim 1, wherein, In step S03, the compensation function type is selected based on the region characteristics, and the selection process is as follows: If the region characteristics are slight color cast, a quadratic smoothing function is selected; If the region characteristics are moderate color cast, a quadratic smoothing function is selected; If the region characteristics are significant color cast, a perceptual nonlinear function is selected; If the region characteristics are complex color cast, a segmented mixed function is selected.

4. The chroma compensation coefficient prediction method of claim 1, wherein, The color cast types in step S04 are yellow cast, blue cast, red cast, green cast or no offset.

5. A method of optimizing color compensation coefficients, wherein the distribution of the compensation coefficients is obtained by any one of the color compensation coefficient prediction methods of claims 1 to 4, characterized in that, The method comprises the following steps: Step S11, performing gamma linearization processing on a to-be-input original signal to obtain a signal after the gamma linearization processing; Inputting the signal on an LED display screen to obtain a chromaticity distribution; Performing color temperature calibration on the chromaticity distribution to obtain a processed chromaticity distribution; Performing mapping on the chromaticity distribution based on a white point reference to obtain a standardized chromaticity distribution; Step S12, differentiating the white point reference and the normalized chrominance distribution to obtain a color difference distribution wherein, ij is a pixel position; Step S13, obtaining average color difference based on the color difference distribution ; Step S14, when the compensation coefficient distribution is updated, and the normalized chrominance distribution is compensated based on the updated compensation coefficient distribution to obtain a compensated normalized chrominance distribution and perform steps S12-S14; otherwise, the compensation coefficient distribution is taken as the optimized compensation coefficient distribution. wherein is a value in the range 1-1.

5.

6. The method of optimizing color compensation coefficients according to claim 5, wherein, Before step S11, the following steps are performed: Periodically detecting LED chip luminous flux attenuation, environmental temperature or humidity change, or driving current stability change; When the LED chip luminous flux attenuation, environmental temperature or humidity change, or driving current stability change exceeds a threshold value, step S11 is started.

7. A display control method of an LED screen, comprising an image rendering step and a display output step, wherein the chrominance compensation coefficient in the display control method is the chrominance compensation coefficient obtained by any one of the methods of claims 1 to 5, characterized in that, In the image rendering step and the display output step, each pixel in an image in an input frame is subjected to chromaticity compensation processing by signal gray value of a pixel after chroma compensation is obtained wherein, is an original signal gray value of a pixel in the input frame; is a chroma compensation coefficient of the pixel. is an original signal gray value of a pixel in the input frame; is a chroma compensation coefficient of the pixel.

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