Backlight source partition dimming control method

Abstract

The invention discloses a backlight source partition dimming control method, and belongs to the field of data security, and the control method comprises the following specific steps: I, analyzing the data features of an input image, recognizing a highlight motion region, and predicting and optimizing a current backlight partition dimming strategy; iI, adjusting a backlight area in real time according to the brightness, contrast and color change of the input content, and constructing a multi-level light diffusion coupling model after partition adjustment; the backlight partition can be adaptively adjusted according to the image content, the local contrast ratio and energy efficiency are improved, more uniform light distribution is achieved, the problems of halo and uneven brightness are reduced, the dimming precision is improved, the service life of a light source is prolonged, the light overflow effect of the partition edge is effectively reduced, and the picture layering sense is improved; the subjective image quality experience is effectively improved, the light efficiency utilization rate is improved, meanwhile, image quality and power consumption optimization can be considered, brightness adjustment better conforms to human eye visual characteristics, visual fatigue is reduced, and it is ensured that the optimal image quality is maintained under the lowest power consumption.

Description

Technical Field

[0001] The present invention relates to the field of data security, and particularly to a backlight source zoned dimming control method. Background Art

[0002] In recent years, with the rapid development of display technology, and the increasing demands for high dynamic range (HDR) display, ultra-high definition (UHD), and low-power display, the requirements for backlight regulation of display devices have been increasing day by day. Due to the lack of independent control ability for local areas, the traditional global backlight dimming method is prone to problems such as insufficient black level performance, decreased contrast, and excessive energy consumption. In contrast, the zoned dimming technology can significantly improve the contrast of images and reduce backlight power consumption by independently controlling the backlight areas. However, the current mainstream zoned dimming technology still has problems such as difficulty in adapting to different image contents for self-adjustment, resulting in insufficient brightness control accuracy; not fully considering the perception characteristics of the human eye for different brightness and contrast, affecting the subjective visual experience; adopting a single LED backlight scheme, it is difficult to achieve the best balance between energy efficiency and image quality, and high-density LED backlight zoned dimming will bring significant heat generation problems when increasing the brightness, affecting the system stability and lifespan. Therefore, it is particularly important to invent a backlight source zoned dimming control method.

[0003] After retrieval, Chinese Patent No. CN117935743A discloses a backlight source zoned dimming control method, circuit, and LED driving chip. Although this invention can achieve the adaptive numbering of cascaded LED driving chips, flexibly set the start / end chip numbers for the brightness update command, so that only the LED driving chips within the number range perform brightness updates, realizing the brightness refresh of single / batch LED driving chips, and the dimming method is more flexible and efficient, but the backlight zones cannot be adaptively adjusted according to the image content, reducing the local contrast and energy efficiency, unable to evenly distribute light, prone to problems such as halos and uneven brightness, and at the same time reducing the dimming accuracy and shortening the lifespan of the light source. In addition, the existing backlight source zoned dimming control methods cannot improve the subjective image quality experience, reduce the light efficiency utilization rate, cannot balance the optimization of image quality and power consumption, and increase visual fatigue. Therefore, we propose a backlight source zoned dimming control method. Summary of the Invention

[0004] The purpose of the present invention is to solve the defects existing in the prior art, and to propose a backlight source zoned dimming control method.

[0005] To achieve the above purpose, the present invention adopts the following technical solutions: A backlight source zoned dimming control method, and the specific steps of this control method are as follows: Ⅰ. Analyze the characteristics of the input image data, identify the high-brightness moving areas, and predict and optimize the current backlight zoned dimming strategy; II. According to the brightness, contrast, and color changes of the input content, adjust the backlight area in real time. After the zonal adjustment, construct a multi-level light diffusion coupling model; III. Analyze the sensitivity of the human eye to different brightness and contrast levels, adjust the local brightness weights, and optimize the brightness mapping relationship of the backlight zones; IV. Allocate the optimal light source type and driving method according to the scene characteristics, monitor the temperature of the backlight module in real time, and dynamically adjust the driving current based on the monitored data; V. Adopt an independent dimming control strategy for different backlight areas according to the optimized brightness zones and dimming control strategy; VI. Continuously monitor the output image quality parameters, re-predict the backlight zone dimming strategy in combination with the scene, and form an adaptive closed-loop control.

[0006] As a further solution of the present invention, the specific steps of identifying the high-light motion area in step I are as follows: S1.1: Perform brightness normalization processing on each frame of input image data. Then, randomly select a set of gamma correction coefficients within the range of [0.8, 2.2], adjust the contrast of the image according to the selected gamma correction coefficients, and then divide the corrected image data into multiple non-overlapping sub-regions; S1.2: Calculate the gray histogram of each sub-region to obtain the gray distribution of each sub-region, calculate the cumulative distribution value CDF of the histogram of each sub-region. Then, clip the CDF value based on a preset contrast limit threshold, and calculate the pixel values after histogram equalization of the corresponding sub-regions according to the clipped CDF values. Then, smooth the boundaries of the equalized sub-regions through bilinear interpolation and reconstruct the complete image; S1.3: Use the white balance algorithm based on the gray world assumption to keep the colors consistent between different frames of image data. Then, remove the noise of the image data through non-local means filtering, calculate the optical flow field between the processed frames of image data to obtain the motion information of the pixel points, and use the optical flow field information higher than the preset high-light brightness threshold and motion amplitude threshold as the high-light motion area and mark it.

[0007] As a further solution of the present invention, the specific steps of predicting and optimizing the current backlight zone dimming strategy in step I are as follows: S2.1: Initialize the strategy adjustment model based on the LSTM model architecture, including the input layer, LSTM layer, and output layer. Extract the temporal features of each frame of preprocessed image data through the CNN network, associate each temporal feature with its corresponding high-light motion area, and then divide the generated temporal features into a training set and a test set, and then input the training set into the strategy adjustment model; S2.2: The input layer of the policy adjustment model receives the training set data, divides the training set data into multiple batches of training groups, and sequentially propagates the training groups forward to the LTSM layer through forward propagation. Then, each group of training groups is processed sequentially through the forget gate, input gate, and output gate in the LSTM layer. The Softmax function of the fully connected layer is used to perform non-linear processing on the final result output by the output gate to obtain the finally predicted scene recognition result and the highlight motion area within the preset time interval, and the result is output through the output layer; S2.3: Calculate the true scene and highlight motion area labels of the current frame and the predicted scene recognition result and highlight motion area through the cross-entropy loss function. Input the calculated loss value from the output layer of the policy adjustment model, perform backpropagation based on the chain rule, and sequentially calculate the gradients of the loss value with respect to each network layer of the policy adjustment model. Then, adjust the parameters of each network layer through the Adam optimizer; S2.4: After each round of training, evaluate the accuracy, precision, recall rate, and F1 score of the policy adjustment model through the test set data and generate a comprehensive score. If the score does not reach the preset score threshold, retrain and test the policy adjustment model until the model loss value converges to the preset range, and then stop training; S2.5: Use the external memory module to record the historical task information of the policy adjustment model, that is, the features of known scenes and model parameters. Then, perform temporal modeling on each image data of past frames through the policy adjustment model, and use the forward propagation algorithm to generate the predicted results of the highlight motion area and the scene recognition result within the preset time period. Calculate the brightness gain of each partition according to the recognized scene category and the predicted highlight area, and adjust and optimize the original backlight partition dimming strategy based on the calculated brightness gain; S2.6: When the loss value between the output result and the actual result of the policy adjustment model exceeds the preset threshold, calculate the cosine similarity between each historical task information stored in the external memory module and the current image data, and obtain the enhanced hidden state from the historical task information with the highest cosine similarity. Based on the selected enhanced hidden state and the current loss value, recalculate the loss value, and calculate the gradient of the current task based on the latest loss value. Update the parameters of the policy adjustment model based on the calculated gradient, and update the historical task information recorded in the memory module.

[0008] As a further solution of the present invention, the strategy adjustment model in S2.1 constructs a model for corresponding levels based on the input layer, multiple LSTM layers, and output layer of the LSTM model, and the processing flow of each layer of the strategy adjustment model is that the input layer receives the extracted feature data, and the input gate, forget gate, and output gate of the multiple LSTM layers process the extracted groups of feature data and generate corresponding hidden states. Then, the fully connected unit in the output layer performs non-linear processing on the hidden states through the LeakyReLU activation function to generate the brightness coefficient, dimming direction suggestion, and dynamic dimming amplitude of the backlight partition, and outputs them.

[0009] As a further solution of the present invention, the specific steps for real-time adjusting the backlight area in step II are as follows: S3.1: After adjusting the brightness of each partition through the optimized backlight partition dimming strategy, calculate the local brightness requirement of the current frame image through weighted average, then divide the entire image into multiple grid areas, and calculate the average brightness of each grid; S3.2: Set a brightness threshold. If the grid average brightness is higher than the brightness threshold, use honeycomb-like dense partitioning to subdivide the area. If the grid average brightness is lower than the brightness threshold, merge adjacent areas and use sparse rectangular partitioning. After traversing all grids, obtain the number of adjusted backlight partitions; S3.3: After the dynamic partition adjustment is completed, extract the center points of each partition, and each center point represents a backlight adjustment area. Divide the entire screen area into multiple polygon partitions through the Voronoi diagram algorithm; S3.4: Calculate the centroid of each partition through the Lloyd iteration algorithm, and use the new centroid as the new Voronoi generation point. Then repeat the Voronoi calculation and Lloyd iteration until the position change value of the Voronoi generation point converges within a preset range, and output the finally generated irregular backlight partition.

[0010] As a further solution of the present invention, the specific calculation formula for the weighted average in S3.1 is as follows: ; In the formula, represents the brightness value at pixel point ; represents the channel weight; represents the value of the pixel point on the color channel (red R, green G, blue B); The specific calculation formula for the Voronoi diagram algorithm in S3.3 is as follows: ; In the formula, represents the set of real numbers on a two-dimensional plane; p represents a two-dimensional coordinate point in the two-dimensional plane; represents the distance from point p to the center point ; represents the distance from point p to the center point ; The specific calculation formula of the Lloyd iteration algorithm described in S3.4 is as follows: ; In the formula, represents the center point of the th region; represents the th region segmented by the Voronoi diagram algorithm; represents the th two-dimensional coordinate point in the

[0011] As a further solution of the present invention, the specific steps of constructing the multi-level light diffusion coupling model described in step II are as follows: S4.1: Set the initial radiation intensity distribution of the light source of the backlight module. Then, the light emitted by the light source enters the light guide plate, and the multiple scattering and absorption behaviors of light in the light guide plate are calculated through the radiation transfer equation to obtain the light diffusion process; S4.2: Use the prisms and microstructures of the diffusion film of the light guide plate to perform additional direction adjustment and intensity redistribution on the light, and model it through the transmission function. Then, model the light homogenization process of the diffusion film through the diffusion equation, and comprehensively combine the light diffusion processes to construct a multi-level light diffusion coupling model; S4.3: Based on the multi-level light diffusion coupling model, discretize it by randomly sampling the light emission angle and position to simulate the propagation of light in the backlight module, and use the discretization result as the initial ray tracing; S4.4: According to the light transmission equation, simulate the propagation process of light reflecting, refracting, and scattering in the backlight module, obtain the light diffusion effects in different sub-regions, and according to the ray tracing results of each partition of the backlight source, obtain the light diffusion ratios in different sub-regions, and establish the corresponding light diffusion transfer matrix. According to the established light diffusion transfer matrix; S4.5: Calculate the final brightness of the corresponding backlight partition through the light diffusion transfer matrix and the partition drive current. According to the set target brightness of each backlight partition, construct the target brightness equation of each partition, and repeatedly solve the target brightness equation through the least squares method to calculate the optimal compensation current until the difference value between the partition brightness and the target brightness converges to the preset range, and then stop the solution. Then, according to the minimum and maximum values of the drive current, clip the calculated compensation current to generate the final compensation current to adjust the brightness of each backlight partition.

[0012] As a further solution of the present invention, the specific calculation formula of the radiation transfer equation in S4.1 is as follows: ; In the formula, represents the light intensity at position along the direction , where represents the angle between the light ray direction and the normal direction, represents the angle between the light ray and the preset reference direction on the horizontal plane; represents the infinitesimal element of the light propagation path; represents the absorption loss of light in the light guide plate; represents the scattering degree of light in the light guide plate; represents the probability distribution of the angular change during light scattering; represents the unit direction space; The specific calculation formula of the transmittance function described in S4.2 is as follows: ; In the formula, represents the light intensity at the microstructure layer position ; represents the transmittance distribution at the microstructure layer position ; represents the light intensity output from the light guide plate to the microstructure layer position ; The specific calculation formula of the diffusion equation described in S4.2 is as follows: ; In the formula, represents the light intensity at the diffusion film position ; t represents the diffusion time; D represents the diffusion rate of light in the film; represents the Laplace operator of the light intensity, measuring the local brightness change; represents the attenuation coefficient of light; The specific calculation formula of the multi-level light diffusion coupling model described in S4.2 is as follows: ; In the formula, represents the finally output light intensity distribution; represents the diffusion calculation of the diffusion film; represents the transmittance calculation of the microstructure layer; represents the radiation transfer calculation in the light guide plate; represents the initial light intensity distribution.

[0013] As a further solution of the present invention, the thickness of the backlight module in S4.1 is in the range of [1.5 mm, 5 mm], and the horizontal size of the backlight module is in the range of [50 mm 2 , 500 mm 2 . The wavelength of the light is in the range of [380 nm, 780 nm], and the initial emission angle of the light is in the range of [0, 90°].

[0014] As a further solution of the present invention, the specific steps of adjusting the local brightness weight in step III are as follows: S5.1: According to the red R, green G, and blue B channel values of each image data, convert the input image into a grayscale image. Through two-dimensional fast Fourier transform, convert the grayscale image from the spatial domain to the frequency domain, and perform centering processing on the converted image data to move the zero-frequency component to the center of the image; S5.2: Respectively, through a low-pass filter, a band-pass filter, and a high-pass filter, decompose the spatial frequency of the image into low-frequency, medium-frequency, and high-frequency components. Then, through inverse Fourier transform, convert the extracted different frequency components back to the spatial domain, generate corresponding reconstructed images, and perform normalization processing on the reconstructed images to obtain the perceived brightness values of each pixel point; S5.3: Calculate the local contrast of each image through the Weber contrast model. Based on the width and height of the image and the perceived brightness values of each pixel point, calculate the spatial frequencies in the horizontal and vertical directions of the image according to the pixel size of each feature information in the image, the distance from the observer to the screen, and the viewing angle; S5.4: Calculate the contrast sensitivity of the human eye to different spatial frequencies through the CSF function. Obtain the contrast sensitivity threshold of the human eye for each frame of the image at the current frequency through the contrast sensitivity, and draw the human eye visual perception characteristic curve. Then, calculate the brightness weight of the corresponding pixel according to the contrast, spatial frequency, and CSF function value, and adjust the local brightness of the image based on the calculated brightness weight.

[0015] As a further solution of the present invention, the specific calculation formula of the CSF function in S5.4 is as follows: ; In the formula, represents the contrast sensitivity at the spatial frequency f; f represents the spatial frequency; represents the average brightness of the image background; A, B, C, D, E, and F respectively represent empirical fitting parameters.

[0016] As a further solution of the present invention, the specific steps of allocating the best light source type and driving method according to the scene characteristics in step IV are as follows: S6.1: Analyze the currently displayed image information, extract the scene features affecting backlight control in each group of brightness distribution, contrast, dynamic range, and color information, and then calculate the lighting requirements for different regions based on the brightness histogram and local contrast; S6.2: According to the scene features, calculate the brightness dynamic range, select the corresponding light source type including Mini-LED, OLED, and Micro-LED based on the brightness and dynamic range of the current displayed image, and then optimize the power consumption and image quality of each backlight zone through local dimming or global dimming driving modes; S6.3: According to the power consumption of each light source under different driving modes, use different light sources for combined driving, monitor the screen output brightness using sensors, evaluate the energy efficiency ratio of the light source selection and zone driving strategy, and readjust the light source output based on the evaluation results of the energy efficiency ratio.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. The backlight source zone dimming control method calculates the brightness requirements for each zone according to the optimized backlight zone dimming strategy, divides the image area by grid, sets thresholds according to the average grid brightness, uses honeycomb-shaped dense zoning for high-brightness areas, and merges low-brightness areas into sparse rectangular zones. After traversing all grids, obtain the dynamically adjusted number of backlight zones, and iteratively optimize the zone centroid until the position change converges to generate the final irregular backlight zones. Subsequently, set the initial radiation intensity of the backlight module light source, combine the optical characteristics of the light guide plate prism and diffusion film, model the multi-level light diffusion coupling process through the transmission function and diffusion equation, discretize the light emission angle and position to simulate the light propagation in the backlight module, and establish a light diffusion transfer matrix to solve the final brightness of the backlight zones. Calculate the optimal compensation current based on the target brightness equation and the least squares method, iteratively optimize until the brightness error converges, and clip the calculated compensation current so that the backlight zones can adaptively adjust according to the image content, improve the local contrast and energy efficiency, achieve a more uniform light distribution, reduce halo and brightness non-uniformity problems, improve the dimming accuracy and extend the light source life, effectively reduce the light spillage effect at the zone edges, and enhance the picture layering.

[0018] 2. The backlight source zoned dimming control method converts the RGB three channels of the input image into a grayscale image, and transforms it from the spatial domain to the frequency domain through two-dimensional fast Fourier transform. The transformed image data is centered, and the low-pass, band-pass, and high-pass filters are used to decompose the low-frequency, medium-frequency, and high-frequency components. Then, the inverse Fourier transform is used to reconstruct each frequency component, and the pixel perception brightness value is obtained by normalization. Based on the Weber contrast model, the local contrast is calculated, and combined with the image size, viewing distance, and viewing angle, the spatial frequencies in the horizontal and vertical directions are calculated. The contrast sensitivity function is used to calculate the perception sensitivity threshold of the human eye to different spatial frequencies, and the visual perception characteristic curve is drawn to calculate the brightness weight of the corresponding pixel, so as to optimize the local brightness, analyze the brightness distribution, contrast, dynamic range, and color information of the current display image, evaluate the lighting requirements of different regions based on the brightness histogram and local contrast, and calculate the overall brightness dynamic range. According to the scene characteristics, the light source type is selected, and the local dimming or global dimming mode is adopted to optimize the power consumption and image quality of the backlight zones, effectively improving the subjective image quality experience, increasing the light efficiency utilization rate, and at the same time being able to balance the optimization of image quality and power consumption, making the brightness adjustment more in line with the human eye's visual characteristics, reducing visual fatigue, and ensuring the best image quality is maintained at the lowest power consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation to the present invention.

[0020] Figure 1 It is a flowchart of a backlight source zoned dimming control method proposed by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] Example 1, referring to Figure 1 A backlight source zoned dimming control method, the specific steps of the control method are as follows: Analyze the characteristics of the input image data, identify the high-brightness moving areas, and predict and optimize the current backlight zone dimming strategy.

[0022] Specifically, perform brightness normalization processing on each frame of input image data, then randomly select a set of gamma correction coefficients within the range of [0.8, 2.2], and adjust the contrast of the image according to the selected gamma correction coefficients. After that, divide the corrected image data into multiple non-overlapping sub-regions, calculate the gray-level histogram of each sub-region to obtain the gray-level distribution of each sub-region, calculate the cumulative distribution value CDF of the histogram of each sub-region. Then, clip the CDF values based on a preset contrast limit threshold, and calculate the pixel values after histogram equalization of the corresponding sub-regions according to the clipped CDF values. After that, smooth the boundaries of the equalized sub-regions through bilinear interpolation and reconstruct the complete image. Use the white balance algorithm based on the gray world assumption to keep the colors consistent among different frames of image data. Then, remove the noise of the image data through the non-local means filtering method, calculate the optical flow field among the processed frames of image data to obtain the motion information of pixel points, and take the optical flow field information higher than the preset high-light brightness threshold and motion amplitude threshold as the high-light motion region and mark it.

[0023] Specifically, initialize the policy adjustment model based on the LSTM model architecture, including an input layer, an LSTM layer, and an output layer. Extract the temporal features of each frame of image data after preprocessing through a CNN network, and associate each temporal feature with its corresponding highlight motion area. Then, divide the generated temporal features into a training set and a test set. Next, input the training set into the policy adjustment model. The input layer of the policy adjustment model receives the training set data and divides the training set data into multiple batches of training groups. At the same time, sequentially forward-propagate the training groups to the LTSM layer, and then process each group of training groups through the forget gate, input gate, and output gate in the LSTM layer in turn. Perform nonlinear processing on the final result output by the output gate through the Softmax function of the fully connected layer to obtain the finally predicted scene recognition result and the highlight motion area within a preset time interval, and output through the output layer. Calculate the true scene and highlight motion area labels of the current frame and the predicted scene recognition result and highlight motion area through the cross-entropy loss function. Input the calculated loss value from the output layer of the policy adjustment model, perform backpropagation based on the chain rule, and sequentially calculate the gradients of the loss value for each network layer of the policy adjustment model. Then, adjust the parameters of each network layer through the Adam optimizer. After each round of training, evaluate the precision, accuracy, recall rate, and F1 score of the policy adjustment model through the test set data and generate a comprehensive score. If the score does not reach the preset score threshold, retrain and test the policy adjustment model until the model loss value converges to the preset range, then stop training. Use an external memory module to record the historical task information of the policy adjustment model, that is, the features of known scenes and model parameters. Then, perform temporal modeling on each image data of past frames through the policy adjustment model, and use the forward propagation algorithm to generate the predicted result of the highlight motion area and the scene recognition result within a preset time period. According to the recognized scene category and the predicted highlight area, calculate the brightness gain of each partition, and adjust and optimize the original backlight partition dimming strategy based on the calculated brightness gain. When the loss value between the output result of the policy adjustment model and the actual result exceeds the preset threshold, calculate the cosine similarity between each historical task information stored in the external memory module and the current image data, and obtain the enhanced hidden state from the historical task information with the highest cosine similarity. Based on the selected enhanced hidden state and the current loss value, recalculate the loss value, and calculate the gradient of the current task based on the latest loss value. Update the parameters of the policy adjustment model based on the calculated gradient, and update the historical task information recorded in the memory module.

[0024] It should be further noted that the strategy adjustment model constructs corresponding hierarchical models based on the input layer, multiple LSTM layers, and output layer of the LSTM model. The processing flow of each layer of the strategy adjustment model is that the input layer receives the extracted feature data, and the input gate, forget gate, and output gate of the multiple LSTM layers process the extracted groups of feature data and generate corresponding hidden states. Then, the fully connected units in the output layer perform non-linear processing on the hidden states through the LeakyReLU activation function to generate the brightness coefficients of the backlight partitions, dimming direction suggestions, and dynamic dimming amplitudes, and output them.

[0025] According to the brightness, contrast, and color changes of the input content, the backlight area is adjusted in real time, and after the partition adjustment, a multi-level light diffusion coupling model is constructed.

[0026] Specifically, after adjusting the brightness of each partition through the optimized backlight partition dimming strategy, the local brightness requirement of the current frame image is calculated by weighted average. Then, the entire image is divided into multiple grid regions, and the average brightness of each grid is calculated. A brightness threshold is set. If the grid average brightness is higher than the brightness threshold, the area is subdivided using a honeycomb-like dense partition. If the grid average brightness is lower than the brightness threshold, adjacent regions are merged and a sparse rectangular partition is used. After traversing all grids, the number of adjusted backlight partitions is obtained. After the dynamic partition adjustment is completed, the center points of each partition are extracted, and each center point represents a backlight adjustment area. The entire screen area is divided into multiple polygon partitions through the Voronoi diagram algorithm. The centroid of each partition is calculated through the Lloyd iteration algorithm, and the new centroid is used as the new Voronoi generation point. Then, the Voronoi calculation and Lloyd iteration are repeated until the position change value of the Voronoi generation point converges within a preset range, and the finally generated irregular backlight partitions are output.

[0027] In addition, in this embodiment, the specific calculation formula for weighted average is as follows: ; In the formula, represents the brightness value at pixel point ; represents the channel weight; represents the value of the pixel point on the color channel (red R, green G, blue B); The specific calculation formula for the Voronoi diagram algorithm is as follows: ; In the formula, represents the set of real numbers in the two-dimensional plane; p represents a two-dimensional coordinate point in the two-dimensional plane; represents the distance from point p to the center point ; The distance from the representative point p to the center point ; The specific calculation formula of the Lloyd iteration algorithm is as follows: ; In the formula, represents the center point of the th region; represents the th region segmented by the Voronoi diagram algorithm; represents the th two-dimensional coordinate point in the

[0028] Specifically, set the initial radiation intensity distribution of the light source of the backlight module. Then, the light emitted by the light source enters the light guide plate, and the multiple scattering and absorption behaviors of light in the light guide plate are calculated through the radiation transfer equation to obtain the light diffusion process. The prism of the light guide plate and the microstructures of the diffusion film are used to perform additional direction adjustment and intensity redistribution on the light, and it is modeled through the transmission function. Then, the light homogenization process of the diffusion film is modeled through the diffusion equation, and each light diffusion process is integrated to construct a multi-level light diffusion coupling model. Based on the multi-level light diffusion coupling model, by randomly sampling the light emission angle and position, discretization processing is performed on it to simulate the propagation of light in the backlight module, and the discretization result is used as the initial ray tracing. According to the ray transfer equation, the propagation process of light reflection, refraction, and scattering in the backlight module is simulated to obtain the light diffusion influence in different sub-regions, and according to the ray tracing results of each partition of the backlight source, the light diffusion ratio in different sub-regions is obtained, and the corresponding light diffusion transfer matrix is established. According to the established light diffusion transfer matrix, the final brightness of the corresponding backlight partition is calculated through the light diffusion transfer matrix and the partition drive current. According to the set target brightness of each backlight partition, the target brightness equation of each partition is constructed, and the target brightness equation is repeatedly solved by the least squares method to calculate the optimal compensation current until the difference value between the partition brightness and the target brightness converges to the preset range, and the solution is stopped. Then, according to the minimum and maximum values of the drive current, the calculated compensation current is trimmed to generate the final compensation current to adjust the brightness of each backlight partition.

[0029] It should be further noted that the specific calculation formula of the radiation transfer equation is as follows: ; In the formula, represents the light intensity at the position along the direction , where represents the angle between the light ray direction and the normal direction, and represents the angle between the light ray and the preset reference direction on the horizontal plane; Represents an infinitesimal element of the light propagation path; Represents the absorption loss of light in the light guide plate; Represents the scattering degree of light in the light guide plate; Represents the probability distribution of the angular change during light scattering; Represents the unit direction space; The specific calculation formula of the transmittance function is as follows: ; In the formula, Represents the light intensity at the position of the microstructure layer ; Represents the transmittance distribution at the position of the microstructure layer ; Represents the light intensity output from the light guide plate to the position of the microstructure layer ; The specific calculation formula of the diffusion equation is as follows: ; In the formula, Represents the light intensity at the position of the diffusion film; t represents the diffusion time; D represents the diffusion rate of light in the film; ; Represents the Laplacian operator of the light intensity, measuring the local brightness change; Represents the attenuation coefficient of light; The specific calculation formula of the multi-level light diffusion coupling model is as follows: ; In the formula, Represents the final output light intensity distribution; Represents the diffusion calculation of the diffusion film; Represents the transmittance calculation of the microstructure layer; Represents the radiation transfer calculation in the light guide plate; Represents the initial light intensity distribution.

[0030] In addition, when performing multiple scattering and absorption behaviors, the thickness of the backlight module is in the range of [1.5 mm, 5 mm], the horizontal size of the backlight module is in the range of [50 mm 2 , 500 mm 2 , the wavelength of the light is in the range of [380 nm, 780 nm], and the initial emission angle of the light is in the range of [0, 90°].

[0031] Example 2, referring to Figure 1 , a backlight source zoned dimming control method, and the specific steps of the control method are as follows: Analyze the sensitivity of the human eye to different brightness and contrast, adjust the local brightness weight, and optimize the brightness mapping relationship of the backlight zones.

[0032] Specifically, according to the red R, green G, and blue B channel values of each image data, the input image is converted into a grayscale image. The grayscale image is transformed from the spatial domain to the frequency domain through a two-dimensional fast Fourier transform. The converted image data is centered to move the zero-frequency component to the center of the image. The spatial frequency of the image is decomposed into low-frequency, medium-frequency, and high-frequency components through a low-pass filter, a band-pass filter, and a high-pass filter respectively. Then, the extracted different frequency components are converted back to the spatial domain through an inverse Fourier transform, and corresponding reconstructed images are generated. Then, the reconstructed images are normalized to obtain the perceived brightness values of each pixel point. The local contrast of each image is calculated through the Weber contrast model. According to the width and height of the image and the perceived brightness values of each pixel point, the spatial frequencies in the horizontal and vertical directions of each image are calculated based on the pixel size of each feature information in the image, the distance from the observer to the screen, and the viewing angle. The contrast sensitivity of the human eye to different spatial frequencies is calculated through the CSF function. The contrast sensitivity threshold of the human eye for each frame of the image at the current frequency is obtained, and the human eye visual perception characteristic curve is plotted. Then, the brightness weight of the corresponding pixel is calculated according to the contrast, spatial frequency, and CSF function value. Based on the calculated brightness weight, the local brightness of the image is adjusted.

[0033] In this embodiment, the specific calculation formula of the CSF function is as follows: ; In the formula, represents the contrast sensitivity at the spatial frequency f; f represents the spatial frequency; represents the average brightness of the image background; A, B, C, D, E, and F respectively represent empirical fitting parameters.

[0034] Allocate the best light source type and driving method according to the scene characteristics, monitor the temperature of the backlight module in real time, and dynamically adjust the driving current according to the monitoring data.

[0035] Specifically, analyze the currently displayed image information, extract the scene characteristics that affect backlight control, such as brightness distribution, contrast, dynamic range, and color information. Then, calculate the lighting requirements of different regions based on the brightness histogram and local contrast. According to the scene characteristics, calculate the brightness dynamic range, and select the corresponding light source type according to the brightness and dynamic range of the currently displayed image, including Mini-LED, OLED, and Micro-LED. Then, through the driving modes of local dimming or global dimming, optimize the power consumption and image quality of each backlight zone. According to the power consumption of each light source under different driving modes, different light sources are combined for driving, and the sensor is used to monitor the screen output brightness, and the energy efficiency ratio of the light source selection and zone driving strategy is evaluated. Based on the evaluation result of the energy efficiency ratio, the light source output is readjusted.

[0036] According to the optimized brightness zoning and dimming control strategy, an independent dimming control strategy is adopted for different backlight regions.

[0037] Continuously monitor the output picture quality parameters, re-judge the backlight zoning dimming strategy in combination with the scene, and form an adaptive closed-loop control.

Claims

1. A backlight source zone dimming control method, characterized in that: The specific steps of the control method are as follows: Ⅰ. Analyze the input image data features, identify the highlight motion area, and predict and optimize the current backlight partition dimming strategy; II. According to the brightness, contrast and color changes of the input content, the backlight area is adjusted in real time, and after the partition adjustment, a multi-level light diffusion coupling model is constructed; III. Analyze the sensitivity of the human eye to different brightness and contrast, adjust the local brightness weight, and optimize the brightness mapping relationship of the backlight partition; IV. Allocate the best light source type and driving mode according to scene characteristics, monitor the backlight module temperature in real time, and dynamically adjust the driving current according to the monitoring data; V. According to the optimized brightness partitioning and dimming control strategy, independent dimming control strategies are adopted for different backlight areas; VI. Continuously monitor the output image quality parameters, re-predict the backlight zone dimming strategy based on the scene, and form an adaptive closed-loop control.

2. A backlight source zone dimming control method according to claim 1, characterized in that: The specific steps of identifying the highlight motion area described in step Ⅰ are as follows: S1.1: Perform brightness normalization on each frame of input image data, randomly select a set of gamma correction coefficients from the range of [0.8, 2.2], adjust the contrast of the image according to the selected gamma correction coefficients, and then divide the corrected image data into multiple non-overlapping sub-regions; S1.2: Calculate the grayscale histogram of each sub-region to obtain the grayscale distribution of each sub-region, calculate the cumulative distribution value CDF of the histogram of each sub-region, then clip the CDF value based on the preset contrast limit threshold, and calculate the pixel value of the corresponding sub-region histogram equalization according to each clipped CDF value, then smooth the boundaries of each equalized sub-region through bilinear interpolation and reconstruct the complete image; S1.3: Use a white balance algorithm based on the grayscale world assumption to keep the colors of different frames of image data consistent, then use the non-local mean filtering method to remove the noise of the image data, calculate the optical flow field between the processed frames of image data to obtain the motion information of the pixels, and mark the optical flow field information that is higher than the preset highlight brightness threshold and motion amplitude threshold as the highlight motion area.

3. A backlight source zone dimming control method according to claim 2, characterized in that: The specific steps of predicting and optimizing the current backlight partition dimming strategy described in step I are as follows: S2.1: Initialize the policy adjustment model based on the LSTM model architecture, extract the temporal features of each frame image data after preprocessing, and associate them with the corresponding highlight motion area. Then, divide the generated temporal features into a training set and a test set, and then input the training set into the policy adjustment model. S2.2: The input layer of the strategy adjustment model receives the training set data and divides the training set data into multiple batches of training groups. At the same time, the training groups are forward-propagated to the LTSM layer in turn. The final result output by the output gate is nonlinearly processed by the Softmax function of the fully connected layer to obtain the final predicted scene recognition result and the highlight motion area within the preset time interval, and then output through the output layer; S2.3: Calculate the real scene and highlight motion area labels of the current frame and the predicted scene recognition results and highlight motion areas through the cross entropy loss function, input the calculated loss value from the output layer of the strategy adjustment model, perform back propagation based on the chain rule, and adjust the parameters of each network layer through the Adam optimizer; S2.4: After each round of training, the performance indicators of the strategy adjustment model are evaluated through the test set data, and a comprehensive score is generated. If the score does not reach the preset score threshold, the strategy adjustment model is retrained and tested until the model loss value converges to the preset range, and then the training is stopped; S2.5: Use an external memory module to record historical task information of the strategy adjustment model, then use the strategy adjustment model to perform time series modeling on each image data of the past frames, and use the forward propagation algorithm to generate the prediction results of the highlight motion area within a preset time period, as well as the scene recognition results. According to the recognized scene category and the predicted highlight area, the brightness gain of each partition is calculated, and the original backlight partition dimming strategy is adjusted and optimized; S2.6: When the loss value between the output result of the policy adjustment model and the actual result exceeds the preset threshold, the cosine similarity between the historical task information stored in the external memory module and the current image data is calculated to obtain the enhanced hidden state. Based on the selected enhanced hidden state and the current loss value, the loss value is recalculated, and the gradient of the current task is calculated based on the latest loss value to re-update the policy adjustment model parameters and update the historical task information recorded in the memory module.

4. The backlight source zone dimming control method according to claim 1, characterized in that: The specific steps of real-time adjustment of the backlight area in step II are as follows: S3.1: After adjusting the brightness of each partition through the optimized backlight partition dimming strategy, the local brightness requirement of the current frame image is calculated by weighted average, and then the entire image is divided into multiple grid areas, and the average brightness of each grid is calculated; S3.2: Set a brightness threshold. If the average brightness of the grid is higher than the brightness threshold, use honeycomb dense partitioning to subdivide the area. If the average brightness of the grid is lower than the brightness threshold, merge the adjacent areas and use sparse rectangular partitioning. After traversing all grids, obtain the adjusted number of backlight partitions. S3.3: After the dynamic partition adjustment is completed, the center points of each partition are extracted, and each center point represents a backlight adjustment area. The entire screen area is divided into multiple polygonal partitions by using the Voronoi diagram algorithm; S3.4: Calculate the centroid of each partition through the Lloyd iteration algorithm, and use the new centroid as a new Voronoi generation point, repeat the Voronoi calculation and Lloyd iteration until the position change value of the Voronoi generation point converges to a preset range, and output the final generated irregular backlight partition.

5. A backlight source zone dimming control method according to claim 4, characterized in that: The specific steps of constructing the multi-level light diffusion coupling model described in step II are as follows: S4.1: setting the initial radiation intensity distribution of the light source of the backlight module, after which the light emitted by the light source enters the light guide plate, and calculating the multiple scattering and absorption behavior of the light in the light guide plate by using the radiation transfer equation to obtain the light diffusion process; S4.2: Use the prism of the light guide plate and the microstructures of the diffusion film to perform additional direction adjustment and intensity redistribution of the light, and model it through the transmission function. Then, model the light homogenization process of the diffusion film through the diffusion equation, and integrate the various light diffusion processes to construct a multi-level light diffusion coupling model; S4.3: Based on the multi-level light diffusion coupling model, the light emission angle and position are randomly sampled and discretized to simulate the propagation of light in the backlight module, and the discretization result is used as the initial ray tracing; S4.4: According to the light transmission equation, simulate the propagation process of reflection, refraction and scattering of light in the backlight module, obtain the light diffusion effect between different partitions, and according to the ray tracing results of each partition of the backlight light source, obtain the light diffusion ratio between different partitions, and establish the corresponding light diffusion transfer matrix, according to the established light diffusion transfer matrix; S4.5: Calculate the final brightness of the corresponding backlight partition through the light diffusion transfer matrix and the partition driving current. Construct the target brightness equation of each partition according to the set target brightness of each backlight partition, and repeatedly solve the target brightness equation through least squares to calculate the optimal compensation current until the difference between the partition brightness and the target brightness converges to within the preset range. Stop solving, and then trim the calculated compensation current according to the minimum and maximum values ​​of the driving current to generate the final compensation current to adjust the brightness of each backlight partition.

6. A backlight source zone dimming control method according to claim 5, characterized in that: The specific steps for adjusting the local brightness weight in step III are as follows: S5.1: converting the input image into a grayscale image according to the three sets of channel values ​​of red (R), green (G) and blue (B) of each image data, converting the grayscale image from the spatial domain to the frequency domain through a two-dimensional fast Fourier transform, and performing centering processing on the converted image data to move the zero-frequency component to the center of the image; S5.2: Decompose the spatial frequency of the image into low-frequency, medium-frequency and high-frequency components through low-pass filter, band-pass filter and high-pass filter respectively, then convert the extracted different frequency components back to the spatial domain through inverse Fourier transform, generate the corresponding reconstructed image, and then normalize the reconstructed image to obtain the perceived brightness value of each pixel; S5.3: Calculate the local contrast of each image using the Weber contrast model, and calculate the spatial frequency of each image in the horizontal and vertical directions according to the width, height, and perceived brightness value of each pixel of the image, the pixel size of each feature information in the image, the distance from the observer to the screen, and the viewing angle; S5.4: Calculate the contrast sensitivity of the human eye to different spatial frequencies through the CSF function, obtain the contrast sensitivity threshold of the human eye to each frame image at the current frequency through the contrast sensitivity, and draw the human eye visual perception characteristic curve, then calculate the brightness weight of the corresponding pixel according to the contrast, spatial frequency and CSF function value, and adjust the local brightness of the image based on the calculated brightness weight.

7. A backlight source zone dimming control method according to claim 6, characterized in that: The specific steps of allocating the best light source type and driving mode according to the scene characteristics described in step IV are as follows: S6.1: Analyze the currently displayed image information, extract the scene features of brightness distribution, contrast, dynamic range and color information that affect backlight control, and then calculate the lighting requirements of different areas based on the brightness histogram and local contrast; S6.2: Calculate the dynamic range of brightness based on scene characteristics, and select the corresponding light source type, including Mini-LED, OLED and Micro-LED, according to the brightness and dynamic range of the current displayed image. Then, optimize the power consumption and image quality of each backlight partition through local dimming or global dimming driving mode. S6.3: According to the power consumption of each light source under different driving modes, different light sources are used for combined driving, and sensors are used to monitor the screen output brightness. The energy efficiency ratio of light source selection and partition driving strategy is evaluated, and the light source output is readjusted based on the energy efficiency ratio evaluation results.

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