Backlight modulation method and device, liquid crystal display device and storage medium

Abstract

The invention provides a backlight modulation method and device, a liquid crystal display device and a storage medium. The backlight modulation method comprises the following steps: acquiring a brightness statistical value of each partition of a current frame image; comparing the brightness statistical values of the partitions of the current frame image and the previous frame image; and adjusting the backlight brightness of the LED lamp beads based on the comparison result. According to the invention, the dynamic adjustment of the backlight brightness of the LED lamp bead is realized based on the comparison result of the brightness statistical values of the partitions of the two adjacent frames of images, the problem of backlight convergence when a scene changes or the brightness change of a display picture is relatively large is effectively solved, the application range of backlight adjustment is wide, and the accuracy of backlight brightness adjustment is improved.

Description

Technical Field

[0001] The present application relates to the field of display technology, and in particular to a backlight modulation method, device, liquid crystal display device and storage medium. Background Art

[0002] Mini LED backlight technology significantly improves the dynamic contrast ratio of liquid crystal displays (LCDs) through individually controllable, array-distributed LEDs. Mini LED backlights combined with LCD display technology are widely used in end products.

[0003] Due to manufacturing process limitations, when the display image changes continuously, the LED lamp beads in Mini LEDs switch between bright and dark, causing the display image to flicker. To alleviate this phenomenon, filters are usually added to adjust the backlight brightness of the LED lamp beads. However, when the display image brightness changes significantly, the use of filters can cause backlight convergence, resulting in poor display quality.

[0004] Therefore, there is an urgent need to provide a backlight brightness adjustment solution with wide application range and reliability. Summary of the Invention

[0005] The present application provides a backlight modulation method, device, liquid crystal display device and storage medium, which realizes dynamic adjustment of the LED lamp bead backlight brightness based on the comparison results of the brightness statistical values ​​of each partition of two adjacent frames of images, effectively overcoming the problem of backlight convergence when the scene changes or the display screen brightness changes significantly. It has a wide range of applications and improves the accuracy of backlight brightness adjustment.

[0006] In a first aspect, the present application provides a backlight modulation method, comprising:

[0007] Get the brightness statistics of each partition of the current frame image;

[0008] Compare the brightness statistics of each partition of the current frame image and the previous frame image;

[0009] Based on the comparison results, the backlight brightness of the LED lamp beads is adjusted.

[0010] Optionally, based on the comparison result, adjust the backlight brightness of the LED lamp beads, including:

[0011] Based on the comparison result, determining the probability of scene switching;

[0012] Adjusting filter parameters based on the probability of scene switching;

[0013] The backlight brightness of the LED lamp beads is adjusted based on the adjusted filter.

[0014] Optionally, adjusting filter parameters based on the probability of scene switching includes:

[0015] When the probability of the scene switching is greater than the first probability, reducing the filtering strength of the time domain filter and increasing the filter strength of the spatial domain filter;

[0016] When the probability of the scene switching is less than the second probability, the filtering strength of the time domain filter is increased, and the filter strength of the spatial domain filter is decreased.

[0017] Optionally, based on the comparison result, adjust the backlight brightness of the LED lamp beads, including:

[0018] Determining the probability of scene switching for each partition of the current frame image based on the comparison result and a pre-trained scene switching probability prediction model;

[0019] Based on the probability of scene switching in each partition of the current frame image, the parameters of the filter corresponding to each partition are adjusted;

[0020] The backlight brightness of the LED lamp beads corresponding to each partition is adjusted through the adjusted filters corresponding to each partition.

[0021] Optionally, the comparison result includes a difference in brightness statistics of each partition, and determining the probability of scene switching of each partition of the current frame image based on the comparison result and a pre-trained scene switching probability prediction model includes:

[0022] For each partition, calculate the ratio of the difference between the brightness statistics of the partition and the brightness statistics of the partition in the previous frame image to obtain an input matrix;

[0023] The input matrix is ​​input into a pre-trained scene switching probability prediction model, and the probability of scene switching of each partition of the current frame image is predicted based on the pre-trained scene switching probability prediction model.

[0024] Optionally, the training process of the scene switching probability prediction model includes:

[0025] Obtaining brightness statistics of each partition of a plurality of sample frames, wherein the plurality of sample frames include a plurality of groups of two adjacent sample frames, and the two adjacent sample frames include a previous sample frame and a current sample frame;

[0026] For each group of two adjacent sample frames, calculate the ratio of the difference in brightness statistics of each partition of the two adjacent sample frames to the brightness statistics of each partition of the previous sample frame in the group of two adjacent sample frames, and obtain the input matrix corresponding to the group of two adjacent sample frames;

[0027] Obtaining a probability true value of the annotation of the current frame sample image in each group of two adjacent frame sample images, wherein the probability true value includes a true value of the probability of scene switching of each partition of the current frame sample image of the corresponding group;

[0028] Based on the input matrix and probability true value corresponding to each group of two adjacent frame sample images, the scene switching probability prediction model is trained, and the parameters of the scene switching probability prediction model are updated by the loss value calculated by the probability predicted by the scene switching probability prediction model and the corresponding probability true value.

[0029] Optionally, based on the probability of scene switching in each partition of the current frame image, the parameters of the filter corresponding to each partition are adjusted, including:

[0030] For each partition of the current frame image, when the probability of scene switching of the partition is greater than a first probability, reducing the filter strength of the temporal filter corresponding to the partition, and increasing the filter strength of the spatial filter corresponding to the partition;

[0031] When the probability of scene switching of the partition is less than the second probability, the filter strength of the time domain filter corresponding to the partition is increased, and the filter strength of the spatial domain filter corresponding to the partition is reduced.

[0032] Optionally, the filter includes a time domain filter and a space domain filter, and based on the probability of scene switching in each partition of the current frame image, adjusting the parameters of the filter corresponding to each partition includes:

[0033] For each partition, if the probability of the partition scene switching in the current frame image is greater than the first probability, increasing the weight of the current frame image in the temporal filter corresponding to the partition, and expanding the filtering window of the spatial filter corresponding to the partition;

[0034] If the probability of the scene switching of the partition of the current frame image is less than the second probability, reducing the weight of the current frame image in the time domain filter corresponding to the partition, and reducing the filtering window of the spatial domain filter corresponding to the partition;

[0035] The first probability is higher than the second probability.

[0036] Optionally, obtain the brightness statistics of each partition of the current frame image, including:

[0037] Obtaining brightness statistics of each lamp bead partition of the current frame image, wherein one lamp bead partition corresponds to one LED lamp bead;

[0038] Downsampling is performed on the brightness statistics of each lamp bead partition of the current frame image to obtain the brightness statistics of each partition of the current frame image.

[0039] Optionally, the partitions are divided into non-overlapping areas and overlapping areas, and two vertically adjacent partitions correspond to the same overlapping area; obtaining brightness statistics of each partition of the current frame image includes:

[0040] For each partition of the current frame image, calculating brightness statistics of non-overlapping areas of the partition;

[0041] For a pixel within the overlapping area of ​​the partitions, determining a weight coefficient of the pixel with respect to the partition based on the row where the pixel is located;

[0042] Calculating brightness statistics of the overlapping area of ​​the partitions based on weight coefficients of each pixel in the overlapping area of ​​the partitions with respect to the partitions;

[0043] The brightness statistics of the partitions are obtained based on the brightness statistics of the non-overlapping areas of the partitions and the brightness statistics of the overlapping areas of the partitions.

[0044] Optionally, calculating brightness statistics of non-overlapping areas of the partitions includes:

[0045] The sum of the maximum color values ​​of each pixel in the non-overlapping area of ​​the partition is calculated to obtain the brightness statistics of the non-overlapping area; the maximum color value of a pixel is the maximum value among the color values ​​of different color channels of the pixel.

[0046] Optionally, calculating the brightness statistics of the overlapping area of ​​the partitions based on the weight coefficient of each pixel in the overlapping area of ​​the partitions with respect to the partition includes:

[0047] The sum of the products of the maximum color value of each pixel in the overlapping area of ​​the partitions and the weight coefficient of the corresponding pixel with respect to the partition is calculated to obtain the brightness statistics of the overlapping area.

[0048] Optionally, the method further includes:

[0049] For a pixel in the overlapping area of ​​the partitions, determining a weight coefficient of the pixel with respect to a lower partition of the partition based on a row where the pixel is located;

[0050] Calculating the sum of the maximum color values ​​of pixels in the overlapping area of ​​the partitions and the product of the weight coefficients of the corresponding pixels with respect to the lower partition of the partition to obtain a brightness statistical value of the overlapping area with respect to the corresponding lower partition;

[0051] Obtaining brightness statistics of the partitions based on brightness statistics of non-overlapping areas of the partitions and brightness statistics of overlapping areas of the partitions includes:

[0052] Determine a brightness statistic of the partition based on brightness statistics of non-overlapping areas of the partitions, brightness statistics of overlapping areas of the partitions, and brightness statistics of overlapping areas of upper partitions of the partitions with respect to the partition;

[0053] The lower partition of the partition is a partition located below and adjacent to the partition along the direction of the pixel column, and the upper partition of the partition is a partition located above and adjacent to the partition along the direction of the pixel column.

[0054] Optionally, compare the brightness statistics of each partition of the current frame image and the previous frame image, including:

[0055] For each partition of the current frame image, calculate the sum of the brightness statistics of the non-overlapping area of ​​the partition and the brightness statistics of the overlapping area of ​​the partition, and divide it by the number of pixels in the partition to obtain the brightness mean of the partition;

[0056] Compare the brightness mean of each partition of the current frame image and the previous frame image.

[0057] Optionally, the comparison result is a coefficient matrix, and the comparison of the brightness mean of each partition of the current frame image and the previous frame image includes:

[0058] Calculate the difference between the brightness mean of each partition of the current frame image and the brightness mean of the corresponding partition of the previous frame image and the preset color value to obtain a difference matrix;

[0059] Based on the comparison result of the elements corresponding to each partition in the difference matrix and the preset color value, the backlight brightness coefficient of each partition is determined to obtain a coefficient matrix.

[0060] Optionally, determining the backlight brightness coefficient of each partition based on a comparison result between an element corresponding to each partition in the difference matrix and the preset color value includes:

[0061] For each partition, if the element corresponding to the partition in the difference matrix is ​​greater than a preset color value, determining the backlight coefficient corresponding to the partition as a ratio of the element corresponding to the partition to the preset color value;

[0062] If the element corresponding to the partition in the difference matrix is ​​smaller than the preset color value, the backlight coefficient corresponding to the partition is determined to be 1 minus the ratio of the element corresponding to the partition to the preset color value.

[0063] Optionally, based on the comparison result, adjust the backlight brightness of the LED lamp beads, including:

[0064] Sending the comparison result and the current frame image to a local dimming module;

[0065] The regional dimming module adjusts the backlight brightness of the LED lamp beads corresponding to each partition based on the comparison result, so as to display the current frame image based on the adjusted backlight brightness of the LED lamp beads.

[0066] In a second aspect, the present application provides a backlight modulation device, comprising:

[0067] The partition brightness statistics module is used to obtain the brightness statistics of each partition of the current frame image;

[0068] A comparison module is used to compare the brightness statistics of each partition of the current frame image and the previous frame image;

[0069] The backlight adjustment module is used to adjust the backlight brightness of the LED lamp beads based on the comparison result.

[0070] In a third aspect, the present application provides a liquid crystal display device, comprising: a liquid crystal display screen, an LED array, a graphics processor, a camera, an image signal processor, a central processing unit, and an image processing chip;

[0071] The liquid crystal display screen is a carrier for displaying images; the LED array includes a plurality of LED lamp beads, which are the backlight source of the liquid crystal display screen;

[0072] The image signal processor is used to process the image data collected by the camera to obtain a frame of image;

[0073] The central processing unit is used to receive the image generated by the image signal processor or the graphics processor, calculate and store the brightness statistics of each partition of the image, and compare it with the stored brightness statistics of each partition of the previous frame image to obtain a comparison result;

[0074] The image processing chip includes a local dimming module, and the local dimming module is used to adjust the backlight brightness of the LED lamp beads in the LED array based on the comparison result.

[0075] In a fourth aspect, the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the method provided in the first aspect of the present application is implemented.

[0076] In a fifth aspect, the present application provides a computer program product, comprising a computer program, which, when executed by a processor, implements the method provided in the first aspect of the present application.

[0077] The backlight modulation method, device, liquid crystal display device and storage medium provided in the present application determine whether the display scene of the current frame image has switched by comparing the brightness statistical values ​​of each partition of the current frame and the previous frame image. At the same time, when the display scene switches, the backlight brightness of the LED lamp beads is dynamically adjusted based on the comparison results of the brightness statistical values ​​of each partition. Compared with the method of adjusting the backlight based on the filter, the method of adjusting the backlight based on the comparison results of the brightness statistical values ​​of each partition effectively overcomes the problem of backlight convergence when the scene changes significantly or the brightness of the display screen changes greatly. It has a wide range of applications, and the backlight is adjusted according to the changes in the brightness statistical values, thereby improving the accuracy of the backlight brightness adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0079] Figure 1 A schematic structural diagram of a liquid crystal display based on Mini LED backlight technology provided in an embodiment of the present application;

[0080] Figure 2 A schematic diagram of a backlight modulation method according to an embodiment of the present application;

[0081] Figure 3 For this application Figure 2 A schematic diagram of image partitioning in the illustrated embodiment;

[0082] Figure 4 A schematic diagram of partition downsampling provided in an embodiment of the present application;

[0083] Figure 5 A schematic diagram of a filter-based backlight modulation process provided in an embodiment of the present application;

[0084] Figure 6 A schematic flow chart of another backlight modulation method provided in an embodiment of the present application;

[0085] Figure 7 A schematic diagram of an input matrix and a probability truth value provided in an embodiment of the present application;

[0086] Figure 8 For this application Figure 6 A schematic diagram of the model training and inference process provided by the illustrated embodiment;

[0087] Figure 9 Schematic diagram of non-overlapping partitions and overlapping partitions provided in an embodiment of the present application;

[0088] Figure 10 A schematic flow chart of another backlight modulation method provided in an embodiment of the present application;

[0089] Figure 11 A schematic flow chart of another backlight modulation method provided in an embodiment of the present application;

[0090] Figure 12 A schematic diagram of the hardware architecture for implementing the method provided in the embodiments of the present application;

[0091] Figure 13 A schematic flow chart of another backlight modulation method provided in an embodiment of the present application;

[0092] Figure 14 A schematic structural diagram of a liquid crystal display device provided in an embodiment of the present application.

[0093] The above drawings illustrate specific embodiments of the present application, which will be described in more detail below. These drawings and the textual description are not intended to limit the scope of the present application in any way, but rather to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments. DETAILED DESCRIPTION

[0094] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0095] Figure 1 A schematic diagram of the structure of a liquid crystal display based on Mini LED backlight technology provided in an embodiment of the present application is shown in FIG. Figure 1 As shown, the liquid crystal display includes a liquid crystal display screen LCD and a Mini LED backlight module, wherein the Mini LED backlight module is a direct-type backlight source of the LCD.

[0096] The Mini LED backlight module consists of a Mini LED light board, a driver board, and an optical material layer. The Mini LED light board is equipped with a large number of LED lamp beads; the driver board is used to drive the LED lamp beads in the Mini LED light board; the optical material layer, including a diffuser and optical lens, is used to ensure the uniform dispersion of light emitted by the LED lamp beads, while also protecting the Mini LED light board and reducing the optical distance of the backlight.

[0097] The Mini LED backlight module supports local dimming, which can divide the entire backlight into multiple independently controlled areas, and control the brightness of the lamp beads in a single area through the LED chip, thereby achieving higher contrast.

[0098] Due to the large number of LEDs distributed across a Mini LED panel, when the LCD display brightness continuously changes, uneven brightness distribution can occur in areas with significant changes between adjacent frames, causing flickering and halos. To address this issue, a related technique has proposed adding a time-domain filter to control LED brightness.

[0099] However, the time domain filter may cause sudden changes in the display screen, or serious backlight convergence problems may occur when the display screen brightness changes significantly. That is, when the scene changes, the backlight brightness cannot switch to the value under the new scene, resulting in poor display effect and affecting the user experience.

[0100] Based on this, the present application provides a backlight modulation method, which, for a display based on Mini LED backlight technology, adjusts the backlight brightness of the LED lamp beads on the Mini LED lamp board based on the comparison results of the brightness statistical values ​​of each partition of the current frame image and the previous frame image, and compares the brightness statistical values ​​at the partition granularity, thereby adjusting the backlight brightness based on the comparison results, thereby improving the accuracy of the backlight brightness adjustment.

[0101] The following specific embodiments describe in detail the technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0102] Figure 2 This is a flow chart of a backlight modulation method provided in an embodiment of the present application. This method can be executed by an electronic device with corresponding data processing capabilities, such as an image processing chip, a central processing unit, a backlight modulation module, etc. Figure 2 As shown, the backlight modulation method includes the following steps:

[0103] Step S201: Obtain brightness statistics of each partition of the current frame image.

[0104] Since each LED lamp bead on the Mini LED light board can be controlled individually, the image can be partitioned according to the LED lamp bead, with one partition corresponding to one LED lamp bead.

[0105] In other embodiments, the image may be partitioned in a default manner, such as dividing the image into 50×50, 5×5, or other numbers of partitions, and the number of pixels in different partitions may be different.

[0106] For example, Figure 3 For this application Figure 2 A schematic diagram of image partitioning in the embodiment shown, Figure 3 For example, the image is divided into 3×3 partitions, such as Figure 3 As shown, the partitioning can be uniform or non-uniform.

[0107] The brightness statistics of a partition may be one or more of the average value, maximum value, median value, or other statistical values ​​calculated in other ways of the brightness of each partition.

[0108] For example, the brightness statistics of a partition may include an average value and a maximum value of the brightness in the partition.

[0109] For a scene of inputting continuous multiple-frame images, for each frame image except the first frame image in the multiple-frame images, when the frame image (ie, the current frame image) is received, the brightness statistical value of each partition of the frame image is counted.

[0110] The CPU may partition the brightness data of an input frame of image to obtain brightness data of each partition of the frame of image, and then the image processing module determines the brightness statistics of each partition of the frame of image based on the brightness data of each partition of the frame of image.

[0111] In some embodiments, in order to reduce the amount of calculation, after receiving the brightness data of each partition of the frame image output by the CPU, the partitions of the image can also be downsampled, that is, the number of image partitions can be reduced, such as from 5×5 partitions to 3×3 partitions, so as to calculate the brightness statistics of each partition after downsampling.

[0112] For example, Figure 4 A schematic diagram of partition downsampling provided in an embodiment of the present application is shown in FIG. Figure 4 As shown, the CPU divides the image into 5×5 even partitions, namely partitions 1 to 25, and then divides the image into 3×3 uneven partitions, namely partitions 26 to 34, through downsampling. Taking the calculation of the brightness statistics of partition 26 as an example, partition 26 includes partitions 1, 2, 6, and 7. The brightness statistics of partition 26 can be calculated based on the brightness statistics of partitions 1, 2, 6, and 7. Taking the average brightness statistics as an example, the brightness statistics of partition 26 is the average of the brightness statistics of partitions 1, 2, 6, and 7.

[0113] The CPU sends the brightness data of each partition of a frame of image to the image processing module, and also sends the brightness statistics of each partition to the image processing module. The image processing module can calculate the brightness statistics of each partition after downsampling based on the mapping relationship between the partitions before and after downsampling and the brightness statistics of each partition before downsampling.

[0114] Step S202 : comparing the brightness statistics of each subregion of the current frame image and the previous frame image.

[0115] The preceding frame image is one or more frames of image before the current frame image, such as the previous frame image.

[0116] Assume that the current frame image is the Kth frame image, where K is an integer greater than 1, and the previous frame image may be one or more frames from the 1st to the K-1th frames. Taking K as 5 as an example, the previous frame may be one or more frames from the 1st to the 4th frames, such as the 3rd frame, or the 2nd and 4th frames.

[0117] In some embodiments, if there are multiple previous frames, the brightness of the multiple previous frame images can be averaged to obtain a previous frame average image, and the brightness statistics of each partition of the current frame image and the previous frame average image can be compared.

[0118] In other embodiments, a weighted IIR filter (Infinite Impulse Response Filter) may be provided to filter multiple previous frame images using the IIR filter to obtain a filtered image of the previous frame, and then compare the brightness statistics of each partition of the current frame image with the filtered image of the previous frame. The closer the previous frame image is to the current frame image, the greater the weight of the previous frame image in the IIR filter.

[0119] Obtain the brightness statistics of each partition of the previous frame image of the current frame image, compare the brightness statistics of each partition of the current frame image and the previous frame image partition by partition, and obtain comparison results, such as the difference or ratio of the brightness statistics of each partition.

[0120] The comparison result is used to describe the change of the brightness statistics of each partition of two adjacent frame images (the current frame image and the previous frame image).

[0121] For example, the comparison result D of the i-th partition i Can be: (V ni -V (n-1)i ) / V (n-1)I , where V ni is the brightness statistics of the ith partition of the nth frame image, V (n-1)i is the brightness statistics of the i-th partition of the n-1-th frame image.

[0122] Step S203: Based on the comparison result, adjust the backlight brightness of the LED lamp beads.

[0123] Specifically, the backlight brightness of the LED lamp beads corresponding to each partition can be adjusted based on the value corresponding to each partition in the comparison result.

[0124] Specifically, the number of partitions whose values ​​in the comparison results are greater than a preset threshold can be counted and recorded as the number of switching partitions. Based on the comparison result of the number of switching partitions and the preset number, it is determined whether the scene has switched. If so, the backlight brightness of the LED lamp beads is adjusted based on the number of switching partitions.

[0125] In some embodiments, the probability of scene switching can be determined based on the comparison result, and the backlight brightness of the LED lamp beads can be adjusted based on the probability of scene switching.

[0126] The backlight brightness of LEDs can be adjusted by adjusting filter parameters. This means you can adjust the filter parameters based on the probability of scene switching or the number of switching zones, and then adjust the backlight value of the LEDs based on the adjusted filter parameters. Filters can include temporal and spatial filters.

[0127] Taking the brightness statistics as the maximum and average brightness in the corresponding area as an example, the process of adjusting the filter parameters is as follows: Figure 5 As shown, Figure 5 This is a schematic diagram of a backlight modulation process based on a filter provided in an embodiment of the present application. This embodiment is described as an example in which the previous frame is the previous frame. Figure 5As shown, for two adjacent frame images fm0 (previous frame image) and fm1 (current frame image), calculate the difference MeanDiff between the average brightness of each partition of fm0 mean_cell_fm0 and the average brightness of each partition of fm1 mean_cell_fm1, judge whether MeanDiff exceeds the average value threshold MeanDiffTH, and record the number of partitions where MeanDiff exceeds the average value threshold MeanDiffTH to obtain the average value exceeding the limit number MeanDiffCout; calculate the difference MaxDiff between the maximum value max_cell_fm0 of the brightness of each partition of fm0 and the maximum value max_cell_fm of the brightness of each partition of fm1, judge whether MaxDiff exceeds the maximum value threshold MaxDiffTH, The number of partitions where MaxDiff exceeds the maximum threshold MaxDiffTH is recorded to obtain the maximum value exceeding the limit number MaxDiffCout; the mean value exceeding the limit number MeanDiffCout and the maximum value exceeding the limit number MaxDiffCout are added to obtain the number of switching partitions; and it is determined whether the number of switching partitions exceeds the confidence threshold ConfidenceTH. If so, the scene has switched, and the scene switching probability scene_cut of fm1 relative to fm0 must be calculated based on the number of switching partitions. The filter parameters are adjusted based on the scene switching probability scene_cut, and the backlight is controlled based on the adjusted filter parameters. If not, the scene has not switched, and the scene switching probability scene_cut is set to 0, so that the filter does not need to perform filtering. The filter determines whether to perform filtering based on the truth of the scene switching probability scene_cut (e.g., whether it is 0).

[0128] Taking the filter including a time domain filter and a spatial domain filter as an example, the parameters of the filter are adjusted based on the probability of scene switching, including: when the probability of scene switching is greater than the first probability, reducing the filtering strength of the time domain filter and increasing the filter strength of the spatial domain filter; when the probability of scene switching is less than the second probability, increasing the filtering strength of the time domain filter and reducing the filter strength of the spatial domain filter.

[0129] Among them, the first probability is higher than the second probability.

[0130] Exemplarily, the first probability may be 0.5 and the second probability may be 0.1.

[0131] The strength of the temporal filter can be represented by the weight of the current frame image in the temporal filter, while the strength of the spatial filter can be represented by the size of the spatial filter window.

[0132] When the probability of scene switching is between the first probability and the second probability, the strength of the filter may not be adjusted, that is, the parameters of the filter may not be adjusted.

[0133] Optionally, based on the comparison result, adjust the backlight brightness of the LED lamp beads, including:

[0134] Based on the comparison results and a pre-trained scene switching probability prediction model, the probability of scene switching in each partition of the current frame image is determined; based on the probability of scene switching in each partition of the current frame image, the parameters of the filter corresponding to each partition are adjusted; and the backlight brightness of the LED lamp beads corresponding to each partition is adjusted through the adjusted filters corresponding to each partition.

[0135] The scene switching probability prediction model is used to output the probability of scene switching of each partition based on the input comparison result or data obtained after preprocessing the comparison result.

[0136] Exemplarily, the scene switching probability prediction model may be a model such as SVM (Support Vector Machine), decision tree, LR (Logistic Regression), or a neural network model for classification.

[0137] The comparison result can be represented by a matrix, where the elements in the matrix represent the comparison results of the brightness statistics of the corresponding partitions, such as the difference, or the ratio of the difference to the brightness statistics of the partition of the previous frame image.

[0138] In some embodiments, the brightness of an LED lamp bead is controlled by one or a group of filters, and each partition corresponds to one LED lamp bead. The scene switching probability output by the scene switching probability prediction model is correlated with the filter strength of the filter or filter group controlling the LED lamp bead in each partition. Based on the scene switching probability of each partition, the parameters of the filter of the LED lamp bead corresponding to each partition can be adjusted, thereby adjusting the backlight brightness of the corresponding LED lamp bead through the adjusted filter parameters.

[0139] Based on the scene switching probability prediction model, the scene switching probability is predicted with high prediction accuracy, thereby improving the accuracy of backlight adjustment.

[0140] Optionally, based on the probability of scene switching in each partition of the current frame image, the parameters of the filter corresponding to each partition are adjusted, including:

[0141] For each partition, if the probability of the partition scene switching in the current frame image is greater than the first probability, the weight of the current frame image in the time domain filter corresponding to the partition is increased, and the filtering window of the spatial domain filter corresponding to the partition is expanded; if the probability of the partition scene switching in the current frame image is less than the second probability, the weight of the current frame image in the time domain filter corresponding to the partition is reduced, and the filtering window of the spatial domain filter corresponding to the partition is narrowed; wherein, the first probability is higher than the second probability.

[0142] When the probability of scene switching in a partition is high, it indicates that the brightness of the scene in the partition changes greatly. By reducing the filtering strength of the temporal filter, that is, increasing the weight of the current frame, the brightness of the displayed image is made closer to the brightness of the current frame, avoiding the problem of backlight convergence. At the same time, by enhancing the filtering strength of the spatial filter, that is, expanding the filtering window, the current frame image is made smoother and sudden changes in the image are avoided.

[0143] The backlight modulation method provided in this embodiment determines whether the display scene of the current frame image has switched by comparing the brightness statistics of each partition of the current frame and the previous frame image. At the same time, when the display scene switches, the backlight brightness of the LED lamp beads is dynamically adjusted based on the comparison results of the brightness statistics of each partition. Compared with the method of adjusting the backlight based on the filter, the method of adjusting the backlight based on the comparison results of the brightness statistics of each partition effectively overcomes the problem of backlight convergence when the scene changes significantly or the brightness of the display screen changes greatly. It has a wide range of applications, and the backlight is adjusted according to the changes in the brightness statistics, thereby improving the accuracy of the backlight brightness adjustment.

[0144] Figure 6 A flow chart of another backlight modulation method provided in an embodiment of the present application. Figure 2 On the basis of the embodiment shown, the further definition of step S201 and step S203 is as follows: Figure 6 As shown, the backlight modulation method provided in this embodiment may specifically include the following steps:

[0145] Step S601: Obtain brightness statistics of each lamp bead partition of the current frame image, wherein one lamp bead partition corresponds to one LED lamp bead.

[0146] The CPU on the application side can provide the brightness statistics of each lamp bead partition in each frame image.

[0147] After the image data img_data of each frame image is input into the CPU, the CPU divides the image into each lamp bead partition and calculates the brightness statistical value of each lamp bead partition.

[0148] Each frame of image captured by the camera is processed into image data img_data by the ISP (Image Signal Processing), or each frame of image is drawn by the GPU (Graphics Processing Unit), and the generated image data img_data of each frame of image is sent to the CPU.

[0149] In some embodiments, the image processing module may perform image partitioning, that is, the image processing module obtains statistical brightness values ​​of each lamp bead partition in each frame of image based on the image data of each frame of image received.

[0150] Before counting the brightness statistics of the lamp bead partitions, the image data can also be downsampled to improve efficiency.

[0151] Step S602 : downsampling the brightness statistics of each lamp bead partition of the current frame image to obtain the brightness statistics of each partition of the current frame image.

[0152] For each frame of image, the image processing module, such as the image processing chip, receives the brightness statistics of each lamp bead partition of the frame image, downsamples the lamp bead partition, thereby dividing the image into fewer partitions, and calculates the brightness statistics of each partition of the image based on the mapping relationship between the partition and the lamp bead partition during downsampling.

[0153] Through downsampling, the brightness statistics of one or more lamp bead partitions can be used to represent the brightness statistics of a partition. Taking the maximum brightness statistics as an example, the maximum value among the brightness statistics of multiple lamp bead partitions corresponding to the partition can be represented as the brightness statistics of the partition.

[0154] Through downsampling processing, the number of partitions is effectively reduced, thereby reducing the dimension of the model input and further reducing the computational complexity of scene switching probability prediction.

[0155] In some embodiments, step S602 can be omitted and the partitions can be directly based on the lamp bead partitions.

[0156] Step S603 : Calculate the difference between the brightness statistics of each subregion of the current frame image and the previous frame image.

[0157] The calculation method of the brightness statistical value of each partition of each frame image (including the current frame image and the previous frame image) is the same.

[0158] For each partition, the difference between the brightness statistics of the current frame image and the previous frame image of the partition is calculated to obtain a difference matrix.

[0159] Step S604 : For each partition, calculate the ratio of the difference between the brightness statistics of the partition and the brightness statistics of the partition in the previous frame image to obtain an input matrix.

[0160] In order to better describe the change in brightness of the current frame image relative to the previous frame image, after obtaining the difference matrix, it is necessary to divide each element in the difference matrix by the brightness statistics of the corresponding partition of the previous frame image to achieve normalization of the brightness statistics and obtain the input matrix of the scene switching probability prediction model.

[0161] Step S605 : inputting the input matrix into a pre-trained scene switching probability prediction model, and predicting the probability of scene switching of each partition of the current frame image based on the pre-trained scene switching probability prediction model.

[0162] Optionally, the training process of the scene switching probability prediction model includes:

[0163] Obtain brightness statistics of each partition of a multi-frame sample image, wherein the multi-frame sample image includes a plurality of sample image pairs, and the sample images included in the sample image pairs are divided into a current frame sample image and a previous frame sample image; for each sample image pair, calculate the difference between the brightness statistics of the current frame sample image and the brightness statistics of each partition of the previous frame sample image in the sample image pair, and the ratio of the brightness statistics of each partition of the previous frame sample image in the sample image pair to obtain an input matrix corresponding to the sample image pair; obtain the probability true value of the current frame sample image annotation in each sample image pair, wherein the probability true value includes the true value of the probability of scene switching of each partition of the current frame sample image; based on the input matrix and probability true value corresponding to each sample image pair, train a scene switching probability prediction model, and update the parameters of the scene switching probability prediction model by using the loss value calculated by the probability predicted by the scene switching probability prediction model and the corresponding probability true value.

[0164] The true value of the probability can be manually labeled, such as when the current frame sample image with better display effect is displayed, the filter parameters of each lamp bead are determined.

[0165] The output of the scene switching probability prediction model is a matrix with the same dimension as the input. Each element in the matrix output by the scene switching probability prediction model represents the probability of scene switching occurring in the corresponding partition, and its physical meaning is the filtering strength of the filter.

[0166] For example, Figure 7 A schematic diagram of an input matrix and a probability true value provided in an embodiment of the present application is shown as follows: Figure 7 As shown in the figure, the matrices corresponding to the brightness statistics of each partition of the n-th frame sample image and the n-1-th frame sample image are M n and M n-1 , calculate the matrix M n and matrix Mn-1 The difference between the two matrixes is obtained by dividing each element of the difference matrix D by the matrix M. n-1 The elements at the corresponding positions in the matrix are obtained, and the values ​​of the elements in the matrix I are as follows: Figure 7 As shown, the value range of the elements in the input matrix I is [-∞, +∞], then a possible probability true value of the input matrix I is as follows Figure 7 As shown in the matrix L in , the value range of each element in the matrix corresponding to the true value of the probability is [0,1]. The closer the value is to 1, the greater the probability of scene switching in the corresponding partition, and the greater the strength of the filter.

[0167] This application does not limit the training method of the scene switching probability prediction model, and any training method can be used for training.

[0168] Figure 8 For this application Figure 6 The schematic diagram of the model training and reasoning process provided by the illustrated embodiment, Figure 8 Take the scene switching probability prediction model as an example of a machine learning model. Figure 8 As shown in the figure, during the model training phase, the machine learning model is trained using training data, and the model parameters are continuously updated by calculating the loss value LOSS until the training end conditions are met, such as the training time reaching the upper limit time, the loss value meeting certain conditions, etc. After the model training is completed, the model inference phase begins. Based on the partition data of the current frame image (the brightness statistics of each partition) and the partition data of the previous frame image, a normalized partition brightness change matrix (i.e., the above-mentioned input matrix) is obtained and input into the trained machine learning model. The machine learning model outputs the analysis results of the scene switching probability of each partition, i.e., the probability of scene switching of each partition (between 0 and 1), and the filter parameters are adjusted based on this analysis result.

[0169] Step S606: For each partition of the current frame image, if the probability of the partition scene switching is greater than the first probability, the weight of the current frame image in the time domain filter corresponding to the partition is increased, and the filtering window of the spatial domain filter corresponding to the partition is expanded; if the probability of the partition scene switching is less than the second probability, the weight of the current frame image in the time domain filter corresponding to the partition is reduced, and the filtering window of the spatial domain filter corresponding to the partition is reduced.

[0170] Among them, the first probability is higher than the second probability.

[0171] The temporal filter and the spatial filter may be run under default parameters. When the predicted partition scene switching probability is greater than the first probability or less than the second probability, the parameters of the temporal filter and the spatial filter are adjusted.

[0172] Specifically, when the probability of partition scene switching is greater than the first probability, such as 0.5, it means that the brightness change of the partition scene is large. Based on the probability of partition scene switching, the weight of the current frame image in the time domain filter corresponding to the partition can be increased, and the filtering window of the spatial domain filter corresponding to the partition can be expanded.

[0173] When the probability of partition scene switching is less than the second probability, such as 0.1, it means that the brightness change of the partition scene is very small, then the filtering strength of the time domain filter and the spatial domain filter can be weakened, that is, based on the probability of partition scene switching, the weight of the current frame image in the time domain filter corresponding to the partition is reduced, and the filtering window of the spatial domain filter corresponding to the partition is narrowed.

[0174] The weight of the current frame image in the temporal filter and the size of the filter window in the spatial filter are both positively correlated with the probability of scene switching.

[0175] When the probability of partition scene switching is between the first probability and the second probability, such as 0.3, the parameters of the temporal filter and the spatial filter may not be adjusted, or the weight of the current frame image in the temporal filter may be adjusted to 50%, and the spatial filter may operate under default parameters, such as a filter window size of 5×5.

[0176] Step S607: adjusting the backlight brightness of the LED lamp beads corresponding to each partition through the adjusted filters corresponding to each partition.

[0177] In this embodiment, scene switching probability analysis is performed based on the model, which improves the accuracy of scene switching probability analysis and thus improves the accuracy of backlight adjustment; backlight control is performed through two filters, namely time domain filter and spatial domain filter, which improves the precision of backlight control, and the parameters of the filter are determined based on the scene switching probability, and the filter parameter control accuracy is high, which improves the precision of backlight modulation, realizes dynamic and real-time adjustment of filter parameters, and improves the timeliness of backlight adjustment.

[0178] In some embodiments, since the light emitted by the lamp beads has a scattering characteristic, the light emitted by adjacent lamp beads will overlap. Based on this, the lamp beads can be divided into a non-overlapping area and an overlapping area.

[0179] Figure 9 A schematic diagram of non-overlapping partitions and overlapping partitions provided in an embodiment of the present application is shown in FIG. Figure 9 As shown, a frame image is divided into multiple partitions. For two partitions adjacent to each other in the vertical direction, the upper partition can be divided into a non-overlapping area and an overlapping area, wherein the overlapping area is adjacent to the lower partition.

[0180] In two vertically adjacent partitions, the area corresponding to the last k rows of pixels in the upper partition (k rows of pixels close to the lower partition) can be determined as the overlapping area of ​​the partitions, and k can be 3, 5 or other values.

[0181] Different methods may be used to calculate the brightness statistics of the overlapping area and the non-overlapping area, thereby combining the brightness statistics of the overlapping area and the non-overlapping area of ​​the same partition to obtain the brightness statistics of the partition.

[0182] Figure 10 A flow chart of another backlight modulation method provided in an embodiment of the present application is provided. In this embodiment, the image partition is further divided into overlapping areas and non-overlapping areas. The method can be executed by an upstream module of the regional dimming module, such as a CPU on the application side, or an image processing module, such as Figure 10 As shown, the backlight modulation method may include the following steps:

[0183] Step S1001 : For each partition of the current frame image, calculate the brightness statistics of the non-overlapping area of ​​the partition.

[0184] The number of pixels in different image partitions may vary. Partitioning can be done as evenly as possible. For example, if an image of size 2510×2800 is divided into 50×50 partitions, the first 49 rows of partitions can be 50×56, and the last row of partitions can be 60×56.

[0185] Illustratively, the brightness statistics of the non-overlapping area may be the sum of the pixel values ​​of each pixel (also referred to as pixel) in the non-overlapping area.

[0186] The pixel value of a pixel may be the maximum color value among the color values ​​of multiple color channels of the pixel.

[0187] Step S1002 : for pixels in the overlapping area of ​​each partition of the current frame image, a weight coefficient of the pixel with respect to the partition is determined based on the row where the pixel is located.

[0188] Step S1003 : calculating brightness statistics of the overlapping area of ​​the partitions based on the weight coefficient of each pixel in the overlapping area of ​​the partitions with respect to the partition.

[0189] For pixels in the overlapping area of ​​partitions, since the pixel values ​​of the pixels in the overlapping area will be affected by the lamp beads of the adjacent partition, specifically by the lamp beads of the partition located vertically below the partition, it is necessary to determine the weight coefficient of the pixel value in the corresponding partition based on the position of the pixel, such as the row or the row and column.

[0190] An overlapping area corresponds to a set of upper partitions and lower partitions. The upper partition is the partition where the overlapping area is located, and the lower partition is the partition adjacent to the upper partition in the longitudinal direction and located below the upper partition.

[0191] The closer a pixel in the overlapping area is to the lower partition, the smaller the weight coefficient of the pixel in the upper partition.

[0192] The brightness statistics value of the overlapping area may be a weighted sum of the pixel values ​​of each pixel in the overlapping area.

[0193] Figure 10 Taking the parallel calculation of brightness statistics of non-overlapping areas and overlapping areas of the same partition as an example, in some embodiments, the brightness statistics of non-overlapping areas and overlapping areas of the same partition can also be calculated serially, which is not limited in this application.

[0194] Step S1004 : Obtaining brightness statistics of the partition based on the brightness statistics of the non-overlapping areas of the partitions and the brightness statistics of the overlapping areas of the partitions.

[0195] The brightness statistics of a partition may be the sum of the brightness statistics of the non-overlapping area and the brightness statistics of the overlapping area within the partition, or a weighted sum.

[0196] In some embodiments, the brightness statistics of a partition also need to be determined based on the pixel values ​​of the pixels in the overlapping area of ​​the upper partition of the partition and the weight coefficients of the pixels in the overlapping area of ​​the upper partition with respect to the partition. The upper partition of the partition is a partition that is located above the partition in the vertical direction (the direction of the column) and adjacent to the partition.

[0197] An overlapping region affects the calculation of brightness statistics for two vertically adjacent partitions, designated the upper and lower partitions. For overlapping regions within the lower partition, pixels within the overlapping region contribute to the brightness statistics of the corresponding upper and lower partitions in different proportions, i.e., their weight coefficients differ, depending on their location.

[0198] Specifically, for each overlapping area, based on the row, or row and column, where the pixel is located in the overlapping area, a weight coefficient of the pixel with respect to the upper partition and the lower partition corresponding to the overlapping area is determined.

[0199] For example, taking the overlapping area including 5 rows of pixels as an example, from the 1st row to the 5th row from top to bottom, the contribution ratios or weight coefficients of the pixels in the 1st row to the 5th row to the upper partition and the lower partition are (80%, 20%), (60%, 40%), (50%, 50%), (40%, 60%) and (20%, 80%), respectively, where (80%, 20%) means that the contribution ratio or weight coefficient of the pixel to the upper partition is 80%, and the contribution ratio or weight coefficient to the lower partition is 20%.

[0200] The brightness statistics of a partition can be determined by three brightness statistics: the brightness statistics of the non-overlapping area within the partition (referred to as the first statistics), the brightness statistics of the overlapping area within the partition (referred to as the second statistics), and the brightness statistics of the pixels in the overlapping area of ​​the upper partition of the partition calculated under the weight coefficient of the partition (the lower partition) (referred to as the third statistics). The partition brightness statistics can be a weighted sum of the first statistics, the second statistics, and the third statistics.

[0201] When calculating the brightness statistics of the partition, the weight coefficients of the first statistics, the second statistics, and the third statistics may be set based on experience or adopt default values.

[0202] For example, when calculating the brightness statistics of the partitions, the weight coefficient of the first statistical value, i.e., the brightness statistics of the non-overlapping area, can be 1, and the weight coefficients of the second statistical value and the third statistical value are less than 1. The weight coefficients of the second statistical value and the third statistical value can be the same, such as both are 0.5, or they can be different.

[0203] The brightness statistics calculated for the overlapping area under the weight coefficient for the lower partition are similar to those for the overlapping area, with only the weight coefficients for the pixels being adjusted from the weight coefficient for the current partition to the weight coefficient for the lower partition.

[0204] In some embodiments, since the number of pixels in different partitions may be different, in order to eliminate the influence of the number of pixels on the brightness statistics, the ratio of the brightness statistics of the partition to the number of pixels in the partition can also be calculated to obtain the brightness mean of the partition, and the brightness mean is used instead of the brightness statistics for subsequent processing.

[0205] Optionally, compare the brightness statistics of each partition of the current frame image and the previous frame image, including:

[0206] For each partition of the current frame image, calculate the sum of the brightness statistics of the non-overlapping areas of the partition and the brightness statistics of the overlapping areas of the partition and divide it by the number of pixels in the partition to obtain the brightness mean of the partition; compare the brightness mean of each partition of the current frame image and the previous frame image.

[0207] After calculating the brightness statistics or brightness mean of each partition of the current frame image, a brightness histogram or brightness mean histogram of the current frame image may be drawn and saved based on the brightness statistics or brightness mean of each partition of the current frame image.

[0208] Step S1005 : comparing the brightness statistics of each subregion of the current frame image and the previous frame image.

[0209] When comparing the brightness statistics or brightness mean values ​​of each subregion of two adjacent image frames, the comparison may be performed based on a brightness histogram or a brightness mean histogram to obtain a comparison result.

[0210] The difference or ratio of the brightness statistics of each partition between the current frame image and the previous frame image may be calculated according to the partition to obtain a comparison result.

[0211] Step S1006 : Send the comparison result to the local dimming module, so that the local dimming module performs backlight modulation based on the comparison result.

[0212] The local dimming module can obtain a coefficient matrix for backlight reconstruction based on the comparison results of the values ​​of each partition in the comparison results with the preset values, so as to perform backlight modulation based on the coefficient matrix, that is, control the brightness of the LED lamp beads on the Mini LED lamp board based on the coefficient matrix.

[0213] Optionally, sending the comparison result to the regional dimming module includes sending the comparison result and the image data of the current frame image to the regional dimming module. The regional dimming module adjusts the backlight brightness of the LED lamp beads corresponding to each partition based on the comparison result, so as to display the current frame image based on the adjusted backlight brightness of the LED lamp beads.

[0214] In this embodiment, by further dividing the lamp bead partitions into overlapping areas and non-overlapping areas, the pixels in the overlapping areas affect the calculation of the brightness statistics of the upper and lower partitions according to a certain contribution ratio, which fully considers the luminous characteristics of the lamp beads on the MiniLED lamp board, improves the accuracy of the calculation of the partition brightness statistics, and thus improves the accuracy of backlight modulation.

[0215] Figure 11 A flow chart of another backlight modulation method provided in an embodiment of the present application. Figure 10 On the basis of the embodiment shown, further limitations are placed on steps S1001, S1003 to S1005, and a step is added before step S1004 of calculating the brightness statistics of the overlapping area with respect to the lower partition based on the pixel values ​​of the pixels in the overlapping area of ​​the upper partition, as shown in FIG. Figure 11 As shown, the backlight modulation method provided in this embodiment specifically includes the following steps:

[0216] Step S1101 : for each partition of the current frame image, the sum of the maximum color values ​​of each pixel in the non-overlapping area of ​​the partition is calculated to obtain the brightness statistics of the non-overlapping area.

[0217] The maximum color value of a pixel is the maximum value among the color values ​​of different color channels of the pixel (such as channels in color spaces such as RGB and XYZ).

[0218] Step S1102 : For a pixel in the overlapping area, based on the row where the pixel is located, determine a weight coefficient of the pixel with respect to the partition and the sub-partition of the partition.

[0219] Step S1103 , for each partition of the current frame image, calculate the sum of the products of the maximum color value of each pixel in the overlapping area of ​​the partition and the weight coefficient of the corresponding pixel with respect to the partition, and obtain the brightness statistics of the overlapping area.

[0220] Step S1104, for each partition of the current frame image, calculate the sum of the maximum color values ​​of each pixel in the overlapping area of ​​the partition and the product of the weight coefficient of the corresponding pixel with respect to the lower partition of the partition, and obtain the brightness statistics of the overlapping area with respect to the corresponding lower partition.

[0221] Step S1105 : determining the brightness statistics of the partition based on the brightness statistics of the non-overlapping areas of the partitions, the brightness statistics of the overlapping areas of the partitions, and the brightness statistics of the overlapping areas of the upper partitions of the partitions with respect to the partition.

[0222] The terms upper and lower are relative terms; the same partition can be either an upper or lower partition. A lower partition is defined as the partition below and adjacent to the partition along the pixel columns. An upper partition is defined as the partition above and adjacent to the partition along the pixel columns.

[0223] Since the overlapping area will affect the calculation of the brightness statistics of the upper and lower partitions, for each overlapping area, it is necessary to determine the weight coefficient of the pixel with respect to the upper partition (the partition where the pixel is located) and the lower partition (the lower partition of the partition where the pixel is located) based on the row, or the row and column where the pixel is located in the overlapping area, so as to add the maximum color value of the pixel proportionally to the brightness statistics of the upper and lower partitions based on the weight coefficient.

[0224] Specifically, the brightness statistics of the partition may be determined based on the weighted sum of the brightness statistics of the non-overlapping area of ​​the partition, the brightness statistics of the overlapping area of ​​the partition, and the brightness statistics of the overlapping area of ​​the upper partition of the partition with respect to the partition.

[0225] Step S1106 : For each partition of the current frame image, calculate the sum of the brightness statistics of the non-overlapping area of ​​the partition and the brightness statistics of the overlapping area of ​​the partition, and divide it by the number of pixels in the partition to obtain the brightness mean of the partition.

[0226] Step S1107 , calculating the sum of the difference between the brightness mean of each partition of the current frame image and the brightness mean of the corresponding partition of the previous frame image and the preset color value to obtain a difference matrix.

[0227] After obtaining the brightness mean of each partition of a frame image, a histogram of the brightness mean may be drawn and saved.

[0228] During the comparison, the histogram of the brightness mean of the current frame image can be subtracted from the histogram of the brightness mean of the previous frame image to obtain a difference histogram. To facilitate the subsequent determination of the backlight brightness coefficient, the value in the difference histogram can be updated to the sum of the value and the preset color value to obtain a difference matrix.

[0229] For example, taking the color value range of 0 to 255 as an example, the preset color value may be 128.

[0230] The CPU can send the image data of the current frame image and the difference matrix (i.e., comparison result) corresponding to the current frame image and the previous frame image to the local dimming module, and the local dimming module performs backlight modulation based on the difference matrix to control the display of the current frame image.

[0231] In some embodiments, after the difference matrix is ​​obtained, the difference matrix may be further processed, and the processing result may be sent to the local dimming module as a comparison result, such as the processing method provided in step S1108.

[0232] Step S1108 : determining the backlight brightness coefficient of each partition based on the comparison result of the element corresponding to each partition in the difference matrix and the preset color value, and obtaining a coefficient matrix.

[0233] The backlight brightness coefficient is positively correlated with the difference between the element value and the preset color value.

[0234] The backlight brightness coefficient of the partition corresponding to the element whose median value in the difference matrix is ​​greater than the preset color value is greater than 1, the backlight brightness coefficient of the partition corresponding to the element whose median value in the difference matrix is ​​less than the preset color value is less than 1, and the backlight brightness coefficient of the partition corresponding to the element whose median value in the difference matrix is ​​the preset color value is equal to 1.

[0235] Optionally, determining the backlight brightness coefficient of each partition based on a comparison result between an element corresponding to each partition in the difference matrix and the preset color value includes:

[0236] For each partition, if the element corresponding to the partition in the difference matrix is ​​greater than or equal to the preset color value, the backlight coefficient corresponding to the partition is determined to be the ratio of the element corresponding to the partition to the preset color value; if the element corresponding to the partition in the difference matrix is ​​less than the preset color value, the backlight coefficient corresponding to the partition is determined to be 1 minus the ratio of the element corresponding to the partition to the preset color value.

[0237] Taking the preset color value of 128 as an example, for each element in the difference matrix, if the value x of the element is greater than 128, the backlight brightness coefficient of the partition corresponding to the element is x / 128, which is a coefficient greater than 1; if the value of the element is less than 128, the backlight brightness coefficient of the partition corresponding to the element is 1-x / 128, which is a coefficient less than 1.

[0238] Step S1109 : sending the coefficient matrix and the image data of the current frame image to the local dimming module.

[0239] In step S1110 , the backlight brightness of the LED lamp beads corresponding to each partition is adjusted based on the coefficient matrix via the regional dimming module, so as to display the current frame image based on the adjusted backlight brightness of the LED lamp beads.

[0240] The regional dimming module can update the backlight brightness of the LED lamp bead to the product of the backlight brightness and the backlight brightness coefficient of the partition corresponding to the LED lamp bead in the coefficient matrix, that is, new_LED_value = old_LED_value*C, where new_LED_value and old_LED_value are the backlight brightness of the LED lamp bead after and before modulation, respectively, and C is the backlight brightness coefficient of the partition corresponding to the LED lamp bead.

[0241] In this embodiment, the maximum color value is used to represent the pixel value of the pixel, which simplifies the calculation of the brightness statistical value while taking into account the accuracy of the calculation; the brightness statistical value of the lower partition is calculated in combination with the overlapping area of ​​the upper partition, which fully considers the characteristics of the Mini LED light board, improves the accuracy of the calculation of the partition brightness statistical value, and thus improves the accuracy of the backlight modulation; the coefficient matrix is ​​calculated based on the preset color value, so that the control of the LED lamp bead backlight is realized based on the coefficient matrix, with low calculation complexity and high backlight modulation efficiency.

[0242] Figure 12 A schematic diagram of the hardware architecture for implementing the method provided in the embodiment of the present application is shown as follows: Figure 12As shown, a CPU is deployed on the application side. The image data input to the CPU can be provided by a GPU or an image signal processor ISP. The image data output by the image signal processor ISP comes from the image captured by the camera. The CPU is used to process the input image data to obtain parameters for backlight modulation, such as the above-mentioned comparison results, difference matrix, coefficient matrix, etc. The CPU splices the parameters for backlight modulation at the end of the corresponding image data to obtain processed image data, and sends the processed image data to the regional dimming module in the image processing chip. The regional dimming module reconstructs the backlight based on the parameters for backlight modulation in the processed image data to obtain the backlight values ​​of each partition of the corresponding image. The image processing chip can also perform liquid crystal pixel compensation operations to further correct the backlight values ​​of each partition.

[0243] During the liquid crystal pixel compensation operation, it can be performed based on the image data of the current frame image, the comparison result, and the backlight value of each partition of the current frame image after backlight reconstruction.

[0244] The image processing chip transmits the image data of each frame and the backlight value of each partition of each frame to the Mini LED display panel through MIPI (Mobile Industry Processor Interface), so as to control the driver board of the Mini LED display panel and the LED lamp beads of the Mini LED light board to realize the display of each frame of image and backlight modulation.

[0245] Figure 13 A flow chart of another backlight modulation method provided in an embodiment of the present application is shown as follows: Figure 13 As shown, taking the first frame image (image 1) and the second frame image (image 2) as examples, the processing processes of image 1 and image 2 are similar, and only image 2 is used as an example for explanation. After obtaining the image data of image 2, the CPU counts the maximum color value histogram of the pixels in image 2, divides it by the number of pixels in the partition, and obtains the maximum color value mean histogram of the pixels in image 2. The maximum color value histogram of the pixels is used to describe the distribution of the sum of the maximum color values ​​of the pixels in each partition of the image, and the maximum color value mean histogram of the pixels is used to describe the distribution of the ratio of the sum of the maximum color values ​​of the pixels in each partition of the image to the number of pixels in the partition; compare the maximum color value mean histogram of the pixels of image 2 and image 1 to obtain a difference matrix; the data obtained by splicing the difference matrix with the image data of image 2, such as 2500 bytes of data, is sent to the image processing chip for backlight reconstruction and liquid crystal pixel compensation.

[0246] The present application also provides a backlight modulation device, including:

[0247] The partition brightness statistics module is used to obtain the brightness statistics of each partition of the current frame image; the comparison module is used to compare the brightness statistics of each partition of the current frame image and the previous frame image; the backlight adjustment module is used to adjust the backlight brightness of the LED lamp beads based on the comparison results.

[0248] Optional backlight adjustment module, including:

[0249] A switching probability prediction unit is used to determine the probability of scene switching in each partition of the current frame image based on the comparison result and a pre-trained scene switching probability prediction model; a filter parameter adjustment unit is used to adjust the parameters of the filter corresponding to each partition based on the probability of scene switching in each partition of the current frame image; and a backlight reconstruction unit is used to adjust the backlight brightness of the LED lamp beads corresponding to each partition through the adjusted filters corresponding to each partition.

[0250] Optionally, the comparison result includes a difference in brightness statistics of each partition, and the switching probability prediction unit is specifically configured to:

[0251] For each partition, the ratio of the difference between the brightness statistics of the partition and the brightness statistics of the partition in the previous frame image is calculated to obtain an input matrix; the input matrix is ​​input into a pre-trained scene switching probability prediction model, and the probability of scene switching in each partition of the current frame image is predicted based on the pre-trained scene switching probability prediction model.

[0252] Optionally, the device further includes a model training module for:

[0253] Obtain brightness statistics of each partition of a multi-frame sample image, wherein the multi-frame sample image includes a plurality of sample image pairs, and the sample images included in the sample image pairs are divided into a current frame sample image and a previous frame sample image; for each sample image pair, calculate the difference between the brightness statistics of the current frame sample image and the brightness statistics of each partition of the previous frame sample image in the sample image pair, and the ratio of the brightness statistics of each partition of the previous frame sample image in the sample image pair to obtain an input matrix corresponding to the sample image pair; obtain the probability true value of the current frame sample image annotation in each sample image pair, wherein the probability true value includes the true value of the probability of scene switching of each partition of the current frame sample image; based on the input matrix and probability true value corresponding to each sample image pair, train a scene switching probability prediction model, and update the parameters of the scene switching probability prediction model by using the loss value calculated by the probability predicted by the scene switching probability prediction model and the corresponding probability true value.

[0254] Optionally, the filter includes a time domain filter and a space domain filter, and the filter parameter adjustment unit is specifically used to:

[0255] For each partition of the current frame image, when the probability of scene switching of the partition is greater than the first probability, the filtering strength of the time domain filter corresponding to the partition is reduced, and the filter strength of the spatial domain filter corresponding to the partition is increased; when the probability of scene switching of the partition is less than the second probability, the filtering strength of the time domain filter corresponding to the partition is increased, and the filter strength of the spatial domain filter corresponding to the partition is reduced.

[0256] Optionally, a filter parameter adjustment unit is specifically used to:

[0257] For each partition of the current frame image, if the probability of the partition scene switching is greater than the first probability, the weight of the current frame image in the time domain filter corresponding to the partition is increased, and the filtering window of the spatial domain filter corresponding to the partition is expanded; if the probability of the partition scene switching is less than the second probability, the weight of the current frame image in the time domain filter corresponding to the partition is reduced, and the filtering window of the spatial domain filter corresponding to the partition is narrowed; wherein, the first probability is higher than the second probability.

[0258] Optional, partition brightness statistics module, specifically used for:

[0259] Obtain the brightness statistics of each lamp bead partition of the current frame image, where one lamp bead partition corresponds to one LED lamp bead; downsample the brightness statistics of each lamp bead partition of the current frame image to obtain the brightness statistics of each partition of the current frame image.

[0260] Optionally, the partitions are divided into non-overlapping areas and overlapping areas, and two partitions adjacent in the vertical direction correspond to the same overlapping area; the partition brightness statistics module includes:

[0261] The first statistical unit is used to calculate the brightness statistical value of the non-overlapping area of ​​each partition of the current frame image; the second statistical unit is used to determine the weight coefficient of the pixel with respect to the partition based on the row in which the pixel is located, and calculate the brightness statistical value of the overlapping area of ​​the partition based on the weight coefficient of each pixel in the overlapping area of ​​the partition with respect to the partition; the partition statistical unit is used to obtain the brightness statistical value of the partition for each partition of the current frame image based on the brightness statistical value of the non-overlapping area of ​​the partition and the brightness statistical value of the overlapping area of ​​the partition.

[0262] Optionally, the first statistical unit is specifically configured to:

[0263] For each partition of the current frame image, the sum of the maximum color values ​​of each pixel in the non-overlapping area of ​​the partition is calculated to obtain the brightness statistics of the non-overlapping area; the maximum color value of a pixel is the maximum value among the color values ​​of different color channels of the pixel.

[0264] Optionally, the second statistical unit is specifically used to:

[0265] For the pixels in the overlapping areas of each partition of the current frame image, the weight coefficient of the pixel with respect to the partition is determined based on the row where the pixel is located, and the sum of the maximum color value of each pixel in the overlapping area of ​​the partition and the product of the weight coefficient of the corresponding pixel with respect to the partition is calculated to obtain the brightness statistics of the overlapping area.

[0266] Optionally, the device further includes a third statistical unit, configured to:

[0267] For the pixels in the overlapping area of ​​the partitions, determine the weight coefficient of the pixel with respect to the lower partition of the partition based on the row where the pixel is located; calculate the sum of the maximum color values ​​of each pixel in the overlapping area of ​​the partitions and the product of the weight coefficient of the corresponding pixel with respect to the lower partition of the partition to obtain the brightness statistics of the overlapping area with respect to the corresponding lower partition.

[0268] Accordingly, the zoning statistical unit is specifically used for:

[0269] The brightness statistics of the partition are determined based on the brightness statistics of the non-overlapping areas of the partitions, the brightness statistics of the overlapping areas of the partitions, and the brightness statistics of the overlapping areas of the upper partitions of the partitions with respect to the partitions; wherein the lower partition of the partition is a partition that is located below the partition and adjacent to the partition along the direction of the pixel columns, and the upper partition of the partition is a partition that is located above the partition and adjacent to the partition along the direction of the pixel columns.

[0270] Optional, comparison module, including:

[0271] A brightness mean calculation unit is used to calculate, for each partition of the current frame image, the sum of the brightness statistics of the non-overlapping area of ​​the partition and the brightness statistics of the overlapping area of ​​the partition, divided by the number of pixels in the partition, to obtain the brightness mean of the partition; a comparison unit is used to compare the brightness mean of each partition of the current frame image and the previous frame image.

[0272] Optionally, a comparison unit includes:

[0273] The difference matrix calculation subunit is used to calculate the difference between the brightness mean of each partition of the current frame image and the brightness mean of the corresponding partition of the previous frame image and the preset color value to obtain a difference matrix; the coefficient matrix calculation subunit is used to determine the backlight brightness coefficient of each partition based on the comparison result of the elements corresponding to each partition in the difference matrix and the preset color value to obtain a coefficient matrix.

[0274] Optional coefficient matrix calculation subunit, specifically used for:

[0275] For each partition, if the element corresponding to the partition in the difference matrix is ​​greater than the preset color value, the backlight coefficient corresponding to the partition is determined to be the ratio of the element corresponding to the partition to the preset color value; if the element corresponding to the partition in the difference matrix is ​​less than the preset color value, the backlight coefficient corresponding to the partition is determined to be 1 minus the ratio of the element corresponding to the partition to the preset color value.

[0276] Optional backlight adjustment module, specifically used for:

[0277] The comparison result and the image data of the current frame image are sent to the regional dimming module; through the regional dimming module, based on the comparison result, the backlight brightness of the LED lamp beads corresponding to each partition is adjusted to display the current frame image based on the adjusted backlight brightness of the LED lamp beads.

[0278] The backlight modulation device provided in the embodiment of the present application can be used to implement the technical solution of the backlight modulation method provided in any of the above embodiments of the present application. Its implementation principle and technical effects are similar, and will not be repeated here in this embodiment.

[0279] Figure 14 This is a schematic diagram of the structure of a liquid crystal display device provided in an embodiment of the present application. Figure 14 As shown, the liquid crystal display device includes a liquid crystal display screen, an LED array, a graphics processor, a camera, an image signal processor, a central processing unit and an image processing chip.

[0280] The liquid crystal display is a carrier for displaying images; the LED array includes multiple LED lamp beads, which are the backlight source of the liquid crystal display; the image signal processor is used to process the image data collected by the camera to obtain a frame of image; the central processing unit is used to receive the image generated by the image signal processor or the graphics processor, calculate and store the brightness statistics of each partition of the image, and compare them with the brightness statistics of each partition of the stored previous frame image to obtain a comparison result; the image processing chip includes a regional dimming module, and the regional dimming module is used to adjust the backlight brightness of the LED lamp beads in the LED array based on the comparison result.

[0281] The central processing unit and image processing chip in the liquid crystal display device provided in this embodiment can be used to execute the backlight modulation method provided in any embodiment of the present application. Its implementation principle and technical effects can be found in the aforementioned embodiments and will not be repeated here.

[0282] An embodiment of the present application further provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, the method provided in any of the aforementioned embodiments can be implemented.

[0283] An embodiment of the present application further provides a computer program product, including a computer program, which implements the method provided in any of the aforementioned embodiments when executed by a processor.

[0284] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of modules is merely a logical function division. In actual implementation, other division methods may be used. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not implemented.

[0285] The above-mentioned integrated module implemented in the form of a software functional module can be stored in a computer-readable storage medium. The above-mentioned software functional module is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) or a processor to perform some steps of the method described in each embodiment of the present application.

[0286] It should be understood that the above-mentioned processor can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the application can be directly embodied as being executed by a hardware processor, or can be executed by a combination of hardware and software modules in the processor. The memory may include high-speed memory, and may also include non-volatile memory, such as at least one disk memory, and can also be a USB flash drive, a mobile hard disk, a read-only memory, a magnetic disk or an optical disk, etc.

[0287] The storage medium may be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as static random access memory, electrically erasable programmable read-only memory, erasable programmable read-only memory, programmable read-only memory, read-only memory, magnetic storage, flash memory, magnetic disk, or optical disk. The storage medium may be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0288] An exemplary storage medium is coupled to a processor, such that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be an integral part of the processor. The processor and storage medium can be located in an application-specific integrated circuit. Of course, the processor and storage medium can also exist as discrete components in an electronic device.

[0289] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0290] The serial numbers of the above embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0291] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods provided in each embodiment of the present application.

[0292] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of the present application and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, and the true scope and spirit of the present application are indicated by the following claims.

[0293] It should be understood that the present application is not limited to the exact structure described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A backlight modulation method, characterized in that: include: Get the brightness statistics of each partition of the current frame image; Compare the brightness statistics of each partition of the current frame image and the previous frame image; Based on the comparison results, the backlight brightness of the LED lamp beads is adjusted.

2. The method according to claim 1, characterized in that Based on the comparison results, adjust the backlight brightness of the LED lamp beads, including: Determining the probability of scene switching for each partition of the current frame image based on the comparison result and a pre-trained scene switching probability prediction model; Based on the probability of scene switching in each partition of the current frame image, the parameters of the filter corresponding to each partition are adjusted; The backlight brightness of the LED lamp beads corresponding to each partition is adjusted through the adjusted filters corresponding to each partition.

3. The method according to claim 2, characterized in that The comparison result includes a difference in brightness statistics of each partition. Based on the comparison result and a pre-trained scene switching probability prediction model, the probability of scene switching of each partition of the current frame image is determined, including: For each partition, calculate the ratio of the difference between the brightness statistics of the partition and the brightness statistics of the partition in the previous frame image to obtain an input matrix; The input matrix is ​​input into a pre-trained scene switching probability prediction model, and the probability of scene switching of each partition of the current frame image is predicted based on the pre-trained scene switching probability prediction model.

4. The method according to claim 3, characterized in that The training process of the scene switching probability prediction model includes: Obtaining brightness statistics of each partition of a plurality of sample frames, wherein the plurality of sample frames includes a plurality of sample image pairs, and the sample images included in the sample image pairs are divided into a current frame sample image and a previous frame sample image; For each sample image pair, calculate the difference between the brightness statistics of the current frame sample image and the brightness statistics of each partition of the previous frame sample image in the sample image pair, and the ratio of the brightness statistics of each partition of the previous frame sample image in the sample image pair to obtain the input matrix corresponding to the sample image pair; Obtaining a probability true value of the annotation of the current frame sample image in each sample image pair, wherein the probability true value includes a true value of the probability of scene switching of each partition of the current frame sample image; Based on the input matrix and probability truth value corresponding to each sample image pair, the scene switching probability prediction model is trained, and the parameters of the scene switching probability prediction model are updated by the loss value calculated by the probability predicted by the scene switching probability prediction model and the corresponding probability truth value.

5. The method according to claim 2, characterized in that The filter includes a time domain filter and a spatial domain filter. Based on the probability of scene switching in each partition of the current frame image, the parameters of the filter corresponding to each partition are adjusted, including: For each partition of the current frame image, when the probability of scene switching of the partition is greater than a first probability, reducing the filter strength of the temporal filter corresponding to the partition, and increasing the filter strength of the spatial filter corresponding to the partition; When the probability of scene switching of the partition is less than the second probability, increasing the filter strength of the time domain filter corresponding to the partition, and reducing the filter strength of the spatial domain filter corresponding to the partition; The first probability is higher than the second probability.

6. The method according to claim 2, characterized in that The filter includes a time domain filter and a spatial domain filter. Based on the probability of scene switching in each partition of the current frame image, the parameters of the filter corresponding to each partition are adjusted, including: For each partition of the current frame image, if the probability of scene switching of the partition is greater than the first probability, increase the weight of the current frame image in the temporal filter corresponding to the partition, and expand the filtering window of the spatial filter corresponding to the partition; If the probability of the partition scene switching is less than the second probability, reducing the weight of the current frame image in the time domain filter corresponding to the partition, and reducing the filtering window of the spatial domain filter corresponding to the partition; The first probability is higher than the second probability.

7. The method according to any one of claims 1 to 6, characterized in that Get the brightness statistics of each partition of the current frame image, including: Obtaining brightness statistics of each lamp bead partition of the current frame image, wherein one lamp bead partition corresponds to one LED lamp bead; Downsampling is performed on the brightness statistics of each lamp bead partition of the current frame image to obtain the brightness statistics of each partition of the current frame image.

8. The method according to claim 1, characterized in that The partitions are divided into non-overlapping areas and overlapping areas, and two adjacent partitions along the longitudinal direction correspond to the same overlapping area; Get the brightness statistics of each partition of the current frame image, including: For each partition of the current frame image, calculating brightness statistics of non-overlapping areas of the partition; For a pixel within the overlapping area of ​​the partitions, determining a weight coefficient of the pixel with respect to the partition based on the row where the pixel is located; Calculating brightness statistics of the overlapping area of ​​the partitions based on weight coefficients of each pixel in the overlapping area of ​​the partitions with respect to the partitions; The brightness statistics of the partitions are obtained based on the brightness statistics of the non-overlapping areas of the partitions and the brightness statistics of the overlapping areas of the partitions.

9. The method according to claim 8, characterized in that Calculating brightness statistics of non-overlapping areas of the partitions, including: Calculating the sum of the maximum color values ​​of each pixel in the non-overlapping area of ​​the partition to obtain the brightness statistics of the non-overlapping area; the maximum color value of a pixel is the maximum value among the color values ​​of different color channels of the pixel; Calculating brightness statistics of the overlapping area of ​​the partitions based on weight coefficients of pixels in the overlapping area of ​​the partitions with respect to the partitions includes: The sum of the products of the maximum color value of each pixel in the overlapping area of ​​the partitions and the weight coefficient of the corresponding pixel with respect to the partition is calculated to obtain the brightness statistics of the overlapping area.

10. The method according to claim 9, characterized in that The method further comprises: For a pixel in the overlapping area of ​​the partitions, determining a weight coefficient of the pixel with respect to a lower partition of the partition based on a row where the pixel is located; Calculating the sum of the maximum color values ​​of pixels in the overlapping area of ​​the partitions and the product of the weight coefficients of the corresponding pixels with respect to the lower partition of the partition to obtain a brightness statistical value of the overlapping area with respect to the corresponding lower partition; Obtaining brightness statistics of the partitions based on brightness statistics of non-overlapping areas of the partitions and brightness statistics of overlapping areas of the partitions includes: Determine a brightness statistic of the partition based on brightness statistics of non-overlapping areas of the partitions, brightness statistics of overlapping areas of the partitions, and brightness statistics of overlapping areas of upper partitions of the partitions with respect to the partition; The lower partition of the partition is a partition located below and adjacent to the partition along the direction of the pixel column, and the upper partition of the partition is a partition located above and adjacent to the partition along the direction of the pixel column.

11. The method according to claim 8, characterized in that Compare the brightness statistics of each partition of the current frame image and the previous frame image, including: For each partition of the current frame image, calculate the sum of the brightness statistics of the non-overlapping area of ​​the partition and the brightness statistics of the overlapping area of ​​the partition, and divide it by the number of pixels in the partition to obtain the brightness mean of the partition; Compare the brightness mean of each partition of the current frame image and the previous frame image.

12. The method according to claim 11, characterized in that The comparison result is a coefficient matrix, which compares the brightness mean of each partition of the current frame image and the previous frame image, including: Calculate the difference between the brightness mean of each partition of the current frame image and the brightness mean of the corresponding partition of the previous frame image and the preset color value to obtain a difference matrix; Based on the comparison result of the elements corresponding to each partition in the difference matrix and the preset color value, the backlight brightness coefficient of each partition is determined to obtain a coefficient matrix.

13. The method according to claim 12, characterized in that Determining a backlight brightness coefficient of each partition based on a comparison result between an element corresponding to each partition in the difference matrix and the preset color value includes: For each partition, if the element corresponding to the partition in the difference matrix is ​​greater than a preset color value, determining the backlight coefficient corresponding to the partition as a ratio of the element corresponding to the partition to the preset color value; If the element corresponding to the partition in the difference matrix is ​​smaller than the preset color value, the backlight coefficient corresponding to the partition is determined to be 1 minus the ratio of the element corresponding to the partition to the preset color value.

14. The method according to any one of claims 8 to 13, characterized in that: Based on the comparison results, adjust the backlight brightness of the LED lamp beads, including: Sending the comparison result and the image data of the current frame image to a local dimming module; The regional dimming module adjusts the backlight brightness of the LED lamp beads corresponding to each partition based on the comparison result, so as to display the current frame image based on the adjusted backlight brightness of the LED lamp beads.

15. A backlight modulation device, characterized in that: include: The partition brightness statistics module is used to obtain the brightness statistics of each partition of the current frame image; A comparison module is used to compare the brightness statistics of each partition of the current frame image and the previous frame image; The backlight adjustment module is used to adjust the backlight brightness of the LED lamp beads based on the comparison result.

16. A liquid crystal display device, characterized in that: include: LCD screen, LED array, graphics processor, camera, image signal processor, central processing unit and image processing chip; The liquid crystal display screen is a carrier for displaying images; the LED array includes a plurality of LED lamp beads, which are the backlight source of the liquid crystal display screen; The image signal processor is used to process the image data collected by the camera to obtain a frame of image; The central processing unit is used to receive the image generated by the image signal processor or the graphics processor, calculate and store the brightness statistics of each partition of the image, and compare it with the stored brightness statistics of each partition of the previous frame image to obtain a comparison result; The image processing chip includes a local dimming module, and the local dimming module is used to adjust the backlight brightness of the LED lamp beads in the LED array based on the comparison result.

17. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, and when a processor executes the computer-executable instructions, the method according to any one of claims 1 to 14 is implemented.

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