A display method, device and electronic equipment for Mini-LED liquid crystal display screen

Abstract

A display method, device and electronic device for a Mini‑LED liquid crystal display screen, relating to the field of data processing. In the method, a video to be displayed is obtained; the video to be displayed is divided into multiple frames of images; a frame difference detection is performed on a first image and a second image to detect whether there is a moving object in the first image and the second image; if it is determined that the movement speed is greater than or equal to a preset speed threshold, the moving direction and moving distance of the moving object from the first image to the second image are determined; the intermediate moving direction and intermediate moving distance of the moving object are predicted; an intermediate frame is generated according to the intermediate moving direction and intermediate moving distance; an intermediate frame is inserted into the first image and the second image, and each image frame is combined to obtain a target video; and the target video is displayed on a liquid crystal display screen. The technical solution provided in the present application is implemented to reduce the visual sense of jump by generating and inserting intermediate frames, thereby providing more visual information and reducing motion blur.

Description

Technical Field

[0001] The present application relates to the field of data processing, and specifically to a display method, device and electronic equipment for a Mini-LED liquid crystal display screen. Background Art

[0002] With the rapid development of display technology, Mini-LED, as an emerging display technology, has attracted widespread attention due to its excellent brightness and contrast control capabilities. Mini-LED technology uses micro-LEDs as backlight sources and achieves higher display performance by precisely controlling thousands of micro-lamp beads. Compared with traditional LED and OLED display technologies, Mini-LED can provide higher brightness, better light control and lower energy consumption. This makes Mini-LED technology widely used in professional monitors and portable devices, especially in situations where high dynamic range (HDR) and fine brightness control are required.

[0003] At present, although Mini-LED display technology has significant advantages in brightness and contrast, it still faces the problem of motion blur when processing fast-moving images. Motion blur is mainly caused by the fact that when the human eye is watching fast-moving objects, the refresh rate of the display screen cannot keep up with the speed of the object, resulting in a break in the visual continuity of the picture.

[0004] Therefore, there is an urgent need for a display method, device and electronic equipment for a Mini-LED liquid crystal display screen. Summary of the invention

[0005] The present application provides a display method, device and electronic device for a Mini-LED liquid crystal display screen, which generates and inserts intermediate frames to provide more visual information to reduce the visual jump feeling, thereby reducing motion blur.

[0006] In a first aspect of the present application, a display method for a Mini-LED liquid crystal display screen is provided, the method comprising: obtaining a video to be displayed; dividing the video to be displayed into multiple frames of images; performing frame difference detection on a first image and a second image to detect whether there is a moving object in the first image and the second image, the first image and the second image being any two adjacent frames of the multiple frames of the image, and the first image being the previous frame of the second image; if it is determined that the moving object exists in the first image and the second image, obtaining the moving speed of the moving object; if it is determined that the moving speed is greater than or equal to a preset speed threshold, determining that the moving object is The moving direction and moving distance from the first image to the second image; predicting the intermediate moving direction and intermediate moving distance of the moving object according to the moving direction and the moving distance, wherein the intermediate moving direction is the moving direction of the moving object at a preset time point, and the intermediate moving distance is the moving distance of the moving object at a preset time point, and the preset time point is a certain time point between the first image and the second image; generating an intermediate frame according to the intermediate moving direction and the intermediate moving distance; inserting the intermediate frame into the first image and the second image, and combining each image frame to obtain a target video; and displaying the target video on a liquid crystal display.

[0007] By adopting the above technical solution, the video content can be converted into a series of image frames by processing the video to be displayed in frames, so as to facilitate subsequent motion detection and prediction. Frame difference detection can sensitively capture the motion information in the image sequence, and by analyzing the differences between adjacent frames, it can be determined whether there is a moving object, providing a basis for motion prediction. By predicting the intermediate moving direction and intermediate moving distance of the moving object, intermediate frames are generated and inserted to smooth the motion trajectory and improve dynamic blur. The synthesis of intermediate frames makes full use of the information of existing frames. Through motion estimation and motion compensation technology, transition frames are constructed between known frames, which increases the temporal sampling density of the video, improves the continuity and smoothness of dynamic display, and reduces the motion blur caused by high-speed moving objects.

[0008] Optionally, obtaining the movement speed of the moving object specifically includes: tracking the moving object to obtain a position correspondence between the moving object in the first image and the second image; calculating a displacement of the moving object between the first image and the second image based on the position correspondence; obtaining a time interval between the first image and the second image; and obtaining the movement speed by calculation based on the displacement and the time interval.

[0009] By adopting the above technical solution, by tracking the moving object, a position correspondence can be established between adjacent frames to determine the displacement of the moving object. At the same time, considering the two factors of displacement and time interval, the motion speed can be accurately calculated to provide a quantitative basis for subsequent motion compensation. Target tracking can eliminate interference factors such as background and noise. By using the method of dividing the displacement by the time interval, the motion speed can be directly estimated in the pixel domain, which is simple and efficient.

[0010] Optionally, generating an intermediate frame according to the intermediate moving direction and the intermediate moving distance specifically includes: extracting an image block of the moving object in the first image; calculating the motion displacement vector of the preset time point according to the intermediate moving direction and the intermediate moving distance; performing motion compensation on the image block according to the motion displacement vector to obtain a predicted image block at the preset time point; placing the predicted image block at a corresponding position of a blank frame to obtain a predicted image of the moving object in the intermediate frame; and copying the image content of the first image except the moving object to the blank frame to obtain the intermediate frame.

[0011] By adopting the above technical solution, by extracting the image blocks of the moving object, the motion information can be localized and the amount of data can be reduced. Using the known intermediate motion direction and intermediate moving distance, the motion displacement vector at the preset time point can be calculated to complete the prediction of the moving object. Motion compensation is performed on the image block, and the corresponding area is found in the reference frame using the motion displacement vector to generate the predicted image block, which can achieve sub-pixel motion matching, improve prediction accuracy, and smooth the motion trajectory. Filling the predicted image block to the corresponding position of the blank frame, and then copying the static background from the reference frame, constitutes a complete intermediate frame, which plays the role of connecting the previous and next frames and compensating for motion residuals.

[0012] Optionally, performing motion compensation on the image block according to the motion displacement vector to obtain the predicted image block at the preset time point specifically includes: determining a reference position of the predicted image block in the first image according to the motion displacement vector; and generating a pixel value of the reference position through an interpolation algorithm to obtain the predicted image block.

[0013] By adopting the above technical solution, the motion relationship between image blocks in adjacent frames can be determined through motion displacement vectors. Since the actual motion displacement is usually not an integer pixel, the pixel values ​​are interpolated in the reference frame to generate fractional pixel points, thereby achieving sub-pixel motion compensation and improving prediction accuracy.

[0014] Optionally, the performing frame difference detection on the first image and the second image to detect whether there is a moving object in the first image and the second image specifically includes: performing grayscale processing and frame difference processing on the first image and the second image to obtain a frame difference image; performing binarization processing on the frame difference image to obtain a binarized image; and determining whether there is a moving object in the first image and the second image based on the binarized image.

[0015] By adopting the above technical solution, by graying and differential processing adjacent frames, the change information between two frames can be extracted, the influence of static background can be eliminated, and the moving area can be highlighted. Binarizing the differential result can convert the change image into a black and white image, highlighting the significant moving object and reducing noise interference. The frame differential detection method can sensitively capture the motion information between adjacent frames. By subtracting the static background, the moving object can be quickly and accurately located, providing a reliable basis for motion speed estimation and motion compensation. Graying reduces the complexity of inter-frame comparison, and the binarization operation further simplifies the motion representation, facilitating real-time calculation.

[0016] Optionally, determining whether there is a moving object in the first image and the second image based on the binary image specifically includes: performing a connected domain analysis on the binary image to extract candidate moving areas; calculating feature quantities of the candidate moving areas, the feature quantities including area, aspect ratio and center position; screening the candidate moving areas based on the feature quantities to obtain a screened area; judging whether the screened area has continuity in multiple frames of the image, and judging whether there is a moving object in the first image and the second image based on the continuity.

[0017] By adopting the above technical solution, through connected domain analysis, independent motion areas can be segmented from the binarization results, eliminating the influence of isolated noise points. For the candidate areas, feature quantities such as area, aspect ratio, and center position are extracted to characterize the morphology and position attributes of the moving object, which is convenient for the subsequent screening of the screening area. By using the feature quantities to screen the candidate areas, pseudo targets such as those that are too small, too narrow, and out of bounds can be eliminated, and the motion areas that are most likely to correspond to real moving objects can be retained, thereby improving the accuracy of motion detection. By tracking and analyzing the continuity of the screening area in multiple frames, the authenticity of the moving target can be further verified, reducing misjudgments and missed judgments.

[0018] Optionally, determining whether the screening area has continuity in multiple frames of the image, and determining whether there are moving objects in the first image and the second image based on the continuity, specifically includes: calculating the change amount of the feature quantity in the multiple frames of the image; determining whether the change amount is less than a preset change threshold; if it is determined that the change amount is less than the preset change threshold, determining that the screening area has continuity in the multiple frames of the image, and further determining that there are moving objects in the first image and the second image; if it is determined that the change amount is greater than or equal to the preset change threshold, determining that the screening area does not have continuity in the multiple frames of the image, and further determining that there are no moving objects in the first image and the second image.

[0019] By adopting the above technical solution and setting a preset change threshold, the continuity of the candidate area can be quantitatively judged. When the amount of change is less than the preset change threshold, the area is considered to be stable in multiple frames and is thus confirmed as a real moving target. Conversely, when the amount of change is greater than or equal to the preset change threshold, the area is considered to lack consistency and is excluded as a false target.

[0020] Optionally, predicting the intermediate moving direction and the intermediate moving distance of the moving object based on the moving direction and the moving distance specifically includes: obtaining the position change of the moving object in multiple frames of the image based on the moving direction and the moving distance; constructing a motion model of the moving object based on the position change; determining the moving position corresponding to a preset time point in the motion model; and determining the intermediate moving direction and the intermediate moving distance of the moving object relative to the first image at the preset time point based on the moving position.

[0021] By adopting the above technical solution, the motion trajectory of the moving object can be obtained by tracking and analyzing the position changes of the moving object in multiple frames. Based on the position changes, a motion model that describes the motion trend is established to reflect the motion law of the target. By using the motion model to predict the position of a preset time point, the motion direction and distance of the moving object at a specified time can be extrapolated based on the known motion state, providing a quantitative basis for the interpolation of intermediate frames.

[0022] In a second aspect of the present application, a display device of a Mini-LED liquid crystal display screen is provided, the device comprising: an acquisition module and a processing module, wherein: the acquisition module is used to acquire a video to be displayed; the processing module is used to divide the video to be displayed into multiple frames of images; the processing module is also used to perform frame difference detection on a first image and a second image, and detect whether there is a moving object in the first image and the second image, the first image and the second image are any two adjacent frames of the multiple frames of the image, and the first image is the previous frame of the second image; the processing module is also used to obtain the movement speed of the moving object if it is determined that the moving object exists in the first image and the second image; the processing module is also used to obtain the movement speed of the moving object if it is determined that the movement speed is greater than or equal to a preset speed threshold, The moving direction and moving distance of the moving object from the first image to the second image are determined; the processing module is also used to predict the intermediate moving direction and intermediate moving distance of the moving object according to the moving direction and the moving distance, the intermediate moving direction is the moving direction of the moving object at a preset time point, the intermediate moving distance is the moving distance of the moving object at a preset time point, and the preset time point is a certain time point between the first image and the second image; the processing module is also used to generate an intermediate frame according to the intermediate moving direction and the intermediate moving distance; the processing module is also used to insert the intermediate frame into the first image and the second image, and combine each image frame to obtain a target video; the processing module is also used to display the target video on a liquid crystal display.

[0023] In the third aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes any one of the methods described above.

[0024] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores instructions, and when the instructions are executed, any of the methods described above is executed.

[0025] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0026] 1. By performing frame processing on the video to be displayed, the video content can be converted into a series of image frames for subsequent motion detection and prediction. Frame difference detection can sensitively capture the motion information in the image sequence. By analyzing the differences between adjacent frames, it can be determined whether there is a moving object, providing a basis for motion prediction. By predicting the intermediate moving direction and the intermediate moving distance of the moving object, intermediate frames are generated and inserted to smooth the motion trajectory and improve dynamic blur. The synthesis of intermediate frames makes full use of the information of existing frames. Through motion estimation and motion compensation technology, transition frames are constructed between known frames, which increases the temporal sampling density of the video, improves the continuity and smoothness of dynamic display, and reduces the motion blur caused by high-speed moving objects.

[0027] 2. By tracking the moving object, a position correspondence can be established between adjacent frames to determine the displacement of the moving object. At the same time, considering the two factors of displacement and time interval, the motion speed can be accurately calculated to provide a quantitative basis for subsequent motion compensation. Target tracking can eliminate interference factors such as background and noise. The method of dividing the displacement by the time interval can directly estimate the motion speed in the pixel domain, which is simple and efficient.

[0028] 3. By extracting the image blocks of the moving object, the motion information can be localized and the amount of data can be reduced. Using the known intermediate motion direction and intermediate moving distance, the motion displacement vector at the preset time point can be calculated to complete the prediction of the moving object. Perform motion compensation on the image block, use the motion displacement vector to find the corresponding area in the reference frame, and generate the predicted image block, which can achieve sub-pixel motion matching, improve prediction accuracy, and smooth the motion trajectory. Fill the predicted image block to the corresponding position of the blank frame, and then copy the static background from the reference frame to form a complete intermediate frame, which plays the role of connecting the previous and next frames and compensating for motion residuals. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a flow chart of a display method of a Mini-LED liquid crystal display screen disclosed in an embodiment of the present application;

[0030] Figure 2 It is a module schematic diagram of a display device of a Mini-LED liquid crystal display screen disclosed in an embodiment of the present application;

[0031] Figure 3 It is a structural schematic diagram of an electronic device disclosed in an embodiment of the present application.

[0032] Explanation of the reference numerals: 201, acquisition module; 202, processing module; 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0033] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0034] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0035] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0036] The present application provides a display method of a Mini-LED liquid crystal display screen, referring to Figure 1 , Figure 1 This is a flow chart of a display method of a Mini-LED liquid crystal display screen provided in an embodiment of the present application. The method is applied to a user device. The user device mentioned in the present application refers to a liquid crystal display device equipped with a Mini-LED backlight system, which can be a smart watch, a monitor, a tablet computer, a mobile phone, or other terminal devices with display functions. The method includes steps S101 to S109, and the above steps are as follows:

[0037] Step S101: Obtain the video to be displayed.

[0038] In step S101, the video to be displayed is stored locally on the user device or on a storage device connected to the user device. The user device can directly read the video file in the specified path through the file system API and load it into the memory for subsequent processing.

[0039] Step S102: dividing the video to be displayed into multiple frames of images.

[0040] In step S102, the user device determines and analyzes the coding format of the video to be displayed. Different coding formats have different compression algorithms and data structures. The user device uses the corresponding decoder to decode the video to be displayed according to the corresponding coding standard. For example, for H.264-encoded videos, the user device can use the libx264 decoder in the FFmpeg library to decode the compressed video data into the original YUV420P pixel format. The decoded video data is stored and processed in frames. The user device cuts the continuous video data stream (video to be displayed) into discrete image frames according to the frame rate of the video (such as 25fps, 30fps, 60fps) and timestamp information. Specifically, the user device can determine the frame boundary by the time interval between frames (such as 40ms corresponding to 25fps).

[0041] Step S103: performing frame difference detection on the first image and the second image to detect whether there is a moving object in the first image and the second image, the first image and the second image are any two adjacent frames of multiple frames, and the first image is the previous frame of the second image.

[0042] In step S103, grayscale processing and frame difference processing are performed on the first image and the second image to obtain a frame difference image; the frame difference image is binarized to obtain a binarized image; and based on the binarized image, it is determined whether there is a moving object in the first image and the second image.

[0043] Specifically, the user device grayscales the first image and the second image. Grayscale is the process of converting a color image into a grayscale image. The user device combines the pixel values ​​of the three RGB channels into a grayscale value according to certain weights. For example, the user device uses a weighted average method to weight the pixel values ​​of the three RGB channels according to weight coefficients of 0.299, 0.587, and 0.114 to obtain the grayscale value of the corresponding pixel. Grayscale processing can reduce the color information of the image, highlight the light and dark contrast and texture features of the image, and facilitate subsequent frame difference and binarization processing.

[0044] After graying, the user device performs frame difference processing on the two grayscale images. Frame difference is an image subtraction operation in the time domain, which can be used to detect motion information in a video sequence. Specifically, the user device subtracts the grayscale value of the first image from the grayscale value of the second image to obtain a difference image between the two frames of images. In the difference image, the pixel value of the static area is close to 0, while the pixel value of the moving area is significantly deviated from 0. By setting a suitable threshold, the pixel values ​​in the difference image can be binarized to obtain a mask image of the moving area. In the binarization process, the user device selects a suitable threshold value based on the pixel value distribution of the difference image, sets the points with pixel values ​​greater than the threshold value to 1 (white), and sets the points with pixel values ​​less than or equal to the threshold value to 0 (black). The selection of the threshold value requires a balance between the sensitivity and false alarm rate of the detection, which can be determined by experimental and statistical methods, and the present application does not limit this. For example, it can be assumed that the pixel values ​​of the difference image obey a Gaussian distribution, and the threshold value is set to the mean plus or minus 3 times the standard deviation according to the 3σ principle. In the mask image obtained by binarization, the white area corresponds to the pixels that have changed significantly between the two frames, which is likely caused by a moving object.

[0045] Finally, the user device performs a connected domain analysis on the binary image to determine the position and size of the moving object. Connected domain analysis refers to extracting connected white pixel areas in the binary image and marking and measuring each connected domain. The user device can determine whether it corresponds to a real moving object based on the size, shape, and density characteristics of the connected domain. For example, the area threshold and aspect ratio threshold of the connected domain can be set to filter out connected domains that are too small or too narrow and reduce the interference of pseudo targets. If there is a connected domain that meets the conditions in the binary image, it is considered that there is a moving object between the first image and the second image; otherwise, it is considered that there is no significant motion change between the two frames of images and there is no moving object.

[0046] In a possible implementation, determining whether there is a moving object in the first image and the second image based on the binary image specifically includes: performing a connected domain analysis on the binary image to extract candidate moving areas; calculating feature quantities of the candidate moving areas, the feature quantities including area, aspect ratio, and center position; screening the candidate moving areas based on the feature quantities to obtain a screened area; determining whether the screened area has continuity in multiple frames of images, and determining whether there is a moving object in the first image and the second image based on the continuity.

[0047] Specifically, the user device performs a connected domain analysis on the binary image to extract candidate motion regions. A connected domain refers to a set of pixels in an image that have the same pixel value and are continuously adjacent. In a binary image, a connected domain corresponds to a potential moving target region. The user device uses the eight-neighborhood algorithm to scan the binary image row by row and column by column, and classifies each white pixel and its white pixels in the upper, lower, left, right, and four diagonal directions into the same connected domain, and marks different connected domains with different label numbers. After the scan is completed, all connected domains in the binary image, i.e., candidate motion regions, are obtained.

[0048] The user device calculates a series of feature quantities for each candidate motion region, including area, aspect ratio and center position. Area refers to the number of all pixels in a connected domain; aspect ratio refers to the ratio of the width to the height of the circumscribed rectangle of the connected domain; center position refers to the arithmetic mean of the coordinates of all pixels in the connected domain. These feature quantities reflect the size, shape and position attributes of the candidate motion region and can be used for subsequent region screening and tracking. For example, a connected domain with a smaller area may be caused by noise or background disturbance, and a connected domain with an excessively large area may correspond to the global motion of the entire screen; a connected domain with an aspect ratio close to 1 usually corresponds to a square or circular moving target, while a connected domain with a large or small aspect ratio may be caused by smear or occlusion; changes in the center position can reflect the motion trajectory and speed of the moving target.

[0049] The user device screens the candidate motion areas according to their feature quantities to obtain several screening areas that are most likely to correspond to real motion objects. The screening criteria are pre-set according to the specific application scenario and prior knowledge, including area threshold, aspect ratio threshold, and position constraint. For example, in face detection, the area threshold can be set according to the typical size of the face, the aspect ratio threshold can be set according to the aspect ratio of the face, and the position constraint can be set according to the possible position of the face in the picture. The candidate motion areas whose feature quantities meet all the screening conditions are retained, and other candidate motion areas that do not meet the requirements are eliminated, thereby obtaining a set of screened screening areas. The user device makes a continuity judgment on the screening area in multiple frames of images to further confirm whether it corresponds to a real motion object. Continuity refers to the continuous existence of the moving target in time and the smooth movement in space.

[0050] In a possible implementation, determining whether the screening area has continuity in multiple image frames, and determining whether there is a moving object in the first image and the second image based on the continuity, specifically includes: calculating the change amount of the feature quantity in the multiple image frames; determining whether the change amount is less than a preset change threshold; if it is determined that the change amount is less than the preset change threshold, determining that the screening area has continuity in the multiple image frames, and then determining that there is a moving object in the first image and the second image; if it is determined that the change amount is greater than or equal to the preset change threshold, determining that the screening area does not have continuity in the multiple image frames, and then determining that there is no moving object in the first image and the second image.

[0051] Specifically, for each screening area, the user device calculates the change in its feature quantity in multiple frames. Feature quantities include area, aspect ratio, and center position, and the change reflects the degree of difference between feature quantities in different frames. For area features, the difference or ratio between the area of ​​the current frame screening area and the area of ​​the previous and next frames is calculated; for center position features, the Euclidean distance or Manhattan distance between the center coordinates of the current frame screening area and the center coordinates of the previous and next frames is calculated. The calculation of the change can be performed between two frames or between multiple frames, using cumulative difference, average difference, maximum difference and other measurement methods. By calculating the change in feature quantity, the user device can quantify the degree of change in the screening area over time, providing a basis for subsequent continuity judgment. The user device compares the calculated change in feature quantity with the preset change threshold to determine whether the screening area has continuity in multiple frames. The preset change threshold is obtained based on experience or experimental statistics, and is used to distinguish the boundary value between continuous motion and discontinuous motion, and this application does not limit this. For example, for the area change, a percentage threshold (such as 20%) can be set. If the difference between the area of ​​the current frame filter area and the area of ​​the previous and next frame filter areas accounts for less than 20% of their average area, the area change is considered to be less than the preset change threshold; for the center position change, a pixel distance threshold (such as 10 pixels) can be set. If the distance between the center coordinates of the current frame filter area and the center coordinates of the previous and next frame filter areas is less than 10 pixels, the center position change is considered to be less than the preset change threshold. The user device compares the change of each feature quantity of each filter area with the corresponding preset change threshold to obtain a set of Boolean values, indicating whether the filter area meets the continuity requirements in each feature dimension.

[0052] The user device determines whether the screening area has continuity in the multiple frames of images based on the comparison result of the change amount of the feature quantity and the preset change threshold, and then determines whether there is a moving object in the first image and the second image. Specifically, if the change amounts of all feature quantities in the screening area are less than the corresponding preset change threshold, it is considered that the screening area has continuity in the multiple frames of images, that is, it corresponds to a real moving object; conversely, if the change amount of any feature quantity in the screening area is greater than or equal to the corresponding preset change threshold, it is considered that the screening area does not have continuity in the multiple frames of images, that is, it corresponds to noise, interference or false motion. After judging the continuity of all screening areas one by one, the user device can obtain a group of candidate moving objects with continuity. If the group is not empty, it is determined that there is a moving object in the first image and the second image; if the group is empty, it is determined that there is no moving object in the first image and the second image.

[0053] At the same time, when identifying moving objects, it is necessary to consider the possible appearance and disappearance of moving objects. Therefore, the judgment of continuity is not to calculate and analyze the changes between all frames. The server will define a continuity window and select a suitable "continuity window" size. For example, if the continuity window is set to 5 frames, it means that in any given 5 consecutive frames, the feature changes in the screening area meet the above continuity conditions, then it can be considered that there are moving objects in the first image and the second image.

[0054] For example, assume that the user device extracts three screening areas A, B, and C from the binarization results of the first image and the second image, and calculates the changes in their respective areas, aspect ratios, and center positions in five consecutive frames of images. After comparison with the preset change threshold, it is found that the area change and center position change of area A are both less than the preset change threshold, the area change of area B is less than the preset change threshold but the center position change is greater than the preset change threshold, and the area change and center position change of area C are both greater than the preset change threshold. Based on this, the user device determines that area A has continuity in multiple frames of images, while areas B and C do not have continuity in multiple frames of images. Furthermore, the user device determines that there is a moving object in the first image and the second image, and the corresponding moving area is the screening area A.

[0055] Step S104: If it is determined that there is a moving object in the first image and the second image, the moving speed of the moving object is obtained.

[0056] In step S104, target tracking is performed on the moving object to obtain a position correspondence between the moving object in the first image and the second image; based on the position correspondence, the displacement of the moving object between the first image and the second image is calculated; the time interval between the first image and the second image is obtained; and the movement speed is obtained by calculation based on the displacement and the time interval.

[0057] Specifically, the user device tracks the moving object in the first image and the second image to obtain the position correspondence between the two frames. Target tracking refers to the process of locating and identifying the same moving target in continuous video frames, including target representation, target matching and target update steps. Target tracking algorithms include region-based tracking, feature-based tracking, model-based tracking, etc. Preferably, in one embodiment of the present application, the user device uses a region-based Meanshift tracking algorithm to represent the moving region of the moving object as a color histogram model, and then searches for the region most similar to the color histogram model in the second image to achieve the positioning and matching of the moving object. In another embodiment of the present application, the user device uses a feature-based KLT tracking algorithm to extract significant feature points (such as corner points and edge points) of the moving object, and then searches for the corresponding positions of these feature points in the second image to achieve stable tracking of the moving target. Through target tracking, the user device can obtain the pixel coordinate position of the moving object in the first image and the second image, and establish a position correspondence between the two frames. The user device calculates the displacement under its pixel coordinates according to the position correspondence between the moving object in the first image and the second image. The displacement refers to the change in the spatial position of the moving object between two frames of images, which can be expressed by the difference in pixel coordinates. For example, the position coordinates of the moving object in the first image are (x1, y1), and the position coordinates in the second image are (x2, y2), then its displacement can be expressed as (Δx, Δy) = (x2-x1, y2-y1). If the displacement of the moving object in two directions is known, the modulus of its displacement can be further calculated, that is, sqrt(Δx^2+Δy^2). The calculation of the displacement can be based on a single tracking point or on the average or weighted average of multiple tracking points. Through the calculation of the displacement, the user device quantifies the moving direction and distance of the moving object at the pixel level between the two frames of images.

[0058] The user device estimates the pixel speed of the moving object based on the displacement of the moving object and the time interval between two frames of images. Speed ​​refers to the displacement of the moving object per unit time, which can be calculated by dividing the displacement by the time interval. For example, if the time interval between two frames of images is Δt, and the displacement of the moving object during this period is (Δx, Δy), then its speed can be estimated as (v x , v y )=(Δx / Δt, Δy / Δt). If only the modulus of the pixel velocity is considered, it can be estimated as v=sqrt(Δx^2+Δy^2) / Δt. After the above steps, the user device can obtain the pixel-level motion velocity of the moving object.

[0059] Step S105: If it is determined that the moving speed is greater than or equal to the preset speed threshold, then determining the moving direction and moving distance of the moving object from the first image to the second image.

[0060] In step S105, the user device first compares the speed of the moving object obtained in step S104 with a preset speed threshold. The preset speed threshold is predetermined by the visual perception characteristics of the human eye, and the present application does not limit this. The preset speed threshold represents the minimum movement speed that the backlight needs to respond to. For example, the preset speed threshold can be set to 10 pixels / frame. If the movement speed of the moving object is greater than or equal to the preset speed threshold, the user device further determines its moving direction. The moving direction can be determined based on the change in the position coordinates of the moving object in the first image and the second image. For example, the position coordinates of the moving object in the first image are (x1, y1), and the position coordinates in the second image are (x2, y2), then its displacement vector can be expressed as (Δx, Δy) = (x2-x1, y2-y1). By calculating the components of the displacement vector on the x-axis and y-axis, the movement trend of the moving object in the horizontal and vertical directions can be determined. If Δx>0, it means that the object moves to the right; if Δx<0, it means that the object moves to the left; if Δx=0, it means that the object remains stationary in the horizontal direction. Similarly, if Δy>0, it means that the object is moving downward; if Δy<0, it means that the object is moving upward; if Δy=0, it means that the object remains stationary in the vertical direction.

[0061] After determining the moving direction of the moving object, the user device also needs to calculate its moving distance. The moving distance can be estimated based on the displacement and pixel size of the moving object. For example, if the displacement vector of the moving object is (Δx, Δy) and the pixel size is s mm / pixel, then its moving distance on the x-axis and y-axis can be estimated as dx=s*|Δx| and dy=s*|Δy|, respectively, in millimeters. Furthermore, the total moving distance of the moving object d=sqrt(dx^2+dy^2), that is, the modulus of the displacement vector, can be calculated. The size of the moving distance can reflect the degree of position change of the moving object between two frames of images.

[0062] Step S106: Predict the intermediate moving direction and intermediate moving distance of the moving object based on the moving direction and the moving distance, where the intermediate moving direction is the moving direction of the moving object at a preset time point, and the intermediate moving distance is the moving distance of the moving object at a preset time point, where the preset time point is a time point between the first image and the second image.

[0063] In step S106, the position change of the moving object in multiple frame images is obtained according to the moving direction and the moving distance; a motion model of the moving object is constructed according to the position change; in the motion model, the moving position corresponding to the preset time point is determined; and the intermediate moving direction and intermediate moving distance of the moving object relative to the first image at the preset time point are determined according to the moving position.

[0064] Specifically, the user device obtains the position change of the moving object in multiple frames of continuous images. In addition to the first image and the second image, the user device also analyzes the position coordinates of the moving object in the first and last frames to form a time series (t0, p0), (t1, p1), ..., (tn, pn), where ti represents the timestamp of the i-th frame image, and pi represents the position coordinates of the moving object in the i-th frame image. By analyzing the change trend of the position coordinates on the time axis, the dynamic parameters such as the moving direction, speed and acceleration of the moving object can be roughly inferred, providing a basis for subsequent motion modeling. After obtaining the position change sequence of the moving object, the user device establishes a mathematical model to quantitatively describe its motion law, providing a theoretical basis for state prediction at any time. The motion models include uniform linear motion, uniformly accelerated linear motion, horizontal motion, circular motion, etc. The user device fits different motion models according to the position change of the moving object to obtain the motion model corresponding to the moving object.

[0065] According to the motion model of the moving object, the user device predicts its instantaneous motion state at any preset time point between the first image and the second image. The preset time point is usually selected as the moment at the midpoint of the two frames of images. For example, if the time interval between the two frames of images is Δt, the preset moment can be set to t=0.5Δt, that is, the midpoint of the two frames. Substituting the preset time point t into the mathematical motion model of the moving object, the predicted position coordinates (x, y) of the moving object at the preset time point can be calculated. Furthermore, the displacement vector (Δx, Δy) between the predicted position and the position of the first image can be calculated, and combined with the pixel size to convert it into a physical distance, the intermediate movement direction and intermediate movement distance of the moving object relative to the first image at the preset time point can be obtained.

[0066] Step S107: Generate an intermediate frame according to the intermediate moving direction and the intermediate moving distance.

[0067] In step S107, an image block of the moving object in the first image is extracted; a motion displacement vector at a preset time point is calculated according to an intermediate moving direction and an intermediate moving distance; motion compensation is performed on the image block according to the motion displacement vector to obtain a predicted image block at the preset time point; the predicted image block is placed at a corresponding position of a blank frame to obtain a predicted image of the moving object in an intermediate frame; and image content other than the moving object in the first image is copied to the blank frame to obtain an intermediate frame.

[0068] Specifically, the user device extracts the image block where the moving object is located from the first image. The size of the image block is determined according to the size and range of motion of the moving object. It must be large enough to cover the entire moving object and small enough to reduce the computational complexity. For example, the size of the image block can be set to 1.5 times the circumscribed rectangle of the moving object, which takes into account the shape change of the moving object and reduces the processing of redundant pixels. The extraction of image blocks is achieved through target detection and semantic segmentation algorithms to obtain a pixel-level mask of the moving object, and then the position and size of the image block are determined according to the boundary coordinates of the mask. It should be noted that the extraction of image blocks should be as compact as possible to avoid including too many background areas in order to improve the accuracy of motion compensation.

[0069] According to the intermediate moving direction and intermediate moving distance of the moving object at the preset time point predicted in step S106, the user device can calculate its motion displacement vector relative to the first image. The motion displacement vector describes the displacement of the image block in the horizontal and vertical directions and is used to guide the translation transformation of the image block. For example, assuming that the intermediate moving direction is θ (radian system) and the intermediate moving distance is d (pixel), the motion displacement vector can be expressed as (Δx, Δy) = (dcosθ, dsinθ).

[0070] Based on the motion displacement vector, the user device can perform motion compensation on the image block of the moving object to predict its position and shape at a preset time point. The basic idea of ​​motion compensation is to assume that the image block performs translational motion between adjacent frames, and to offset the position deviation caused by the motion by applying a displacement transformation opposite to the motion displacement to the image block. For example, if the motion displacement vector is (Δx, Δy), the following transformation can be performed on each pixel (x, y) of the image block: (x', y') = (x-Δx, y-Δy) to obtain the compensated pixel coordinates (x', y'). After performing the transformation on all pixels, the predicted image block at the preset time point is obtained, which reflects the expected position and shape of the moving object at that moment.

[0071] After obtaining the predicted image block of the moving object, the user device combines it with other parts of the first image to generate a complete intermediate frame image. Specifically, a blank frame of the same size as the first image can be created first, and then the predicted image block can be copied to the corresponding position of the blank frame, and then the other areas of the first image except the moving object can be copied to the blank frame as is, and finally the intermediate frame image is obtained. The intermediate frame image reflects the content of the picture at a preset time point, including the predicted position of the moving object and the original content of the background, and can be used as a reference for Mini-LED backlight compensation. It should be noted that since the edge of the predicted image block may be discontinuous with the background, the edge area needs to be smoothly transitioned to avoid artifacts. Commonly used transition methods include alpha blending, Gaussian filtering, etc., which can be selected according to edge characteristics and display effects.

[0072] For example, assume that the user device establishes a motion model based on the position change of the moving object, and predicts that it is located at (105, 205) at a preset time (t=0.02) between the first frame (t1=0) and the second frame (t2=0.04), and the intermediate movement distance relative to the first frame position (100, 200) is 0.71 mm, and the intermediate movement direction is 45° (i.e., π / 4). The user device extracts a 40×40 pixel image block centered at (100, 200) from the first image as a reference block of the moving object. Then, the user device calculates the corresponding displacement of 7.1 pixels based on the intermediate movement distance of 0.71 mm and the pixel size of 0.1 mm / pixel. Then, based on the intermediate movement direction of 45°, the motion displacement vector (Δx, Δy)=(5,-5) is calculated, that is, a translation of 5 pixels to the right and 5 pixels upward. The user device performs a motion compensation transformation (x', y') = (x-5, y+5) on each pixel (x, y) in the reference image block to obtain a predicted image block. For example, the pixel (20, 20) located at the center of the reference block has coordinates (15, 25) after transformation, corresponding to the new center of the predicted image block. Finally, the user device creates a blank frame of the same size as the first image, copies the predicted image block to the (105, 205) position of the blank frame, and then copies all pixels except the 40×40 block in the first frame to the blank frame as is, obtaining the final intermediate frame image. The intermediate frame image reflects the expected picture content at time t=0.02.

[0073] In the above steps, the user equipment generates an intermediate frame image reflecting the picture content at a preset time point through motion object image block extraction, motion displacement vector calculation, motion compensation prediction and intermediate frame synthesis.

[0074] In a possible implementation, motion compensation is performed on an image block according to a motion displacement vector to obtain a predicted image block at a preset time point, specifically including: determining a reference position of the predicted image block in the first image according to the motion displacement vector; and generating a pixel value of the reference position through an interpolation algorithm to obtain the predicted image block.

[0075] Specifically, the user equipment determines the reference position of the predicted image block in the first image according to the motion displacement vector. The reference position refers to the corresponding position of each pixel in the predicted image block in the first image, reflecting the source of the motion of the pixel. Since the predicted image block is obtained by applying a motion displacement transformation to the image block in the first image, there is a one-to-one geometric mapping relationship between the reference position and the predicted image block. Specifically, assuming that the pixel coordinates in the predicted image block are (x', y') and the motion displacement vector is (Δx, Δy), the reference coordinates (x, y) of the pixel in the first image can be obtained by the following transformation: (x, y) = (x' + Δx, y' + Δy). It should be noted that since the motion displacement vector may contain a sub-pixel fractional part, the reference coordinates (x, y) may also fall between the pixel grids, and an interpolation algorithm is required to estimate its pixel value. After determining the reference position of each pixel of the predicted image block, the user equipment generates the pixel value of the reference position through an interpolation algorithm to obtain a complete predicted image block. The basic idea of ​​the interpolation algorithm is to use the known pixel values ​​around the reference position to estimate the unknown pixel value at that position by weighted averaging, etc., so as to achieve smooth transition and detail reconstruction of the image. The interpolation algorithms that the user device can use include nearest neighbor interpolation, bilinear interpolation, and bicubic interpolation.

[0076] Step S108: inserting intermediate frames into the first image and the second image, and combining the image frames to obtain a target video.

[0077] In step S108, the user device needs to insert the intermediate frame generated in step S107 between the first image and the second image, and combine the inserted image sequence. The user device first determines the time point of the intermediate frame in the target video, that is, the preset time point. The user device inserts the intermediate frame between the first image and the second image in the order of timestamps to form a sequence of three frames of images. After obtaining the inserted three-frame image sequence, the user device combines it with other frames of the video to be displayed to generate the final target video. Specifically, the user device first adds the three-frame image sequence to the frame buffer of the target video in the order of the first image, the intermediate frame, and the second image, and then adds the remaining image frames in the original video to the frame buffer in the order of timestamps until all frames are processed. During the adding process, the user device renumbers the timestamps of each frame to reflect the new frame rate and duration after interpolation. At the same time, it is also necessary to set the encoding parameters of the target video, such as frame size, bit rate, and key frame interval, to balance the video quality and data volume.

[0078] Step S109: Display the target video on the LCD screen.

[0079] In step S109, the user device transmits the target video from the backend system to the front-end device where the LCD screen is located. After receiving the target video data, the LCD screen needs to use its built-in driving circuit and backlight system to display the video frame by frame on the screen and modulate the brightness distribution of the Mini-LED backlight in real time to match the dynamic changes of the picture content.

[0080] Reference Figure 2The present application also provides a display device of a Mini-LED liquid crystal display screen, which is a user device, and the user device includes an acquisition module 201 and a processing module 202, wherein: the acquisition module 201 is used to acquire a video to be displayed; the processing module 202 is used to divide the video to be displayed into multiple frames of images; the processing module 202 is also used to perform frame difference detection on a first image and a second image, and detect whether there is a moving object in the first image and the second image, the first image and the second image are any two adjacent frames of images in the multiple frames, and the first image is the previous frame of the second image; the processing module 202 is also used to obtain the movement speed of the moving object if it is determined that there is a moving object in the first image and the second image; the processing module 202 is also used to obtain the movement speed of the moving object if the movement speed is determined If the speed is greater than or equal to a preset speed threshold, the moving direction and moving distance of the moving object from the first image to the second image are determined; the processing module 202 is also used to predict the intermediate moving direction and intermediate moving distance of the moving object according to the moving direction and moving distance, the intermediate moving direction is the moving direction of the moving object at a preset time point, the intermediate moving distance is the moving distance of the moving object at a preset time point, and the preset time point is a certain time point between the first image and the second image; the processing module 202 is also used to generate an intermediate frame according to the intermediate moving direction and the intermediate moving distance; the processing module 202 is also used to insert the intermediate frame into the first image and the second image, and combine each image frame to obtain the target video; the processing module 202 is also used to display the target video on the LCD screen.

[0081] In a possible implementation, the acquisition module 201 acquires the movement speed of the moving object, specifically including: the processing module 202 tracks the moving object to obtain the position correspondence between the moving object in the first image and the second image; the processing module 202 calculates the displacement of the moving object between the first image and the second image according to the position correspondence; the acquisition module 201 acquires the time interval between the first image and the second image; the processing module 202 performs calculation according to the displacement and the time interval to obtain the movement speed.

[0082] In a possible implementation, the processing module 202 generates an intermediate frame according to the intermediate moving direction and the intermediate moving distance, specifically including: the processing module 202 extracts the image block of the moving object in the first image; the processing module 202 calculates the motion displacement vector at a preset time point according to the intermediate moving direction and the intermediate moving distance; the processing module 202 performs motion compensation on the image block according to the motion displacement vector to obtain a predicted image block at the preset time point; the processing module 202 places the predicted image block at a corresponding position of the blank frame to obtain a predicted image of the moving object in the intermediate frame; the processing module 202 copies the image content in the first image except the moving object to the blank frame to obtain the intermediate frame.

[0083] In a possible implementation, the processing module 202 performs motion compensation on the image block according to the motion displacement vector to obtain a predicted image block at a preset time point, specifically including: the processing module 202 determines a reference position of the predicted image block in the first image according to the motion displacement vector; the processing module 202 generates a pixel value of the reference position through an interpolation algorithm to obtain the predicted image block.

[0084] In a possible implementation, the processing module 202 performs frame difference detection on the first image and the second image to detect whether there is a moving object in the first image and the second image, specifically including: the processing module 202 performs grayscale processing and frame difference processing on the first image and the second image to obtain a frame difference image; the processing module 202 performs binarization processing on the frame difference image to obtain a binarized image; the processing module 202 determines whether there is a moving object in the first image and the second image based on the binarized image.

[0085] In a possible implementation, the processing module 202 determines whether there is a moving object in the first image and the second image based on the binary image, specifically including: the processing module 202 performs a connected domain analysis on the binary image to extract a candidate moving area; the processing module 202 calculates feature quantities of the candidate moving area, the feature quantities include area, aspect ratio and center position; the processing module 202 filters the candidate moving area based on the feature quantities to obtain a filtered area; the processing module 202 determines whether the filtered area has continuity in multiple frames of images, and determines whether there is a moving object in the first image and the second image based on the continuity.

[0086] In a possible implementation, the processing module 202 determines whether the screening area has continuity in multiple frames of images, and determines whether there is a moving object in the first image and the second image based on the continuity, specifically including: the processing module 202 calculates the change amount of the feature quantity in the multiple frames of images; determines whether the change amount is less than a preset change threshold; if the processing module 202 determines that the change amount is less than the preset change threshold, it determines that the screening area has continuity in the multiple frames of images, and then determines that there is a moving object in the first image and the second image; if the processing module 202 determines that the change amount is greater than or equal to the preset change threshold, it determines that the screening area does not have continuity in the multiple frames of images, and then determines that there is no moving object in the first image and the second image.

[0087] In a possible implementation, the processing module 202 predicts the intermediate moving direction and the intermediate moving distance of the moving object based on the moving direction and the moving distance, specifically including: the acquisition module 201 acquires the position change of the moving object in multiple frame images based on the moving direction and the moving distance; the processing module 202 constructs a motion model of the moving object based on the position change; the processing module 202 determines the moving position corresponding to a preset time point in the motion model; the processing module 202 determines the intermediate moving direction and the intermediate moving distance of the moving object relative to the first image at the preset time point based on the moving position.

[0088] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0089] The present application also provides an electronic device. Figure 3 , Figure 3 The electronic device 300 may include: at least one processor 301 , at least one network interface 304 , a user interface 303 , a memory 305 , and at least one communication bus 302 .

[0090] The communication bus 302 is used to realize the connection and communication between these components.

[0091] The user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0092] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0093] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0094] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may optionally also be at least one storage device located away from the aforementioned processor 301. Refer to Figure 3 , the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module and an application program of a display method of a Mini-LED liquid crystal display screen.

[0095] exist Figure 3In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call the application program storing a display method of a Mini-LED liquid crystal display screen in the memory 305. When executed by one or more processors 301, the electronic device 300 executes one or more of the methods described in the above embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited to the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0096] The present application also provides a computer-readable storage medium, which stores instructions. When executed by one or more processors 301, the electronic device 300 executes one or more of the methods described in the above embodiments.

[0097] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0098] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0099] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0100] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0101] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0102] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other embodiments of the present disclosure.

[0103] This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art not described in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A display method of a Mini-LED liquid crystal display screen, characterized in that: The method comprises: Get the video to be displayed; Dividing the video to be displayed into multiple frames of images; Performing frame difference detection on a first image and a second image to detect whether there is a moving object in the first image and the second image, wherein the first image and the second image are any two adjacent frames of the plurality of frames, and the first image is a previous frame of the second image; If it is determined that the moving object exists in the first image and the second image, obtaining a moving speed of the moving object; If it is determined that the movement speed is greater than or equal to a preset speed threshold, determining a movement direction and a movement distance of the moving object from the first image to the second image; Predicting an intermediate moving direction and an intermediate moving distance of the moving object according to the moving direction and the moving distance, wherein the intermediate moving direction is the moving direction of the moving object at a preset time point, and the intermediate moving distance is the moving distance of the moving object at a preset time point, and the preset time point is a time point between the first image and the second image; generating an intermediate frame according to the intermediate moving direction and the intermediate moving distance; Inserting the intermediate frame into the first image and the second image, and combining the image frames to obtain a target video; Displaying the target video on a liquid crystal display screen; Generating an intermediate frame according to the intermediate moving direction and the intermediate moving distance specifically includes: Extracting an image block of the moving object in the first image; Calculating the motion displacement vector at the preset time point according to the intermediate moving direction and the intermediate moving distance; Performing motion compensation on the image block according to the motion displacement vector to obtain a predicted image block at the preset time point; Placing the predicted image block at a corresponding position of the blank frame to obtain a predicted image of the moving object in the intermediate frame; The image content of the first image except the moving object is copied to the blank frame to obtain the intermediate frame.

2. The method according to claim 1, characterized in that The obtaining of the moving speed of the moving object specifically includes: Tracking the moving object to obtain a position correspondence between the moving object in the first image and the second image; Calculating the displacement of the moving object between the first image and the second image according to the position correspondence; acquiring a time interval between the first image and the second image; The movement speed is obtained by calculating according to the displacement and the time interval.

3. The method according to claim 1, characterized in that The performing motion compensation on the image block according to the motion displacement vector to obtain the predicted image block at the preset time point specifically includes: Determining a reference position of the predicted image block in the first image according to the motion displacement vector; The pixel value of the reference position is generated by an interpolation algorithm to obtain the predicted image block.

4. The method according to claim 1, characterized in that: The performing frame difference detection on the first image and the second image to detect whether there is a moving object in the first image and the second image specifically includes: Performing grayscale processing and frame difference processing on the first image and the second image to obtain a frame difference image; Binarizing the frame difference image to obtain a binary image; According to the binarized image, it is determined whether there is a moving object in the first image and the second image.

5. The method according to claim 4, characterized in that The determining, according to the binarized image, whether there is a moving object in the first image and the second image specifically includes: Performing connected domain analysis on the binary image to extract candidate motion regions; Calculating feature quantities of the candidate motion region, wherein the feature quantities include area, aspect ratio, and center position; According to the feature quantity, the candidate motion region is screened to obtain a screened region; It is determined whether the screening area has continuity in the multiple frames of the image, and it is determined whether there is a moving object in the first image and the second image according to the continuity.

6. The method according to claim 5, characterized in that The determining whether the screening area has continuity in the multiple frames of the image, and determining whether there is a moving object in the first image and the second image according to the continuity, specifically includes: Calculating the variation of the feature quantity in multiple frames of the image; Determining whether the change amount is less than a preset change threshold; If it is determined that the change amount is less than the preset change threshold, determining that the screening area has continuity in the multiple frames of the image, and further determining that there is a moving object in the first image and the second image; If it is determined that the change amount is greater than or equal to the preset change threshold, it is determined that the screening area has no continuity in the multiple frames of the image, and further it is determined that the moving object does not exist in the first image and the second image.

7. The method according to claim 1, characterized in that The predicting the intermediate moving direction and the intermediate moving distance of the moving object according to the moving direction and the moving distance specifically includes: Acquire the position change of the moving object in the plurality of frames of the image according to the moving direction and the moving distance; constructing a motion model of the moving object according to the position change; In the motion model, determining a moving position corresponding to a preset time point; The intermediate moving direction and the intermediate moving distance of the moving object relative to the first image at the preset time point are determined according to the moving position.

8. A display device of a Mini-LED liquid crystal display screen, characterized in that: The device comprises an acquisition module (201) and a processing module (202), wherein: The acquisition module (201) is used to acquire the video to be displayed; The processing module (202) is used to divide the video to be displayed into multiple frames of images; The processing module (202) is further used to perform frame difference detection on the first image and the second image to detect whether there is a moving object in the first image and the second image, the first image and the second image being any two adjacent frames of the plurality of frames, and the first image being a previous frame of the second image; The processing module (202) is further configured to obtain a moving speed of the moving object if it is determined that the moving object exists in the first image and the second image; The processing module (202) is further configured to determine the moving direction and moving distance of the moving object from the first image to the second image if it is determined that the moving speed is greater than or equal to a preset speed threshold; The processing module (202) is further used to predict the intermediate moving direction and intermediate moving distance of the moving object according to the moving direction and the moving distance, wherein the intermediate moving direction is the moving direction of the moving object at a preset time point, and the intermediate moving distance is the moving distance of the moving object at a preset time point, and the preset time point is a time point between the first image and the second image; The processing module (202) is further used to generate an intermediate frame according to the intermediate moving direction and the intermediate moving distance; The processing module (202) is further configured to insert the intermediate frame into the first image and the second image, and combine the image frames to obtain a target video; The processing module (202) is also used to display the target video on a liquid crystal display screen; The processing module (202) is further used to extract an image block of the moving object in the first image; calculate the motion displacement vector of the preset time point according to the intermediate moving direction and the intermediate moving distance; perform motion compensation on the image block according to the motion displacement vector to obtain a predicted image block at the preset time point; place the predicted image block at a corresponding position of a blank frame to obtain a predicted image of the moving object in the intermediate frame; and copy the image content of the first image except the moving object to the blank frame to obtain the intermediate frame.

9. An electronic device, characterized in that: The electronic device (300) comprises a processor (301), a memory (305), a user interface (303) and a network interface (304), wherein the memory (305) is used to store instructions, the user interface (303) and the network interface (304) are used to communicate with other devices, and the processor (301) is used to execute the instructions stored in the memory (305) so that the electronic device (300) executes the method according to any one of claims 1 to 7.

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