A block optimization method, device and medium based on ultra-high-definition video transmission

Abstract

The present application discloses a block optimization method, device and medium based on ultra-high-definition video transmission, which relates to the field of ultra-high-definition video transmission technology. The method includes: obtaining ultra-high-definition video frames, and performing video scene recognition on the ultra-high-definition video frames to obtain a dynamic block recommendation map; based on the dynamic block recommendation map, determining the video block result through block decision analysis; performing layered quantization coding processing on the video block result to obtain dynamic allocation data of differentiated quantization parameters; according to the dynamic allocation data of differentiated quantization parameters, determining anti-fluctuation bandwidth data through bandwidth cycle prediction; based on the anti-fluctuation bandwidth data, obtaining bit rate allocation data through dynamic bit rate control. The present application solves the technical problems of large bit rate fluctuation and obvious image quality loss in ultra-high-definition video block coding in the prior art through the above method, improves the stability of transmission under fluctuating networks, and breaks through the performance bottleneck of existing standards in 8K real-time transmission.

Description

Technical Field

[0001] The present application relates to the field of ultra-high-definition video transmission technology, and in particular to a block optimization method, device and medium based on ultra-high-definition video transmission. Background Art

[0002] With the advancement of video acquisition technology, ultra-high-definition video (such as 8K / 120fps) has become increasingly popular, resulting in a huge amount of video data. Traditional coding standards (such as HEVC) face problems of high bandwidth occupancy and encoding and decoding delay in real-time transmission.

[0003] Existing mainstream block encoding schemes use fixed block sizes, making them difficult to adapt to complex dynamic scenes (such as fast-moving motion), resulting in large bitrate fluctuations and significant image quality loss. Furthermore, they fail to address the block strategy issues in dynamic scenes. When network bandwidth fluctuates, the adaptive algorithm responds slowly, easily leading to lag or resolution degradation. Existing technologies are unable to adjust bitrates based on historical bandwidth predictions and simultaneously optimize encoding based on video content characteristics. Summary of the Invention

[0004] The embodiments of the present application provide a block optimization method, device and medium based on ultra-high-definition video transmission, which solves the technical problems of large fluctuations in the bit rate and obvious loss of image quality in ultra-high-definition video block encoding in the prior art.

[0005] In the first aspect, an embodiment of the present application provides a block optimization method based on ultra-high-definition video transmission, characterized in that the method includes: obtaining ultra-high-definition video frames, and performing video scene recognition on the ultra-high-definition video frames to obtain a dynamic block recommendation map; based on the dynamic block recommendation map, determining the video block result through block decision analysis; performing layered quantization coding processing on the video block result to obtain dynamic allocation data of differentiated quantization parameters; according to the dynamic allocation data of differentiated quantization parameters, determining anti-fluctuation bandwidth data through bandwidth cycle prediction; based on the anti-fluctuation bandwidth data, obtaining bit rate allocation data through dynamic bit rate control.

[0006] In one implementation of the present application, video scene recognition is performed on ultra-high-definition video frames to obtain a dynamic block recommendation map, specifically including: performing motion vector analysis on the ultra-high-definition video frames to obtain a first dynamic feature; performing texture complexity analysis on the ultra-high-definition video frames to obtain a second dynamic feature; determining the dynamic feature vector of the ultra-high-definition video frame based on the first dynamic feature and the second dynamic feature; and obtaining a dynamic block recommendation map through dynamic block analysis based on the dynamic feature vector; wherein the dynamic block analysis includes: region recommendation block size marking, block size optimization, and texture complexity scoring.

[0007] In one implementation of the present application, based on a dynamic block suggestion map, a video block result is determined through block decision analysis, specifically including: configuring block parameters of the dynamic block suggestion map to obtain a block video frame to be optimized; wherein, the block parameter configuration includes: motion-sensitive areas, complex texture areas, and continuous static areas; performing block boundary optimization on the block video frame to be optimized to determine the video block result; wherein, the block boundary optimization includes: morphological closing operation and block size gradient change constraint.

[0008] In one implementation of the present application, after optimizing the block boundaries of the block video frame to be optimized to determine the video block result, the method also includes: determining the video adjustment mode based on the video block result through bandwidth change threshold analysis; wherein the video adjustment mode includes: normal mode and emergency mode; when the video adjustment mode is normal mode, the original video block state is maintained; when the video adjustment mode is emergency mode, a dynamic optimized image is obtained by dynamically adjusting the regional quality; and a preset I frame is inserted into the dynamic optimized image to determine the emergency mode optimization data.

[0009] In one implementation of the present application, the video segmentation results are subjected to hierarchical quantization encoding processing to obtain dynamic allocation data of differential quantization parameters, specifically including: based on the video segmentation results, determining the motion level corresponding to the segmentation type through motion level matching; matching the motion level with the corresponding quantization parameter to obtain hierarchical quantization data; and determining the dynamic allocation data of the differential quantization parameter through bit rate weight matching based on the hierarchical quantization data.

[0010] In one implementation of the present application, based on the dynamic allocation data of the differentiated quantization parameter, the anti-fluctuation bandwidth data is determined by bandwidth cycle prediction, specifically including: based on the dynamic allocation data, obtaining the current available bandwidth by periodic bandwidth detection; wherein the calculation formula for periodic bandwidth detection is:

[0011]

[0012] in, is the packet payload, It is The round trip time of a data packet, is the current available bandwidth value; the current available bandwidth is subjected to anti-interference smoothing processing to obtain anti-fluctuation bandwidth data; wherein, the calculation formula for anti-interference smoothing processing is:

[0013]

[0014] in, is the bandwidth value after bandwidth smoothing, The bandwidth value calculated last time.

[0015] In one implementation of the present application, based on the anti-fluctuation bandwidth data, the bit rate allocation data is obtained through dynamic bit rate control, specifically including: performing a bit rate dynamic calculation on the anti-fluctuation bandwidth data to obtain the bit rate to be allocated; wherein the calculation formula of the bit rate dynamic calculation is:

[0016]

[0017] in, is the maximum bit rate supported by the encoder, For block The code rate weight, For block Current value; allocate the code rate to the corresponding code to obtain code rate allocation data.

[0018] In one implementation of the present application, after obtaining the bit rate allocation data through dynamic bit rate control based on the anti-fluctuation bandwidth data, the method also includes: obtaining ultra-high-definition optimized video based on the bit rate allocation data; transmitting the ultra-high-definition optimized video to a video destination through a preset transmission network to obtain output ultra-high-definition video.

[0019] In the second aspect, an embodiment of the present application also provides a block optimization device based on ultra-high-definition video transmission, characterized in that the device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can: obtain ultra-high-definition video frames, and perform video scene recognition on the ultra-high-definition video frames to obtain a dynamic block recommendation map; based on the dynamic block recommendation map, determine the video block result through block decision analysis; perform layered quantization encoding processing on the video block result to obtain dynamic allocation data of differentiated quantization parameters; based on the dynamic allocation data of differentiated quantization parameters, determine anti-fluctuation bandwidth data through bandwidth period prediction; based on the anti-fluctuation bandwidth data, obtain bit rate allocation data through dynamic bit rate control.

[0020] In a third aspect, an embodiment of the present application further provides a non-volatile computer storage medium for block optimization based on ultra-high-definition video transmission, storing computer-executable instructions, characterized in that the computer-executable instructions are configured to: obtain ultra-high-definition video frames, and perform video scene recognition on the ultra-high-definition video frames to obtain a dynamic block recommendation map; based on the dynamic block recommendation map, determine the video block result through block decision analysis; perform layered quantization coding processing on the video block result to obtain dynamic allocation data of differentiated quantization parameters; determine anti-fluctuation bandwidth data through bandwidth cycle prediction based on the dynamic allocation data of differentiated quantization parameters; and obtain bit rate allocation data through dynamic bit rate control based on the anti-fluctuation bandwidth data.

[0021] The embodiments of the present application provide a block optimization method, device and medium based on ultra-high-definition video transmission. Through block decision-making and quantization, bit rate adjustment based on bandwidth prediction and real-time bit rate allocation, the technical problems of large bit rate fluctuation and obvious image quality loss in ultra-high-definition video block encoding in the prior art are solved, dynamic encoding of ultra-high-definition video is realized, the stability of transmission under fluctuating networks is improved, the transmission delay is reduced, and the performance bottleneck of the existing standard in 8K real-time transmission is broken through. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0023] Figure 1 A flowchart of a block optimization method based on ultra-high-definition video transmission provided in an embodiment of the present application;

[0024] Figure 2 A schematic diagram of the internal structure of a block optimization device based on ultra-high-definition video transmission provided in an embodiment of the present application. DETAILED DESCRIPTION

[0025] To make the purpose, technical solutions, and advantages of this application more clear, the technical solutions of this application will be clearly and completely described below in conjunction with the specific embodiments of this application and the corresponding drawings. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0026] The embodiments of the present application provide a block optimization method, device and medium based on ultra-high-definition video transmission. Through block decision-making and quantization, bit rate adjustment based on bandwidth prediction and real-time bit rate allocation, the technical problems of large bit rate fluctuation and obvious image quality loss in ultra-high-definition video block encoding in the prior art are solved, dynamic encoding of ultra-high-definition video is realized, the stability of transmission under fluctuating networks is improved, the transmission delay is reduced, and the performance bottleneck of the existing standard in 8K real-time transmission is broken through.

[0027] The technical solutions proposed in the embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0028] Figure 1 This is a flow chart of a block optimization method based on ultra-high-definition video transmission provided in an embodiment of the present application. Figure 1 As shown, the embodiment of the present application provides a block optimization method based on ultra-high-definition video transmission, which specifically includes the following steps:

[0029] Step 101: Obtain an ultra-high-definition video frame and perform video scene recognition on the ultra-high-definition video frame to obtain a dynamic block suggestion map.

[0030] For example, a pre-trained lightweight convolutional neural network (CNN) model is used to analyze the motion vector and texture complexity of the input video frame, and the dynamic feature vector of each frame is output, which is recorded as The lightweight convolutional neural network model contains three convolutional layers and one long short-term memory network (LSTM) layer. The model parameter count is ≤1M, and the single-frame processing time is ≤5ms, which enables dynamic feature analysis of ultra-high-definition video frames.

[0031] Specifically, video scene recognition is performed on ultra-high-definition video frames to obtain a dynamic block recommendation map, including: performing motion vector analysis on the ultra-high-definition video frames to obtain a first dynamic feature; performing texture complexity analysis on the ultra-high-definition video frames to obtain a second dynamic feature; determining the dynamic feature vector of the ultra-high-definition video frame based on the first dynamic feature and the second dynamic feature; and obtaining a dynamic block recommendation map through dynamic block analysis based on the dynamic feature vector; wherein the dynamic block analysis includes: region recommendation block size marking, block size optimization, and texture complexity scoring.

[0032] In one embodiment, a lightweight CNN model is first constructed, including the following four layers:

[0033] Convolutional layer 1: 64 channels, 3×3 convolution kernel, stride 2, output resolution reduced to 1 / 4;

[0034] Convolutional layer 2: 128 channels, 3×3 convolution kernel, stride 1, output feature map size unchanged;

[0035] Convolutional layer 3: 256 channels, 1×1 convolution kernel, generating spatial feature vectors;

[0036] LSTM layer: processes the features of 5 consecutive frames and predicts the changing trend of dynamic areas.

[0037] The model training database is a preset 8K video library (covering 10 categories of scenes, such as sports and medical imaging). Motion vector fields (MVFs) are constructed from raw video frames (YUV420 format, resolution ≥ 7680×4320).

[0038] The above model is used to process and output a dynamic block proposal map (marking the recommended block size of each area: 8×8 / 16×16 / 32×32 / 64×64 / 128×128) and a texture complexity score (0.0~1.0, calculated based on local variance).

[0039] Furthermore, the CNN model's training data includes at least 100,000 frames of annotated 8K video samples, including motion region masks and texture level labels. During inference, the model uses low-precision quantization technology to convert floating-point weights to 8-bit integers, reducing memory usage by approximately 60%.

[0040] Step 102: Based on the dynamic segmentation suggestion graph, determine the video segmentation result through segmentation decision analysis.

[0041] For example, since blocks in different states in ultra-high-definition video have different levels of attention from the user's perspective, in actual applications it is necessary to adjust the block parameters according to the characteristics of the blocks. The block features divide the video frame into multiple non-uniform coding blocks based on dynamic feature vectors, etc., and the block size is dynamically adjusted between 8×8 and 128×128.

[0042] Furthermore, the blocking rule is: the area with motion vector amplitude greater than threshold Th1 (for example, Th1=10 pixels / frame) adopts the minimum blocking size, and the area with texture complexity greater than threshold Th2 (for example, Th2=0.6) adopts the medium blocking size.

[0043] Specifically, based on the dynamic block suggestion map, the video block result is determined through block decision analysis, including: configuring the block parameters of the dynamic block suggestion map to obtain the block video frame to be optimized; wherein, the block parameter configuration includes: motion-sensitive areas, complex texture areas, and continuous static areas; performing block boundary optimization on the block video frame to be optimized to determine the video block result; wherein, the block boundary optimization includes: morphological closing operation and block size gradient change constraint.

[0044] In one embodiment, different blocking strategies are used for different types of regions. Motion-sensitive regions (MVF magnitude > 10 pixels / frame) are forced to use the smallest blocking (8×8 or 16×16); regions with complex textures (local variance > 0.7) use medium blocking (32×32 or 64×64); and continuous static regions (MVF < 2 pixels / frame and variance < 0.3) are merged into large 128×128 blocks.

[0045] While dividing the blocks, boundary optimization is performed and morphological closing operation is used to smooth the block edges to avoid jagged boundaries.

[0046] It should be noted that the method adopted is to limit the gradient of block size change, and the difference in adjacent block sizes does not exceed 2 levels (for example: direct transition from 32×32 to 128×128 is not allowed).

[0047] Furthermore, in the dynamic block decision, the Markov decision process (MDP) is used to optimize the block size, and the reward function is explained by the following formula:

[0048] (1)

[0049] in, =0.6, =0.4, both are weight coefficients, For image quality gain, The current available bandwidth value.

[0050] Furthermore, after optimizing the block boundaries of the block video frames to be optimized to determine the video block results, the method also includes: determining the video adjustment mode based on the video block results through bandwidth change threshold analysis; wherein, the video adjustment mode includes: normal mode and emergency mode; when the video adjustment mode is normal mode, the original video block state is maintained; when the video adjustment mode is emergency mode, a dynamic optimized image is obtained by dynamically adjusting the regional quality; and a preset I frame is inserted into the dynamic optimized image to determine the emergency mode optimization data.

[0051] In one embodiment, when the video adjustment mode is the normal mode, the original video segmentation state is maintained, and when the bandwidth drops by more than 20%, the emergency mode is activated.

[0052] Increase the QP value of the static area by 10-15 while keeping the QP value of the moving area unchanged; and trigger rapid I-frame insertion, shortening the GOP length to 60 frames.

[0053] Step 103: Perform hierarchical quantization coding processing on the video segmentation result to obtain dynamic allocation data of differentiated quantization parameters.

[0054] For example, different quantization parameters (QP values) are assigned to different block types, with the QP value range for moving areas being 20-30 and the QP value range for static areas being 35-45. The image is then encoded using the H.266 / AVS3 standard, and the rate-distortion optimization (RDO) algorithm is used to control the bitrate distribution.

[0055] Specifically, the video segmentation results are subjected to hierarchical quantization encoding processing to obtain dynamic allocation data of differential quantization parameters, including: based on the video segmentation results, determining the motion level corresponding to the segmentation type through motion level matching; matching the motion level with the corresponding quantization parameter to obtain hierarchical quantization data; and determining the dynamic allocation data of differential quantization parameters through bit rate weight matching based on the hierarchical quantization data.

[0056] In one embodiment, motion level matching is performed to determine the motion level corresponding to the block type; the motion level is matched to the corresponding quantization parameter to obtain hierarchical quantization data; based on the hierarchical quantization data, dynamic allocation data of the differential quantization parameter is determined through bit rate weight matching to achieve dynamic adjustment of the QP value. The matching rules are explained in detail in the following table:

[0057]

[0058] Table 1

[0059] Table 1 shows the QP value range corresponding to different blocks and motion levels in bit rate weight matching, so as to achieve dynamic adjustment of QP value.

[0060] It's important to note that the QP value, short for Quantizer Parameter, is a parameter used in video encoding to reflect the compression of spatial details. A smaller QP value results in finer quantization and higher image quality, but also a higher bitrate and increased transmission bandwidth consumption. Conversely, a larger QP value results in a loss of detail during the encoding process, resulting in image distortion, reduced quality, lower bitrates, and less transmission bandwidth consumption.

[0061] In this application, the QP value range adopted by the motion area is 20-30, and the QP value range adopted by the static area is 35-45. Then, the H.266 / AVS3 standard is adopted for encoding, and the rate-distortion optimization (RDO) algorithm is used to control the bit rate distribution.

[0062] Step 104 : Determine anti-fluctuation bandwidth data through bandwidth cycle prediction based on the dynamic allocation data of the differentiated quantization parameters.

[0063] For example, the network round-trip delay (RTT) and packet loss rate are collected every 50ms to predict the available bandwidth. In order to reduce the impact of rapid fluctuations in bandwidth in a short period of time and avoid the discomfort caused by too frequent switching of encoding parameters, an exponential weighting method is used to smooth the bandwidth. This makes ultra-high-definition video resistant to rapid fluctuations in bandwidth in a short period of time and improves the stability of video transmission.

[0064] Specifically, based on the dynamic allocation data of the differentiated quantization parameters, the anti-fluctuation bandwidth data is determined by bandwidth cycle prediction, including: based on the dynamic allocation data, obtaining the current available bandwidth by periodic bandwidth detection; wherein the calculation formula for periodic bandwidth detection is:

[0065]

[0066] in, is the packet payload, It is The round trip time of a data packet, is the current available bandwidth value; the current available bandwidth is subjected to anti-interference smoothing processing to obtain anti-fluctuation bandwidth data; wherein, the calculation formula for anti-interference smoothing processing is:

[0067]

[0068] in, is the bandwidth value after bandwidth smoothing, The bandwidth value calculated last time.

[0069] In one embodiment, a group of probe packets is sent every fixed time interval (e.g., 50 ms). The periodic bandwidth detection is explained by the following formula:

[0070] (2)

[0071] in, is the packet payload, It is The round-trip time (RTT) of a data packet, is the current available bandwidth value; the current available bandwidth is smoothed with anti-interference processing to obtain anti-fluctuation bandwidth data, and max() is the maximum value function.

[0072] In order to reduce the impact of rapid bandwidth fluctuations in a short period of time and avoid the discomfort caused by frequent switching of encoding parameters, an exponential weighting method is used to smooth the bandwidth. The specific formula is as follows:

[0073] The anti-interference smoothing process is explained by the following formula:

[0074] (3)

[0075] in, is the bandwidth value after bandwidth smoothing, is the bandwidth value calculated last time, The current available bandwidth value.

[0076] For weak network environments, packet loss compensation is also required. If the packet loss rate is greater than 5%, redundant coding is activated.

[0077] Step 105: Based on the anti-fluctuation bandwidth data, obtain bit rate allocation data through dynamic bit rate control.

[0078] For example, after determining the bandwidth value after bandwidth smoothing processing, in order to achieve the actual bit rate requirement of ultra-high-definition video under the bandwidth, it is necessary to dynamically control the bit rate.

[0079] Specifically, based on the anti-fluctuation bandwidth data, the bit rate allocation data is obtained through dynamic bit rate control, including: performing a bit rate dynamic calculation on the anti-fluctuation bandwidth data to obtain a bit rate to be allocated; wherein the calculation formula of the bit rate dynamic calculation is:

[0080]

[0081] in, is the maximum bit rate supported by the encoder, For block The code rate weight, For block Current value, is the code rate to be allocated, is the bandwidth value after bandwidth smoothing processing; the bit rate to be allocated is allocated to the corresponding code to obtain bit rate allocation data.

[0082] Furthermore, after obtaining the bit rate allocation data through dynamic bit rate control based on the anti-fluctuation bandwidth data, the method also includes: obtaining ultra-high-definition optimized video based on the bit rate allocation data; transmitting the ultra-high-definition optimized video to the video destination through a preset transmission network to obtain output ultra-high-definition video.

[0083] In one embodiment, dynamic rate control is explained by the following formula:

[0084] (4)

[0085] in, is the maximum bit rate supported by the encoder, For block The code rate weight, For block Current value, The bit rate to be allocated.

[0086] Furthermore, the encoding bitrate target value is dynamically adjusted based on the current available bandwidth value and video content characteristics. The target bitrate adjustment is explained by the following formula:

[0087] (5)

[0088] in, is the weight coefficient, which is generally set to 0.7;

[0089] is the bit rate to be allocated, that is, the target bit rate, is the current available bandwidth value, is the historical average bit rate.

[0090] At the same time, high priority tags are assigned to motion area coding blocks, which are transmitted first when the network is congested.

[0091] In one embodiment, through the above technical solution, an 8K original video (7680×4320@60fps, YUV420 format) is input, and the scene recognition module detects the presence of a fast-moving football and a static audience in the picture.

[0092] The coding blocks are dynamically divided into 16×16 blocks (QP=28) for the football area and 64×64 blocks (QP=35) for the audience area. When the network bandwidth is detected to drop from 80Mbps to 30Mbps, bitrate reallocation is initiated to prioritize the bitrate of the moving area and increase the QP value of the static area to 40.

[0093] The video output bit rate is stable at 25-28Mbps, the picture is complete and smooth, and the end-to-end delay is less than 150ms.

[0094] The above is an embodiment of the method proposed in this application. Based on the same inventive concept, this application embodiment also provides a block optimization device based on ultra-high-definition video transmission, the structure of which is as follows: Figure 2 shown.

[0095] Figure 2 This is a schematic diagram of the internal structure of a block optimization device based on ultra-high-definition video transmission provided in an embodiment of the present application. Figure 2 As shown, the equipment includes:

[0096] at least one processor 201;

[0097] and, a memory 202 communicatively coupled to the at least one processor;

[0098] The memory 202 stores instructions that can be executed by at least one processor, and the instructions are executed by the at least one processor 201 to enable the at least one processor 201 to:

[0099] Ultra-high-definition video frames are acquired and video scene recognition is performed on the ultra-high-definition video frames to obtain a dynamic block suggestion map; based on the dynamic block suggestion map, the video block result is determined through block decision analysis; the video block result is subjected to layered quantization coding processing to obtain dynamic allocation data of differentiated quantization parameters; based on the dynamic allocation data of differentiated quantization parameters, anti-fluctuation bandwidth data is determined through bandwidth cycle prediction; based on the anti-fluctuation bandwidth data, bit rate allocation data is obtained through dynamic bit rate control.

[0100] Some embodiments of the present application provide corresponding Figure 1 A non-volatile computer storage medium based on block optimization of ultra-high-definition video transmission stores computer executable instructions, wherein the computer executable instructions are set to:

[0101] Ultra-high-definition video frames are acquired and video scene recognition is performed on the ultra-high-definition video frames to obtain a dynamic block suggestion map; based on the dynamic block suggestion map, the video block result is determined through block decision analysis; the video block result is subjected to layered quantization coding processing to obtain dynamic allocation data of differentiated quantization parameters; based on the dynamic allocation data of differentiated quantization parameters, anti-fluctuation bandwidth data is determined through bandwidth cycle prediction; based on the anti-fluctuation bandwidth data, bit rate allocation data is obtained through dynamic bit rate control.

[0102] The various embodiments in this application are described in a progressive manner. Similar portions between the various embodiments can be referenced to each other. Each embodiment focuses on the differences from the other embodiments. In particular, the IoT device and media embodiments are generally similar to the method embodiments, so their description is relatively simple. For relevant portions, refer to the description of the method embodiments.

[0103] The system and medium provided in the embodiments of the present application correspond one-to-one to the method. Therefore, the system and medium also have similar beneficial technical effects to their corresponding methods. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the system and medium will not be repeated here.

[0104] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0105] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0106] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0107] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0108] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0109] Memory may include non-permanent storage in a computer-readable medium, in the form of random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0110] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can be implemented using any method or technology to store information. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change RAM (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media such as modulated data signals and carrier waves.

[0111] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0112] The above are merely embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A block optimization method based on ultra-high-definition video transmission, characterized in that: The method comprises: Acquire an ultra-high-definition video frame, and perform video scene recognition on the ultra-high-definition video frame to obtain a dynamic block suggestion map; Based on the dynamic block suggestion graph, determining the video block result through block decision analysis; Performing hierarchical quantization coding processing on the video segmentation result to obtain dynamic allocation data of differentiated quantization parameters; Determining anti-fluctuation bandwidth data by bandwidth cycle prediction based on the dynamic allocation data of the differentiated quantization parameters; Based on the anti-fluctuation bandwidth data, obtaining bit rate allocation data through dynamic bit rate control; Based on the dynamic segmentation suggestion graph, a segmentation decision analysis is performed to determine the video segmentation result, specifically including: Performing block parameter configuration on the dynamic block suggestion map to obtain a block video frame to be optimized; wherein the block parameter configuration includes: motion-sensitive areas, complex texture areas, and continuous static areas; Performing block boundary optimization on the block video frame to be optimized to determine the video block result; wherein the block boundary optimization includes: morphological closing operation and block size gradient change constraint; Performing hierarchical quantization coding processing on the video segmentation result to obtain dynamic allocation data of differentiated quantization parameters, specifically including: Based on the video segmentation result, determining the motion level corresponding to the segmentation type through motion level matching; Matching the motion levels to corresponding quantization parameters to obtain hierarchical quantization data; Determining the dynamic allocation data of the differentiated quantization parameters through rate weight matching according to the hierarchical quantization data; Determining anti-fluctuation bandwidth data based on the dynamic allocation data of the differentiated quantization parameters through bandwidth cycle prediction specifically includes: Based on the dynamic allocation data, the current available bandwidth is obtained through periodic bandwidth detection; wherein the calculation formula for the periodic bandwidth detection is: in, The payload of a set of probe packets sent at fixed time intervals, It is The round trip time of a data packet, , is the current available bandwidth value; The current available bandwidth is subjected to anti-interference smoothing processing to obtain the anti-fluctuation bandwidth data; wherein the calculation formula for the anti-interference smoothing processing is: in, is the bandwidth value after bandwidth smoothing, The bandwidth value calculated last time; Based on the anti-fluctuation bandwidth data, the bit rate allocation data is obtained through dynamic bit rate control, specifically including: Performing a dynamic code rate calculation on the anti-fluctuation bandwidth data to obtain a code rate to be allocated; wherein the calculation formula for the dynamic code rate calculation is: in, is the maximum bit rate supported by the encoder, For block The code rate weight, For block Current quantization parameter value, is the code rate to be allocated; The code rate to be allocated is allocated to the corresponding code to obtain the code rate allocation data.

2. The block optimization method based on ultra-high-definition video transmission according to claim 1, characterized in that: Performing video scene recognition on the ultra-high-definition video frame to obtain a dynamic block suggestion map specifically includes: Performing motion vector analysis on the ultra-high-definition video frame to obtain a first dynamic feature; Performing texture complexity analysis on the ultra-high-definition video frame to obtain a second dynamic feature; Determining a dynamic feature vector of the ultra-high-definition video frame based on the first dynamic feature and the second dynamic feature; According to the dynamic feature vector, the dynamic block suggestion map is obtained through dynamic block analysis; wherein the dynamic block analysis includes: region suggestion block size marking, block size optimization, and texture complexity scoring.

3. The block optimization method based on ultra-high-definition video transmission according to claim 1, characterized in that: After obtaining bit rate allocation data through dynamic bit rate control based on the anti-fluctuation bandwidth data, the method further includes: Based on the bit rate allocation data, an ultra-high-definition optimized video is obtained; The ultra-high-definition optimized video is transmitted to a video destination via a preset transmission network to obtain an output ultra-high-definition video.

4. A block optimization device based on ultra-high-definition video transmission, characterized in that: The device comprises: at least one processor; and, a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to: Acquire an ultra-high-definition video frame, and perform video scene recognition on the ultra-high-definition video frame to obtain a dynamic block suggestion map; Based on the dynamic block suggestion graph, determining the video block result through block decision analysis; Performing hierarchical quantization coding processing on the video segmentation result to obtain dynamic allocation data of differentiated quantization parameters; Determining anti-fluctuation bandwidth data by bandwidth cycle prediction based on the dynamic allocation data of the differentiated quantization parameters; Based on the anti-fluctuation bandwidth data, obtaining bit rate allocation data through dynamic bit rate control; Based on the dynamic segmentation suggestion graph, a segmentation decision analysis is performed to determine the video segmentation result, specifically including: Performing block parameter configuration on the dynamic block suggestion map to obtain a block video frame to be optimized; wherein the block parameter configuration includes: motion-sensitive areas, complex texture areas, and continuous static areas; Performing block boundary optimization on the block video frame to be optimized to determine the video block result; wherein the block boundary optimization includes: morphological closing operation and block size gradient change constraint; Performing hierarchical quantization coding processing on the video segmentation result to obtain dynamic allocation data of differentiated quantization parameters, specifically including: Based on the video segmentation result, determining the motion level corresponding to the segmentation type through motion level matching; Matching the motion levels to corresponding quantization parameters to obtain hierarchical quantization data; Determining the dynamic allocation data of the differentiated quantization parameters through rate weight matching according to the hierarchical quantization data; Determining anti-fluctuation bandwidth data based on the dynamic allocation data of the differentiated quantization parameters through bandwidth cycle prediction specifically includes: Based on the dynamic allocation data, the current available bandwidth is obtained through periodic bandwidth detection; wherein the calculation formula for the periodic bandwidth detection is: in, The payload of a set of probe packets sent at fixed time intervals, It is The round trip time of a data packet, , is the current available bandwidth value; The current available bandwidth is subjected to anti-interference smoothing processing to obtain the anti-fluctuation bandwidth data; wherein the calculation formula for the anti-interference smoothing processing is: in, is the bandwidth value after bandwidth smoothing, The bandwidth value calculated last time; Based on the anti-fluctuation bandwidth data, the bit rate allocation data is obtained through dynamic bit rate control, specifically including: A code rate dynamic calculation is performed on the anti-fluctuation bandwidth data to obtain a code rate to be allocated; wherein the calculation formula for the code rate dynamic calculation is: in, is the maximum bit rate supported by the encoder, For block The code rate weight, For block Current quantization parameter value, is the code rate to be allocated; The code rate to be allocated is allocated to the corresponding code to obtain the code rate allocation data.

5. A non-volatile computer storage medium based on block optimization for ultra-high-definition video transmission, storing computer-executable instructions, characterized in that: The computer executable instructions are configured to: Acquire an ultra-high-definition video frame, and perform video scene recognition on the ultra-high-definition video frame to obtain a dynamic block suggestion map; Based on the dynamic segmentation suggestion graph, determining the video segmentation result through segmentation decision analysis; Performing hierarchical quantization coding processing on the video segmentation result to obtain dynamic allocation data of differentiated quantization parameters; Determining anti-fluctuation bandwidth data by bandwidth cycle prediction based on the dynamic allocation data of the differentiated quantization parameters; Based on the anti-fluctuation bandwidth data, obtaining bit rate allocation data through dynamic bit rate control; Based on the dynamic segmentation suggestion graph, a segmentation decision analysis is performed to determine the video segmentation result, specifically including: Performing block parameter configuration on the dynamic block suggestion map to obtain a block video frame to be optimized; wherein the block parameter configuration includes: motion-sensitive areas, complex texture areas, and continuous static areas; Performing block boundary optimization on the block video frame to be optimized to determine the video block result; wherein the block boundary optimization includes: morphological closing operation and block size gradient change constraint; Performing hierarchical quantization coding processing on the video segmentation result to obtain dynamic allocation data of differentiated quantization parameters, specifically including: Based on the video segmentation result, determining the motion level corresponding to the segmentation type through motion level matching; Matching the motion levels to corresponding quantization parameters to obtain hierarchical quantization data; Determining the dynamic allocation data of the differentiated quantization parameters through rate weight matching according to the hierarchical quantization data; Determining anti-fluctuation bandwidth data based on the dynamic allocation data of the differentiated quantization parameters through bandwidth cycle prediction specifically includes: Based on the dynamic allocation data, the current available bandwidth is obtained through periodic bandwidth detection; wherein the calculation formula for the periodic bandwidth detection is: in, The payload of a set of probe packets sent at fixed time intervals, It is The round trip time of a data packet, , is the current available bandwidth value; The current available bandwidth is subjected to anti-interference smoothing processing to obtain the anti-fluctuation bandwidth data; wherein the calculation formula for the anti-interference smoothing processing is: in, is the bandwidth value after bandwidth smoothing, The bandwidth value calculated last time; Based on the anti-fluctuation bandwidth data, the bit rate allocation data is obtained through dynamic bit rate control, specifically including: Performing a dynamic code rate calculation on the anti-fluctuation bandwidth data to obtain a code rate to be allocated; wherein the calculation formula for the dynamic code rate calculation is: in, is the maximum bit rate supported by the encoder, For block The code rate weight, For block Current quantization parameter value, is the code rate to be allocated; The code rate to be allocated is allocated to the corresponding code to obtain the code rate allocation data.

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