Video coding and decoding method, device, equipment and storage medium

Abstract

The invention discloses a video encoding and decoding method, device and equipment and a storage medium, and relates to the technical field of video processing, and the method comprises the steps: determining to-be-processed video data at a current moment, and monitoring a network state at the current moment and an own equipment state; based on the to-be-processed video data, the network state and the equipment state, screening from a preset coding and decoding strategy library by using a quantum heuristic optimization algorithm to obtain target video coding and decoding parameters; the quantum heuristic optimization algorithm is a parallel optimization algorithm written by using a graphics processor framework; and adjusting related parameters of a graphics processor by using the target video coding and decoding parameters, and carrying out coding and decoding operation on the to-be-processed video data by using the adjusted graphics processor. Therefore, the method can dynamically adjust the related parameters of the video coding and decoding according to the real-time scene state of the video coding and decoding based on the quantum heuristic optimization algorithm, and improves the processing efficiency of the video coding and decoding.

Description

Technical Field

[0001] The present invention relates to the field of video processing technology, and in particular to a video encoding and decoding method, device, equipment and storage medium. Background Art

[0002] With the explosive growth of high-definition and ultra-high-definition video content, the currently commonly used video codec technology usually pre-sets the relevant parameters of video codec, and performs all subsequent video codec tasks according to a set of pre-set parameters. This cannot guarantee that the pre-set codec-related parameters are applicable to all subsequent video codec tasks, and may affect the processing efficiency of video codec in some scenarios. When processing complex video scenes, there are bottlenecks in computing resources and efficiency.

[0003] It can be seen that how to flexibly set relevant parameters of video encoding and decoding is a problem to be solved in this field. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide a video encoding and decoding method, device, equipment and storage medium, which can dynamically adjust the relevant parameters of video encoding and decoding according to the scene state of real-time video encoding and decoding based on the quantum-inspired optimization algorithm to improve the processing efficiency of video encoding and decoding. The specific scheme is as follows:

[0005] In a first aspect, the present application provides a video encoding and decoding method, comprising:

[0006] Determine the video data to be processed at the current moment, and monitor the network status and the device status at the current moment;

[0007] Based on the video data to be processed, the network status and the device status, a target video codec parameter is obtained by screening from a preset codec strategy library using a quantum-inspired optimization algorithm; the quantum-inspired optimization algorithm is a parallel optimization algorithm written using a graphics processor framework;

[0008] The target video encoding and decoding parameters are used to adjust relevant parameters of a graphics processor, and the adjusted graphics processor is used to perform encoding and decoding operations on the video data to be processed.

[0009] Optionally, determining the video data to be processed at the current moment includes:

[0010] The original video data at the current moment is preprocessed to obtain the video data to be processed for encoding and decoding operations.

[0011] Optionally, monitoring the current network status and the device status of the device includes:

[0012] Based on the parameter format corresponding to the quantum-inspired optimization algorithm, the monitoring data containing the current network status and its own device status is format converted to obtain the corresponding target network status and target device status.

[0013] Optionally, before the method uses the quantum-inspired optimization algorithm to filter and obtain target video codec parameters from a preset codec strategy library, it also includes:

[0014] The number of quantum bits, the initial superposition state, and the optimization function of the quantum-inspired optimization algorithm are configured so that the configured quantum-inspired optimization algorithm can be used to filter the target video encoding and decoding parameters from the preset encoding and decoding strategy library.

[0015] Optionally, the step of using a quantum-inspired optimization algorithm to select target video codec parameters from a preset codec strategy library includes:

[0016] Based on the quantum-inspired optimization algorithm, the state transfer and measurement process of quantum bits are simulated on the graphics processor to iteratively converge through parallel computing to obtain the corresponding calculation results;

[0017] The target video encoding and decoding parameters are determined from a preset encoding and decoding strategy library according to the calculation result.

[0018] Optionally, before the step of using the quantum-inspired optimization algorithm to filter and obtain target video codec parameters from a preset codec strategy library, the step further includes:

[0019] Based on the quantum-inspired optimization algorithm, the performance of each video encoding and decoding parameter in the preset encoding and decoding strategy library is evaluated by simulating the encoding and decoding process or using a test video data set to obtain the corresponding evaluation results;

[0020] According to the evaluation result, the relevant video codec parameters in the preset codec strategy library are eliminated to obtain a final preset codec strategy library, so as to use the quantum-inspired optimization algorithm to screen the target video codec parameters from the preset codec strategy library.

[0021] Optionally, after using the adjusted graphics processor to perform encoding and decoding operations on the video data to be processed, the method further includes:

[0022] The video transmission conditions corresponding to the encoding and decoding operation are collected, and the preset encoding and decoding strategy library and / or the quantum-inspired optimization algorithm are adjusted based on the video transmission conditions, so as to perform the encoding and decoding operation based on the adjusted preset encoding and decoding strategy library and / or the quantum-inspired optimization algorithm.

[0023] In a second aspect, the present application provides a video encoding and decoding device, including:

[0024] The data determination module is used to determine the video data to be processed at the current moment and monitor the network status and the device status at the current moment;

[0025] A parameter screening module, for screening target video codec parameters from a preset codec strategy library using a quantum-inspired optimization algorithm based on the video data to be processed, the network status, and the device status; the quantum-inspired optimization algorithm is a parallel optimization algorithm written using a graphics processor framework;

[0026] The encoding and decoding module is used to adjust relevant parameters of a graphics processor using the target video encoding and decoding parameters, and use the adjusted graphics processor to perform encoding and decoding operations on the video data to be processed.

[0027] In a third aspect, the present application provides an electronic device, including:

[0028] Memory, used to store computer programs;

[0029] A processor is used to execute the computer program to implement the video encoding and decoding method as described above.

[0030] In a fourth aspect, the present application provides a computer-readable storage medium for storing a computer program, wherein the computer program implements the video encoding and decoding method as described above when executed by a processor.

[0031] It can be seen that in this application, first of all, it is necessary to determine the video data to be processed at the current moment, and monitor the network status and the device status at the current moment; then, based on the video data to be processed, the network status and the device status, the target video codec parameters are screened from the preset codec strategy library using the quantum-inspired optimization algorithm; the quantum-inspired optimization algorithm is a parallel optimization algorithm written using the graphics processor framework; the target video codec parameters are then used to adjust the relevant parameters of the graphics processor, and the adjusted graphics processor is used to perform codec operations on the video data to be processed. In this way, the present application can dynamically adjust the relevant parameters of the video codec based on the scene status of the real-time video codec based on the quantum-inspired optimization algorithm; and the parallel quantum-inspired optimization algorithm written based on the image processor framework can efficiently screen out the target video codec parameters, which can improve the adaptability of the video codec parameters to the real-time scene and improve the processing efficiency of the video codec. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0033] Figure 1 A flow chart of a video encoding and decoding method disclosed in this application;

[0034] Figure 2 A flowchart of a specific video encoding and decoding method disclosed in this application;

[0035] Figure 3 A schematic diagram of the structure of a video encoding and decoding device disclosed in this application;

[0036] Figure 4 This is a structural diagram of an electronic device disclosed in this application. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] See also Figure 1 As shown, an embodiment of the present invention discloses a video encoding and decoding method, including:

[0039] Step S11, determine the video data to be processed at the current moment, and monitor the network status and the device status at the current moment.

[0040] In the embodiment of the present application, during the execution of the video encoding and decoding task, the relevant video data that needs to be processed at the current moment can be determined, that is, the video data to be processed at the current moment can be determined; and the network status and the device status at the current moment can be monitored. It is understandable that the video processing performance in the actual video processing scenario will be affected by the network status and the device operation status.

[0041] In a specific embodiment, the determination of the video data to be processed at the current moment may include: preprocessing the original video data at the current moment to obtain the video data to be processed for the encoding and decoding operation. Specifically, when executing the video encoding and decoding task, it is first necessary to obtain the original video data at the current moment, and further preprocess the original video data. The preprocessing here may include but is not limited to format conversion, resolution adjustment, noise removal, etc., to facilitate the subsequent encoding and decoding operation. Correspondingly, in another specific embodiment, the monitoring of the network state and the device state of the device at the current moment may include: based on the parameter format corresponding to the quantum heuristic optimization algorithm, the monitoring data containing the network state and the device state of the device at the current moment is format converted to obtain the corresponding target network state and target device state. Specifically, since the performance of the video encoding and decoding needs to be optimized in combination with the quantum heuristic optimization algorithm in the future, when the network state and the device state of the device at the current moment are obtained by monitoring, the format conversion of the monitored related data can be performed based on the parameter format of the quantum heuristic optimization algorithm, so that the processed target network state and target device state can be recognized and used by the quantum heuristic optimization algorithm.

[0042] Step S12: Based on the video data to be processed, the network status and the device status, a target video codec parameter is obtained by screening from a preset codec strategy library using a quantum-inspired optimization algorithm; the quantum-inspired optimization algorithm is a parallel optimization algorithm written using a graphics processor framework.

[0043] It is understandable that the quantum-inspired optimization algorithm should be pre-set and can be written using the graphics processor framework to implement a parallel quantum-inspired optimization algorithm, through which the optimal parameters can be searched in the hyperdimensional space; specifically, a pre-prepared quantum-inspired optimization algorithm is used to screen parameters from a preset codec strategy library to obtain the target adaptation codec parameters with the best performance that is adapted to the current video data to be processed, network status, and device status.

[0044] In a specific embodiment, before the target video codec parameters are obtained from the preset codec policy library by using the quantum-inspired optimization algorithm, the method may further include: configuring the number of quantum bits, the initial superposition state, and the optimization function of the quantum-inspired optimization algorithm, so as to obtain the target video codec parameters from the preset codec policy library by using the configured quantum-inspired optimization algorithm. Specifically, the quantum-inspired optimization algorithm needs to set the initial parameters in advance, which may include the number of quantum bits, the configuration of the initial superposition state, and the objective function for optimization; then, the configured quantum-inspired optimization algorithm may be used to obtain the target video codec parameters from the preset codec policy library during the execution of the video codec task.

[0045] In another specific embodiment, the method of using a quantum-inspired optimization algorithm to select and obtain target video codec parameters from a preset codec strategy library may include: simulating the state transfer and measurement process of quantum bits on a graphics processor based on the quantum-inspired optimization algorithm to iteratively converge through parallel computing to obtain corresponding calculation results; and determining the target video codec parameters from the preset codec strategy library according to the calculation results. Specifically, the quantum-inspired optimization algorithm is a parallel algorithm pre-written using a graphics processor framework, which can perform parallel computing iterative convergence, simulate the state transfer and strategy process of quantum bits on a graphics processor, and obtain the final calculation results, i.e., the optimal target video codec parameters, through iterative computing.

[0046] In another specific embodiment, before the target video codec parameters are obtained from the preset codec policy library by using the quantum-inspired optimization algorithm, the method may further include: based on the quantum-inspired optimization algorithm, by simulating the codec process or using a test video data set, the performance of each video codec parameter in the preset codec policy library is evaluated to obtain a corresponding evaluation result; according to the evaluation result, the relevant video codec parameters in the preset codec policy library are eliminated to obtain a final preset codec policy library, so as to obtain the target video codec parameters from the preset codec policy library by using the quantum-inspired optimization algorithm. Specifically, in the process of presetting the codec policy library, it can be expanded or customized based on the existing video codec standard, and can include a variety of codec algorithms, parameter settings and encoding modes; then, the performance of each strategy (codec parameter) in the policy library can be evaluated based on the quantum-inspired optimization algorithm, and the effect of each strategy can be evaluated by simulating the actual codec process or using a test video data set; according to the corresponding evaluation results, the strategy with better effect is selected, and the inefficient strategy is eliminated, that is, the preset codec policy library is finally obtained; subsequently, the target codec parameters can be selected from the preset codec policy library by using the quantum-inspired optimization algorithm.

[0047] Step S13: using the target video encoding and decoding parameters to adjust relevant parameters of a graphics processor, and using the adjusted graphics processor to perform encoding and decoding operations on the video data to be processed.

[0048] In an embodiment of the present application, the above steps can refer to the video to be processed, the network status and the device status at the current moment to determine more appropriate target codec parameters; then the screened target codec parameters can be used to adjust the relevant parameters of the graphics processor that performs the video encoding and decoding task, and the graphics processor with the corresponding adjusted parameters can be used to perform encoding and decoding operations on the video data to be processed at the current moment.

[0049] In a specific embodiment, after the video data to be processed is encoded and decoded using the adjusted graphics processor, it may also include: collecting the video transmission conditions corresponding to the encoding and decoding operation, and adjusting the preset encoding and decoding strategy library and / or the quantum-inspired optimization algorithm based on the video transmission conditions, so as to perform the encoding and decoding operation based on the adjusted preset encoding and decoding strategy library and / or quantum-inspired optimization algorithm. Specifically, after the video data to be processed is encoded and decoded, the transmission conditions of the relevant video data may be further collected, so that the validity of the currently used encoding and decoding parameters can be verified; the preset encoding and decoding strategy library and / or quantum-inspired optimization algorithm may be adjusted according to the actual video transmission conditions, and the video encoding and decoding processing process may be continuously optimized and improved.

[0050] It can be seen that the present application can dynamically adjust the relevant parameters of video encoding and decoding according to the scene status of real-time video encoding and decoding based on the quantum-inspired optimization algorithm; and the parallel quantum-inspired optimization algorithm written based on the image processor framework can efficiently screen out the target video encoding and decoding parameters, which can improve the adaptability of the video encoding and decoding parameters to the real-time scenes and improve the processing efficiency of video encoding and decoding.

[0051] like Figure 2 As shown, the embodiment of the present application discloses a video encoding and decoding method, which specifically includes:

[0052] In this embodiment, the entire video encoding and decoding method can be divided into a data acquisition layer, a preprocessing layer, an optimization engine layer, a graphics processor acceleration layer, and an output control layer; wherein, the data acquisition layer: is responsible for capturing the original video data and real-time monitoring of changes in video content, fluctuations in network bandwidth, and the status of terminal device performance. This includes video capture devices (such as cameras), network monitoring tools, and performance monitoring APIs (Application Programming Interfaces). Preprocessing layer: performs preliminary processing on the collected original video data, such as format conversion, resolution adjustment, noise removal, etc., to facilitate subsequent optimization and encoding processes. At the same time, the real-time monitored data is formatted so that it can be recognized and used by the optimization engine. Optimization engine layer: Quantum-inspired optimization algorithm module: implements quantum-inspired optimization algorithms, and uses GPU (Graphics Processing Unit, graphics processor) programming frameworks to write parallel algorithms, such as CUDA (Compute Unified Device Architecture, a parallel computing platform), or OpenCL (Open Computing Language, open computing language). This module is responsible for searching for the optimal combination of encoding and decoding parameters in hyperdimensional space, and accelerates the convergence speed of the optimization process by simulating the superposition state and entanglement characteristics of quantum bits. GPGPU (General-purpose computing on graphics processing units) acceleration layer: deploy the core computing tasks (such as parameter search, policy evaluation, etc.) in the optimization engine layer to the GPGPU for execution. Utilize the parallel computing capabilities of the GPU to accelerate large-scale data processing and complex computing tasks and improve the overall performance of the system. Design dedicated GPU parallel algorithms and data structures, optimize memory access patterns, reduce data transmission overhead, and further improve the acceleration effect. Output control layer: encode or decode the pre-processed video data according to the optimal encoding and decoding strategy output by the optimization engine. In addition, it can also include a strategy library management module: manage the super-dimensional encoding and decoding strategy library, including the addition, deletion, update and performance evaluation of strategies. This module works closely with the optimization algorithm module to provide a rich selection of strategies for the optimization algorithm, and dynamically adjusts the content of the strategy library according to the optimization results. Furthermore, the encoded video stream can be controlled to be output to the target device or network to ensure the efficiency and smoothness of video transmission. At the same time, the output parameters such as bit rate and frame rate are dynamically adjusted according to real-time monitoring data to adapt to changes in network bandwidth and terminal device performance.

[0053] Specifically, the implementation process of the quantum-inspired optimization algorithm includes three parts: algorithm initialization, parallel computing implementation, and optimization result extraction. Among them, algorithm initialization: setting the initial parameters of the quantum-inspired optimization algorithm, including the number of quantum bits, the configuration of the initial superposition state, the optimization objective function, etc. Parallel computing implementation: using the GPU programming framework to write a parallel implementation version of the quantum-inspired optimization algorithm. The state transfer and measurement process of quantum bits are simulated on the GPU, and the iterative convergence of the optimization process is accelerated through parallel computing. Optimization result extraction: reading the optimization results from the GPU, that is, the optimal combination of encoding and decoding parameters. These parameters will be used to guide the subsequent video encoding or decoding process.

[0054] Correspondingly, the processing of the policy library may include policy library initialization, policy evaluation and screening, and policy update and expansion; among them, policy library initialization: construct an initial super-dimensional codec policy library, including multiple codec algorithms, parameter settings, and encoding modes. These policies can be expanded and customized based on existing video coding standards (such as H.264, H.265, etc.). Policy evaluation and screening: use the optimization engine to evaluate the performance of each policy in the policy library. Evaluate the effectiveness of the policy by simulating the actual coding and decoding process or using a test video data set. Filter out efficient policies based on the evaluation results and eliminate inefficient policies. Policy update and expansion: With the development of video coding technology and changes in market demand, continuously update and expand the content of the policy library. Introduce new codec algorithms, parameter settings, and encoding modes to adapt to different video content and network environments.

[0055] Furthermore, the process of adjusting the strategy according to the actual situation may include real-time monitoring, intelligent analysis, strategy adjustment and feedback optimization; among them, real-time monitoring: collect real-time monitoring data of video content, network bandwidth and terminal device performance through the data acquisition layer. These data will be used to evaluate the current status of the video transmission environment. Intelligent analysis: intelligent analysis of real-time monitoring data to identify key factors affecting video transmission efficiency (such as network congestion, device performance bottlenecks, etc.). Strategy adjustment: dynamically adjust the encoding and decoding strategy according to the results of intelligent analysis. The optimization engine will re-search the optimal strategy suitable for the current environment in the hyperdimensional strategy library, and realize rapid switching and adjustment of the strategy through the GPGPU acceleration layer. Feedback optimization: apply the adjusted strategy to actual video transmission, and collect feedback data to further verify the effectiveness of the strategy. Continuously optimize and improve the strategy library and optimization engine based on feedback data.

[0056] It can be seen that the present application can dynamically adjust the relevant parameters of video encoding and decoding according to the scene status of real-time video encoding and decoding based on the quantum-inspired optimization algorithm; and the parallel quantum-inspired optimization algorithm written based on the image processor framework can efficiently screen out the target video encoding and decoding parameters, which can improve the adaptability of the video encoding and decoding parameters to the real-time scene; and the strategy library or optimization algorithm can be further optimized according to the working conditions of the adjusted graphics processor to continuously optimize the processing efficiency of video encoding and decoding.

[0057] like Figure 3 As shown, the embodiment of the present application discloses a video encoding and decoding device, including:

[0058] The data determination module 11 is used to determine the video data to be processed at the current moment, and monitor the network status and the device status at the current moment;

[0059] A parameter screening module 12, for screening target video codec parameters from a preset codec strategy library using a quantum-inspired optimization algorithm based on the video data to be processed, the network status, and the device status; the quantum-inspired optimization algorithm is a parallel optimization algorithm written using a graphics processor framework;

[0060] The encoding and decoding module 13 is used to adjust relevant parameters of a graphics processor using the target video encoding and decoding parameters, and use the adjusted graphics processor to perform encoding and decoding operations on the video data to be processed.

[0061] It can be seen that the present application can dynamically adjust the relevant parameters of video encoding and decoding according to the scene status of real-time video encoding and decoding based on the quantum-inspired optimization algorithm; and the parallel quantum-inspired optimization algorithm written based on the image processor framework can efficiently screen out the target video encoding and decoding parameters, which can improve the adaptability of the video encoding and decoding parameters to the real-time scenes and improve the processing efficiency of video encoding and decoding.

[0062] In a specific embodiment, the data determination module 11 may include:

[0063] The data determination unit is used to pre-process the original video data at the current moment to obtain the video data to be processed for encoding and decoding operations.

[0064] In a specific embodiment, the data determination module 11 may include:

[0065] The data processing unit is used to perform format conversion processing on the monitoring data including the current network status and the device status of the device itself based on the parameter format corresponding to the quantum-inspired optimization algorithm, so as to obtain the corresponding target network status and target device status.

[0066] In a specific embodiment, the device may further include:

[0067] The algorithm configuration module is used to configure the number of quantum bits, initial superposition state and optimization function of the quantum-inspired optimization algorithm, so as to use the configured quantum-inspired optimization algorithm to filter and obtain the target video encoding and decoding parameters from the preset encoding and decoding strategy library.

[0068] In a specific embodiment, the parameter screening module 12 may include:

[0069] A computing unit, used to simulate the state transfer and measurement process of quantum bits on a graphics processor based on a quantum-inspired optimization algorithm, so as to perform iterative convergence through parallel computing and obtain corresponding computing results;

[0070] The parameter screening unit is used to determine the target video encoding and decoding parameters from the preset encoding and decoding strategy library according to the calculation result.

[0071] In a specific embodiment, the device may further include:

[0072] The parameter evaluation module is used to evaluate the performance of each video encoding and decoding parameter in the preset encoding and decoding strategy library by simulating the encoding and decoding process or using a test video data set based on a quantum-inspired optimization algorithm to obtain corresponding evaluation results;

[0073] The parameter elimination module is used to eliminate the relevant video codec parameters in the preset codec strategy library according to the evaluation result to obtain a final preset codec strategy library, so as to use the quantum-inspired optimization algorithm to screen the target video codec parameters from the preset codec strategy library.

[0074] In a specific embodiment, the device may further include:

[0075] An adjustment module is used to collect the video transmission status corresponding to the encoding and decoding operation, and adjust the preset encoding and decoding strategy library and / or the quantum-inspired optimization algorithm based on the video transmission status, so as to perform the encoding and decoding operation based on the adjusted preset encoding and decoding strategy library and / or quantum-inspired optimization algorithm.

[0076] Furthermore, the present application also discloses an electronic device. Figure 4 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content in the diagram cannot be regarded as any limitation on the scope of use of the present application.

[0077] Figure 4The present invention provides a schematic diagram of the structure of an electronic device 20 provided in an embodiment of the present application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 is used to store a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the video encoding and decoding method disclosed in any of the above embodiments. In addition, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0078] In this embodiment, the power supply 23 is used to provide working voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and the external device, and the communication protocol it follows is any communication protocol that can be applied to the technical solution of the present application, and is not specifically limited here; the input and output interface 25 is used to obtain external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs and is not specifically limited here.

[0079] In addition, the memory 22, as a carrier for storing resources, can be a read-only memory, a random access memory, a disk or an optical disk, etc. The resources stored thereon can include an operating system 221, a computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0080] The operating system 221 is used to manage and control the hardware devices and computer programs 222 on the electronic device 20, and can be Windows Server, Netware, Unix, Linux, etc. In addition to including a computer program that can be used to complete the video encoding and decoding method performed by the electronic device 20 disclosed in any of the aforementioned embodiments, the computer program 222 can further include a computer program that can be used to complete other specific tasks.

[0081] Furthermore, the present application also discloses a computer-readable storage medium for storing a computer program; wherein the computer program, when executed by a processor, implements the video encoding and decoding method disclosed above. For the specific steps of the method, reference may be made to the corresponding contents disclosed in the above embodiments, and no further description will be given here.

[0082] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0083] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0084] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0085] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0086] The technical solution provided by the present application is introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. At the same time, for general technicians in this field, according to the idea of ​​the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A video encoding and decoding method, characterized in that: include: Determine the video data to be processed at the current moment, and monitor the network status and the device status at the current moment; Based on the video data to be processed, the network status and the device status, a target video codec parameter is obtained by screening from a preset codec strategy library using a quantum-inspired optimization algorithm; The quantum-inspired optimization algorithm is a parallel optimization algorithm written using a graphics processor framework; The target video encoding and decoding parameters are used to adjust relevant parameters of a graphics processor, and the adjusted graphics processor is used to perform encoding and decoding operations on the video data to be processed.

2. The video encoding and decoding method according to claim 1, characterized in that: The determining of the video data to be processed at the current moment includes: The original video data at the current moment is preprocessed to obtain the video data to be processed for encoding and decoding operations.

3. The video encoding and decoding method according to claim 1, characterized in that: The monitoring of the current network status and the device status of the device itself includes: Based on the parameter format corresponding to the quantum-inspired optimization algorithm, the monitoring data containing the current network status and its own device status is format converted to obtain the corresponding target network status and target device status.

4. The video encoding and decoding method according to claim 1, characterized in that: Before the method of using the quantum inspired optimization algorithm to select and obtain the target video encoding and decoding parameters from the preset encoding and decoding strategy library, the method further includes: The number of quantum bits, the initial superposition state, and the optimization function of the quantum-inspired optimization algorithm are configured so that the configured quantum-inspired optimization algorithm can be used to filter the target video encoding and decoding parameters from the preset encoding and decoding strategy library.

5. The video encoding and decoding method according to claim 1, characterized in that: The method of using a quantum-inspired optimization algorithm to select target video encoding and decoding parameters from a preset encoding and decoding strategy library includes: Based on the quantum-inspired optimization algorithm, the state transfer and measurement process of quantum bits are simulated on the graphics processor to iteratively converge through parallel computing to obtain the corresponding calculation results; The target video encoding and decoding parameters are determined from a preset encoding and decoding strategy library according to the calculation result.

6. The video encoding and decoding method according to claim 1, characterized in that: Before the method of using the quantum inspired optimization algorithm to select and obtain the target video encoding and decoding parameters from the preset encoding and decoding strategy library, the method further includes: Based on the quantum-inspired optimization algorithm, the performance of each video encoding and decoding parameter in the preset encoding and decoding strategy library is evaluated by simulating the encoding and decoding process or using a test video data set to obtain the corresponding evaluation results; According to the evaluation result, the relevant video codec parameters in the preset codec strategy library are eliminated to obtain a final preset codec strategy library, so as to use the quantum-inspired optimization algorithm to screen the target video codec parameters from the preset codec strategy library.

7. The video encoding and decoding method according to any one of claims 1 to 6, characterized in that: After the adjusted graphics processor is used to perform encoding and decoding operations on the video data to be processed, the method further includes: The video transmission conditions corresponding to the encoding and decoding operation are collected, and the preset encoding and decoding strategy library and / or the quantum-inspired optimization algorithm are adjusted based on the video transmission conditions, so as to perform the encoding and decoding operation based on the adjusted preset encoding and decoding strategy library and / or the quantum-inspired optimization algorithm.

8. A video encoding and decoding device, characterized in that: include: The data determination module is used to determine the video data to be processed at the current moment and monitor the network status and the device status at the current moment; A parameter screening module, used to screen target video codec parameters from a preset codec strategy library using a quantum-inspired optimization algorithm based on the video data to be processed, the network status, and the device status; The quantum-inspired optimization algorithm is a parallel optimization algorithm written using a graphics processor framework; The encoding and decoding module is used to adjust relevant parameters of a graphics processor using the target video encoding and decoding parameters, and use the adjusted graphics processor to perform encoding and decoding operations on the video data to be processed.

9. An electronic device, characterized in that: include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the video encoding and decoding method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: Used to store a computer program, which, when executed by a processor, implements the video encoding and decoding method according to any one of claims 1 to 7.

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