Video data processing method, device, equipment and readable storage medium

Abstract

The embodiment of the present application discloses a video data processing method, device, equipment and readable storage medium, wherein the method includes: obtaining the video data to be processed, and obtaining the heat level of the video data to be processed, and determining the processing order of the video data to be processed based on the heat level; performing feature analysis on the video data to be processed to determine the video feature of the video data to be processed, and the video feature is used to indicate at least one of the video quality information or video content information of the video data to be processed; obtaining the video processing strategy corresponding to the video feature, and performing video processing on the video data to be processed according to the processing order using the video processing strategy to obtain the target video data; the video processing strategy includes video transcoding parameters, and the video processing includes video transcoding processing. By using the embodiment of the present application, targeted video processing can be achieved and video processing efficiency can be improved.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a video data processing method, device, equipment and readable storage medium. Background Art

[0002] Before playing the video data, it is necessary to upload the video data on the platform. After uploading the video data, it is necessary to perform video pre-processing and video transcoding on the video data, so as to push the processed video data.

[0003] Currently, a unified video pre-processing and video transcoding method is used to process the uploaded video data. However, since the uploaded video data is diverse, that is, the conditions of each video data such as video size, video content, video clarity, etc. are all different, targeted video processing cannot be achieved by using a unified video processing method, which will reduce the video processing efficiency. Summary of the invention

[0004] The embodiments of the present application provide a video data processing method, apparatus, device and readable storage medium, which can realize targeted video processing and improve video processing efficiency.

[0005] In a first aspect, the present application provides a video data processing method, comprising:

[0006] Obtaining video data to be processed, and obtaining a heat level of the video data to be processed, and determining a processing order of the video data to be processed based on the heat level;

[0007] Performing feature analysis on the video data to be processed to determine a video feature of the video data to be processed, where the video feature is used to indicate at least one of video quality information or video content information of the video data to be processed;

[0008] A video processing strategy corresponding to the video feature is obtained, and the video processing strategy is used to perform video processing on the video data to be processed according to the processing order to obtain target video data; the video processing strategy includes video transcoding parameters, and the video processing includes transcoding processing.

[0009] In a second aspect, the present application provides another video data processing method, comprising:

[0010] In response to the to-be-processed video data having a heat level greater than a level threshold, feature analysis is performed on the to-be-processed video data to determine a video feature of the to-be-processed video data; the video feature is used to indicate at least one of video quality information or video content information of the to-be-processed video data;

[0011] A video processing strategy corresponding to the video feature is obtained, and the video processing strategy is used to perform video processing on the video data to be processed to obtain target video data; the video processing strategy includes at least one of video enhancement processing and video transcoding processing.

[0012] In a third aspect, the present application provides a video data processing device, the device comprising:

[0013] A video acquisition unit, used to acquire video data to be processed, and acquire the heat level of the video data to be processed, and determine the processing order of the video data to be processed based on the heat level;

[0014] A video analysis unit, used for performing feature analysis on the video data to be processed to determine a video feature of the video data to be processed, where the video feature is used to indicate at least one of video quality information or video content information of the video data to be processed;

[0015] The video processing unit is used to obtain the video processing strategy corresponding to the video feature, and use the video processing strategy to perform video processing on the video data to be processed according to the processing order to obtain target video data; the video processing strategy includes video transcoding parameters, and the video processing includes transcoding processing.

[0016] In a fourth aspect, the present application provides another video data processing device, the device comprising:

[0017] A video response unit, configured to perform feature analysis on the video data to be processed in response to the video data to be processed whose heat level is greater than the level threshold, so as to determine the video features of the video data to be processed; the video features are used to indicate at least one of the video quality information or the video content information of the video data to be processed;

[0018] The strategy acquisition unit is used to acquire the video processing strategy corresponding to the video feature, and use the video processing strategy to perform video processing on the video data to be processed to obtain the target video data; the video processing strategy includes at least one of video enhancement processing and video transcoding processing.

[0019] In a fifth aspect, the present application provides a computer device, including: a processor, a memory, and a network interface;

[0020] The above-mentioned processor is connected to a memory and a network interface, wherein the network interface is used to provide a data communication function, the above-mentioned memory is used to store computer program code, and the above-mentioned processor is used to call the above-mentioned computer program code so that a computer device including the processor executes the video data processing method of the first aspect and the second aspect.

[0021] In a sixth aspect, the present application provides a computer-readable storage medium, which stores a computer program, and the computer program is suitable for being loaded and executed by a processor so that a computer device having the processor performs the video data processing method of the first and second aspects mentioned above.

[0022] In a seventh aspect, the present application provides a computer program product or a computer program, the computer program product or the computer program including computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the video data processing method provided in various optional ways in the first aspect and the second aspect of the present application.

[0023] In the embodiment of the present application, by obtaining the heat level of the video data to be processed, determining the processing order of the video data to be processed based on the heat level, determining the processing order by using the heat level of the video data to be processed, and performing video processing in combination with the processing order, the video processing efficiency can be improved. By performing feature analysis on the video data to be processed, the video features of the video quality information and video content information of the video data to be processed can be determined; and then when selecting the video processing strategy corresponding to the video data to be processed, the strategy selection can be combined with the specific video features of the video data, and when the video data to be processed is subsequently processed, targeted video processing can be achieved to improve the accuracy of data processing. Since the video features of the video data to be processed are analyzed from multiple dimensions, the obtained video processing strategy is more targeted, the video processing strategy is also more accurate, and the video processing is performed according to the features contained in the video data. Compared with the use of a unified video processing method for video processing, the amount of data calculation can be reduced, and the video processing efficiency can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0025] Figure 1 is a network architecture diagram of a video data processing system provided by an embodiment of the present application;

[0026] Figure 2 It is a flowchart of a video data processing method provided in an embodiment of the present application;

[0027] Figure 3aIt is a flowchart of another video data processing method provided in an embodiment of the present application;

[0028] Figure 3b It is a flowchart of another video data processing method provided in an embodiment of the present application;

[0029] Figure 4a It is a schematic diagram of the composition structure of a video data processing device provided in an embodiment of the present application;

[0030] Figure 4b is a schematic diagram of the composition structure of another video data processing device provided in an embodiment of the present application;

[0031] Figure 5 It is a schematic diagram of the composition structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0032] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0033] In some possible scenarios, since there are multiple processing methods for video data, such as video pre-processing and video transcoding methods, and the video data uploaded in the video playback platform is diverse, each video data contains different video parameters, so the algorithms and parameters of video pre-processing required for different video data, as well as the parameters of video transcoding are also different. The current video processing method generally adopts a unified processing method for all video data, and the pertinence of video processing is low, and targeted video processing cannot be achieved. In view of this, the technical solution of the present application provides a video data processing method, which can find suitable video pre-processing algorithms and parameters for different video data, as well as suitable video transcoding algorithms and transcoding parameters, so as to achieve targeted video processing, so that the effect of each video data is better, and the video processing efficiency can be improved. The technical solution in the embodiment of the present application can also be applied to any platform that needs to process video data, such as, but not limited to, video playback platform, video editing platform, video interaction platform and other platforms with the function of playing video data, and the embodiment of the present application is not limited to this.

[0034] Please refer to Figure 1 , Figure 1 is a network architecture diagram of a video data processing system provided in an embodiment of the present application, such as Figure 1As shown, the computer device can exchange data with the terminal device, and the number of the terminal devices can be one or at least two. For example, when the number of the terminal devices is multiple, the terminal device may include Figure 1 The terminal device 101a, the terminal device 101b and the terminal device 101c, etc. Among them, taking the terminal device 101a as an example, the computer device 102 can obtain the video data to be processed and the heat level of the video data to be processed, and determine the processing order of the video data to be processed based on the heat level. Further, the computer device 102 can perform feature analysis on the video data to be processed to determine the video features of the video data to be processed. Further, the computer device 102 can also obtain the video processing strategy corresponding to the video feature, and use the video processing strategy to perform video processing on the video data to be processed according to the processing order to obtain the target video data. Optionally, the computer device 102 can also send the video data to be processed to the terminal device 101a to play the video data to be processed on the terminal device 101a.

[0035] The computer device mentioned in the embodiments of the present application may refer to a server or a terminal device, or may also be a system composed of a server and a terminal device, which is not limited in the embodiments of the present application. The terminal device may be an electronic device, including but not limited to a mobile phone, a tablet computer, a laptop computer, a vehicle-mounted device, etc. The server mentioned above may be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, vehicle-road collaboration, content delivery networks (Content Delivery Network, CDN), and big data and artificial intelligence platforms. When the server is an independent physical server, the server can independently process video data. When the server is multiple physical servers, multiple physical servers can collaborate to process video data.

[0036] For further information, see Figure 2 , Figure 2 is a flow chart of a video data processing method provided in an embodiment of the present application; Figure 2 As shown, the video data processing method can be applied to a computer device, and the video data processing method includes but is not limited to the following steps:

[0037] S101, obtaining video data to be processed and obtaining the heat level of the video data to be processed, and determining the processing order of the video data to be processed based on the heat level.

[0038] In an embodiment of the present application, the video data to be processed can be obtained from any terminal device or local storage. For example, the video data to be processed can be obtained from a terminal device, for example, when the terminal device triggers an upload event for the video data to be processed, the computer device detects the upload event for the video data to be processed, and thus obtains the video data to be processed. For example, when the terminal device detects a trigger operation for an upload component in the screen of the terminal device, the terminal device triggers an upload event for the video data to be processed, and then the video data to be processed is obtained. The video data to be processed may refer to a continuous sequence of images, and the video data to be processed may be obtained by recording various elements of nature such as people, animals, plants, and scenery by a video capture device, or may be obtained by synthesizing multiple video data or image data and sound data, which is not limited in the embodiment of the present application.

[0039] After obtaining the video data to be processed, the heat prediction of the video data to be processed can be performed to determine the heat level of the video data to be processed. Optionally, the heat level of the video data to be processed can be determined in combination with the heat characteristics of the video data to be processed. The heat characteristics can be used to reflect the heat of the video data to be processed, that is, the popularity of the video data. Specifically, the heat characteristics of the video data to be processed can be obtained, and the heat characteristics include at least one of the playback volume, the number of likes, the number of reposts, or the number of favorites of the video data to be processed; the heat level of the video data to be processed is determined based on the heat characteristics of the video data to be processed.

[0040] Among them, by obtaining the heat characteristics of the video data to be processed, such as the number of views, likes, reposts or favorites of the video data to be processed, the heat level of the video data to be processed can be determined by combining the number of views, likes, reposts or favorites of the video data to be processed. The larger the heat level, the higher the popularity of the video data to be processed. For example, if the heat level is greater than the level threshold, the corresponding video data to be processed can be a hot video; if the heat level is less than or equal to the level threshold, the corresponding video data to be processed is not a hot video. Hot videos can be used to reflect the popularity of videos.

[0041] Specifically, if the playback volume of the video data to be processed is greater than the playback threshold, the number of likes is greater than the like threshold, and the number of reposts is greater than the repost threshold, then the popularity of the video data to be processed is greater than the level threshold. If the playback volume of the video data to be processed is less than or equal to the playback threshold, the number of likes is less than or equal to the like threshold, and the number of reposts is less than or equal to the repost threshold, then the popularity of the video data to be processed is equal to or less than the level threshold.

[0042] In a specific implementation, the data such as the number of views, likes and reposts of the video data to be processed can be obtained within the target time period after the terminal device uploads the video data to be processed, and the heat level of the video data to be processed can be determined by combining the relationship between each heat feature and the corresponding threshold.

[0043] Optionally, the popularity level of the video data to be processed can also be determined in combination with data such as the account attention of the publishing account of the video data to be processed, which is not limited in the embodiments of the present application. The account attention of the publishing account of the video data to be processed can refer to the degree of attention paid to the account that published the video data to be processed. For example, the account attention can be determined based on the number of associated accounts of the publishing account, and the associated account can refer to an account that has a follow-up relationship with the publishing account. The more the number of associated accounts of the publishing account, the greater the account attention of the publishing account. The fewer the number of associated users of the publishing account, the smaller the account attention of the publishing account.

[0044] In the embodiment of the present application, by combining multiple characteristics of the video data to be processed, such as the number of views, the number of likes, the number of reposts, the number of favorites, and the account attention of the publishing account, a heat analysis is performed on the video data to be processed, and the heat level of the video data to be processed is determined. This allows for a multi-dimensional analysis of the heat of the video data to be processed, thereby improving the accuracy of heat level prediction.

[0045] Further, after determining the heat level of the video data to be processed, the processing order of the video data to be processed can be determined based on the heat level, and the heat level is positively correlated with the processing order of the video data to be processed. The greater the heat level, the higher the priority of the processing order of the video data to be processed. The smaller the heat level, the later the processing order of the video data to be processed.

[0046] For example, if the heat level is greater than the level threshold, the processing order of the video data to be processed is higher than the sequence threshold; if the heat level is less than or equal to the level threshold, the processing order of the video data to be processed is lower than the sequence threshold. As the heat level of the video data to be processed is higher, by setting a priority processing order, the video data to be processed can be processed as soon as possible, such as transcoding, and the transcoded video data can be sent to the terminal device later, which can reduce the bit rate of the video data, reduce bandwidth costs, and improve the efficiency of video data transmission. For video data with a lower heat level to be processed, by setting a later processing order, channel occupancy can be reduced, avoiding affecting the processing of video data with a higher heat level, thereby improving the efficiency of video data processing.

[0047] S102: Perform feature analysis on the video data to be processed to determine video features of the video data to be processed.

[0048] In the embodiment of the present application, since each video data to be processed contains different video parameters, the video features of each video data to be processed can be determined by performing feature analysis on each video data to be processed. The video feature is used to indicate at least one of the video quality information or video content information of the video data to be processed. In other words, by analyzing the video quality information and video content information of each video data to be processed, a video pre-processing algorithm, a video transcoding algorithm, and a video transcoding parameter corresponding to the video quality information and video content information of each video data can be selected subsequently, thereby achieving targeted video data processing and improving the effect of each video data to be processed.

[0049] Optionally, the video feature may include attribute features of the video data to be processed, and the attribute features of the video data to be processed may be obtained in the following manner: Specifically, the video clarity analysis may be performed on the video data to be processed to obtain the attribute features of the target video data, and the attribute features of the video data to be processed are used to reflect the resolution and bit rate of the video data to be processed.

[0050] Among them, the resolution of the video data to be processed can be used to reflect the clarity of the video data to be processed. The higher the resolution of the video data to be processed, the clearer the visual experience of the video picture. The lower the resolution of the video data to be processed, the blurrier the visual experience of the video picture. The bit rate of the video data to be processed can be used to reflect the number of bits of video data transmitted per unit time, that is, the larger the bit rate, the clearer the video picture. When the resolution is constant, the larger the bit rate, the clearer the video picture in the video data, and the smaller the bit rate, the blurrier the video picture in the video data. By analyzing the characteristics of the video data to be processed, such as the resolution and bit rate, the clarity of the video data to be processed can be determined, and the corresponding video data processing method can be selected according to the clarity of the video data to be processed, thereby realizing targeted video processing.

[0051] Specifically, for example, metadata analysis can be performed on the video data to be processed to obtain attribute characteristics of the video. Metadata can refer to data used to describe the video data to be processed, for example, it can be used to describe attribute information of the video data to be processed, such as the resolution, bit rate, frame rate, etc. of the video data to be processed.

[0052] In an optional implementation, since the video data to be processed includes metadata, when acquiring the video data to be processed, the metadata can be parsed to obtain attribute information such as resolution, bit rate, frame rate, etc. of the video data to be processed.

[0053] Optionally, the video feature may include a complexity feature of the video data to be processed, and the complexity feature of the video data to be processed may be obtained in the following manner. Specifically, complexity analysis is performed on the information domain of the video data to be processed to obtain the complexity feature of the target video data, and the complexity feature of the video data to be processed is used to reflect the inter-frame complexity and intra-frame complexity of the video data to be processed.

[0054] Among them, the information domain may include the time domain and the spatial domain. The inter-frame complexity feature of the video data to be processed may refer to the time domain information of the video data to be processed. The time domain information may be used to reflect the video smoothness of the video data to be processed. The inter-frame complexity may be for the entire video data to be processed. The time domain information may, for example, indicate whether the video data to be processed has features such as freezing, flickering, and jumping. The intra-frame complexity of the video data to be processed may refer to the spatial domain information of the video data to be processed. The spatial domain information may be used to reflect the picture clarity of the video data to be processed. The intra-frame complexity may be for a frame of video image. The spatial domain information may, for example, indicate whether the video data to be processed has features such as blur, noise, block effect, and wrong block.

[0055] For example, when there are features such as freezing, flickering, and jumping in the video data to be processed, it indicates that the inter-frame complexity of the video data to be processed is high. When there are no features such as freezing, flickering, and jumping in the video data to be processed, it indicates that the inter-frame complexity of the video data to be processed is low. When there are features such as blur, noise, block effect, and wrong block in the video data to be processed, it indicates that the intra-frame complexity of the video data to be processed is high. When there are no features such as blur, noise, block effect, and wrong block in the video data to be processed, it indicates that the intra-frame complexity of the video data to be processed is low. By analyzing the inter-frame complexity and intra-frame complexity of the video data to be processed, it is possible to determine which features of the video data to be processed that affect the complexity of the video, thereby determining the complexity features of the video data to be processed, and then subsequently selecting the corresponding video data processing method in combination with the complexity of the video data to be processed, thereby achieving targeted video processing. Specifically, for example, the video data to be processed can be subjected to temporal / spatial complexity analysis to determine the complexity features of the video data to be processed. Spatial complexity analysis can be used to determine the texture diversity and coarseness of the content contained in the video data to be processed. Temporal complexity analysis can be used to determine how fast or slow objects in the video data being processed are moving.

[0056] In an optional implementation, analyzing the temporal complexity of the video data to be processed to obtain the complexity characteristics of the target video data means: respectively detecting whether there are freezing, flickering, jumping and other characteristics in adjacent frames of the video data to be processed, obtaining the temporal detection result for the video data to be processed, and determining the temporal detection result as the complexity characteristics of the target video data. The temporal detection result is used to indicate whether there are freezing, flickering, jumping and other characteristics in adjacent frames of the video data to be processed.

[0057] In a possible implementation, a time domain detection model can be used to detect the time domain complexity of the video data to be processed, and determine whether there are features such as freezing, flickering, jumping, etc. in the adjacent frames of the video data to be processed. The time domain detection model can be, for example, a multi-classification model. Before using the time domain detection model, a large amount of sample video data can be pre-acquired to train the time domain detection model, so that the trained time domain detection model has the ability to detect whether there are features such as freezing, flickering, jumping, etc. in the adjacent frames of the video data to be processed. Then, the time domain detection model can be used for detection, and the model detection result is used as the complexity feature of the target video data. The time domain detection model can include, for example, but is not limited to, a deep neural network model (Deep Neural Networks, DNN), a convolutional neural network model (Convolutional Neural Network, CNN), a residual network model (ResNet), and the like.

[0058] In an optional implementation, analyzing the spatial complexity of the video data to be processed to obtain the complexity characteristics of the target video data means: respectively detecting whether there are blur, noise, block effect, wrong block and other characteristics in each frame of the video data to be processed, obtaining the spatial detection result for the video data to be processed, and determining the spatial detection result as the complexity characteristics of the target video data. The spatial detection result is used to indicate whether there are blur, noise, block effect, wrong block and other characteristics in each frame of the video data to be processed.

[0059] In a possible implementation, a spatial domain detection model can be used to detect the spatial domain complexity of the video data to be processed, and determine whether there are blur, noise, block effect, wrong block and other features in each frame of the video data to be processed. The spatial domain detection model can also be a multi-classification model, for example. Before using the spatial domain detection model, a large amount of sample video data can be obtained in advance to train the spatial domain detection model, so that the trained spatial domain detection model has the ability to detect whether there are blur, noise, block effect, wrong block and other features in each frame of the video data to be processed, and then the spatial domain detection model can be used for detection, and the model detection result is used as the complexity feature of the target video data. The spatial domain detection model can include, for example, but is not limited to, a deep neural network model (Deep Neural Networks, DNN), a convolutional neural network model (Convolutional Neural Network, CNN), a residual network model (ResNet), and the like. Since the sample labels of the sample video data are different when training the temporal domain detection model and the spatial domain detection model, the focus of the two models in the training process is different, and then different results can be output when the trained model is used for complexity detection.

[0060] In the embodiment of the present application, by performing time domain complexity analysis and spatial domain complexity analysis on the video data to be processed respectively, the classification accuracy of the video data can be improved, so as to facilitate subsequent targeted video processing.

[0061] Optionally, the video feature may include content features of the video data to be processed, and the content features of the video data to be processed may be obtained in the following manner: Specifically, the video elements of the video data to be processed are analyzed to obtain content features of the target video data, and the content features of the video data to be processed are used to reflect the video constituent elements and video quality content of the video data to be processed.

[0062] Among them, the video constituent elements of the video data to be processed may include elements such as the composition and color of the video data. The video quality content can be used to indicate whether the video data has features such as blur, jitter, and a large aperture. The video quality content may also include a video quality score, which may refer to a score for the clarity of the video data to be processed. The video quality score may be used to reflect the video clarity. The higher the video clarity, the higher the video quality score. The lower the video clarity, the lower the video quality score. By acquiring the video quality content and video constituent elements of the video data to be processed for analysis, the corresponding video data processing method may be selected in combination with the video quality content and video constituent elements of the video data to be processed, thereby achieving targeted video processing.

[0063] Specifically, for example, the video data to be processed can be analyzed in terms of basic features / aesthetic features / video quality assessment (VQA), so as to determine the video constituent elements and video quality content of the video data to be processed. Basic features are used to indicate whether there are features such as blur, jitter, and large aperture in the video data to be processed. Aesthetic feature analysis is used to reflect whether the composition, color, etc. of the video data to be processed conform to human aesthetics. Video quality assessment is used to determine the video quality score, that is, video quality assessment is used to reflect the clarity of the video quality.

[0064] In an optional implementation, the method of performing basic feature analysis on the video data to be processed can be as follows: based on the video quality assessment model, detect whether there are features such as blur, jitter, and large aperture in the video data to be processed, then determine which basic features the video data to be processed specifically includes based on the detection results of the video quality assessment model. Before using the video quality assessment model to detect the video data to be processed, the video quality assessment model can be pre-trained using a large amount of sample video data, so that the trained video quality assessment model has the ability to detect features such as blur, jitter, and large aperture of the video data to be processed, so that the trained video quality assessment model can detect the basic features in the video data to be processed. Optionally, the video quality assessment model includes, for example, but is not limited to, a convolutional neural network model (CNN), a recurrent neural network model (RNN), a residual network model (ResNet), and the like.

[0065] In an optional implementation, the method of analyzing the aesthetic features of the video data to be processed can be as follows: an aesthetic detection model is used to detect whether the composition, color, etc. of the video data to be processed conform to human aesthetics, and then the detection result of the aesthetic detection model can be used to determine which aesthetic features the video data to be processed specifically includes. Before using the aesthetic detection model to detect the video data to be processed, the aesthetic detection model can be trained in advance using a large amount of sample video data, so that the trained aesthetic detection model has the ability to detect whether the composition, color, etc. of the video data to be processed conform to human aesthetics, so that the aesthetic features in the video data to be processed can be detected using the trained aesthetic detection model. Optionally, the aesthetic detection model includes, for example, but is not limited to, a convolutional neural network model (CNN), a recurrent neural network model (RNN), a residual network model (ResNet), and the like. It can be understood that when training the aesthetic detection model, the sample label of the sample video data is different from the sample label of the training video quality assessment model, so when using the trained model, even if the same video data to be processed is input, the output results of each model are different.

[0066] In an optional implementation, the method of performing video quality assessment on the video data to be processed may be as follows: obtaining a video quality score uploaded by a video quality assessment terminal, where the video quality score may be obtained by evaluating the video quality of the video data to be processed through a video quality assessment device. For example, the video quality assessment terminal may evaluate the video quality of the video data to be processed according to a preset video quality reference standard to obtain a video quality score.

[0067] Optionally, the video feature may include a scene feature of the video data to be processed, and the scene feature of the video data to be processed may be obtained in the following manner: Specifically, scene analysis is performed on the video data to be processed to obtain the scene feature of the target video data, and the scene feature of the video data to be processed is used to reflect the scene category of the video data to be processed.

[0068] Among them, the scene category of the video data to be processed can be used to reflect the scene to which the video data to be processed belongs. The scene to which the video data to be processed belongs can be determined based on the vertical category label of the video data. For example, the vertical category label can include but is not limited to categories such as people, animals, scenery, and food. Further optionally, each category can also be subclassified. For example, the scenery category can also be subdivided into the sky, the sea, the desert, the grassland, and so on. Since the main content contained in the video data for different scene categories is different, when processing the video data, the corresponding video data processing method can be selected according to the content of the video data, thereby realizing targeted video processing.

[0069] Specifically, for example, the video data to be processed can be subjected to audio and video content understanding / content tag information analysis to obtain the scene features of the video. Audio and video content understanding is used to analyze the video scene elements in the video data to be processed, and content tag information analysis is used to analyze the scene to which the video data to be processed belongs.

[0070] In an optional implementation, the method for understanding the audio and video content of the video data to be processed can be as follows: a scene recognition model is used to perform scene recognition on the video data to be processed, and the video scene elements in the video data to be processed are determined. Before using the scene recognition model to perform scene recognition on the video data to be processed, a large amount of sample video data can be used to train the scene recognition model in advance, so that the trained scene recognition model has the ability of scene recognition, so that the trained scene recognition model can be used to identify the video scene elements in the video data to be processed. Optionally, the scene recognition model includes, but is not limited to, a deep neural network model (Deep Neural Networks, DNN), a convolutional neural network model (Convolutional Neural Network, CNN), a residual network model (ResNet), and the like. By identifying the video scene elements in the video data to be processed, the content in the video data to be processed can be further determined, and targeted processing can be performed later when the video is processed. It can be understood that when training the scene recognition model, the sample labels of the sample video data are different from the sample labels of the aesthetic detection model and the sample labels of the training video quality assessment model. Therefore, when using the trained model, even if the same video data to be processed is input, the output results of each model are different.

[0071] In an optional implementation, the method of performing content labeling information on the video data to be processed may be as follows: obtaining the vertical category label of the video data to be processed, and determining the category corresponding to the vertical category label of the video data to be processed as the content label information of the video data to be processed. Among them, the vertical category label of the video data to be processed may be pre-set. By combining the video scene elements and content label information in the video data to be processed, the scene category contained in the video data to be processed can be more accurately determined, thereby facilitating subsequent video processing.

[0072] Optionally, the video features may include two or more of the attribute features of the video data to be processed, the complexity features of the video data to be processed, the content features of the video data to be processed, and the scene features of the video data to be processed. Multiple features of these four features can be combined to determine the video features of the video data to be processed, so as to select the corresponding video data processing method to achieve targeted video processing. By selecting the video data processing method based on multiple video features, the video data can be analyzed from multiple dimensions, thereby improving the accuracy of video data processing.

[0073] S103, obtaining a video processing strategy corresponding to the video feature, and performing video processing on the video data to be processed using the video processing strategy according to a processing order to obtain target video data.

[0074] In the embodiment of the present application, since the video features of the video data to be processed are obtained, a video processing strategy corresponding to the video features can be selected, so that the video data to be processed is processed using the video processing strategy according to the processing order of the video data to be processed to obtain the target video data. The video processing strategy may include a video processing method and video processing parameters, the video processing method refers to a method or algorithm for processing video data, and the video data parameters may refer to parameters corresponding to the video processing method.

[0075] In one embodiment, the video processing strategy may include video transcoding parameters, and the video processing may include video transcoding processing, and the video transcoding parameters corresponding to the video features may be selected to perform video transcoding processing on the video data to be processed. For example, the video features of the video data to be processed include the attribute features of the video data to be processed, and the video transcoding parameters corresponding to the attribute features of the video data to be processed may be obtained. The video transcoding parameters may include transcoding resolution and transcoding bit rate, and the transcoding resolution and transcoding bit rate may be used in the processing order to perform video transcoding processing on the video data to be processed to obtain the target video data. By selecting the transcoding resolution and transcoding bit rate corresponding to the video data to be processed, for example, when the resolution and bit rate contained in the video data to be processed are low, a higher transcoding resolution and transcoding bit rate may be selected, so that the resolution of the transcoded video data is higher and the bit rate is higher, thereby increasing the clarity of the video data.

[0076] Among them, video transcoding processing can be used to encode video data to compress video data. By converting the bit rate of the video data to be processed in advance, the target video data is converted into transcoded video data with a smaller bit rate. As the actual playback volume of the video data to be processed increases, bandwidth can be saved during resource transmission, thereby reducing the communication overhead during resource transmission.

[0077] In one embodiment, the video features of the video data to be processed may include the complexity features of the video data to be processed, and the video transcoding parameters corresponding to the complexity features of the video data to be processed may be obtained. The video transcoding parameters may include the complexity between transcoding frames and the complexity within transcoding frames, and the video data to be processed may be subjected to video transcoding processing using the complexity between transcoding frames and the complexity within transcoding frames in a processing order to obtain target video data. By selecting the complexity between transcoding frames and the complexity within transcoding frames corresponding to the video data to be processed, targeted video data transcoding may be achieved, and the video data transcoding effect may be improved.

[0078] In one embodiment, the video features of the video data to be processed may include the scene features of the video data to be processed, and the video transcoding parameters corresponding to the scene features of the video data to be processed may be obtained. The video transcoding parameters may include scene transcoding parameters, and the video data to be processed may be subjected to video transcoding processing using the scene transcoding parameters in a processing order to obtain target video data. By selecting the scene transcoding parameters corresponding to the video data to be processed, targeted video data transcoding may be achieved, the scene information in the video data may be enhanced, and the video data transcoding effect may be improved.

[0079] In one embodiment, the video processing strategy may include video transcoding parameters, and the video processing may include video transcoding processing, and the video transcoding parameters corresponding to the video features may be selected to perform video transcoding processing on the video data to be processed. Specifically, the video transcoding parameters for the video data to be processed may be determined based on the attribute features of the video data to be processed, the complexity features of the video data to be processed, and the scene features of the video data to be processed; the video transcoding parameters are used to perform video transcoding processing on the video data to be processed in a processing order to obtain target video data.

[0080] Among them, the attribute characteristics of the video data to be processed are used to reflect the resolution and bit rate of the video data to be processed, the complexity characteristics of the video data to be processed are used to reflect the inter-frame complexity and intra-frame complexity of the video data to be processed, and the scene characteristics of the video data to be processed are used to reflect the scene category of the video data to be processed. Therefore, video transcoding parameters corresponding to these video characteristics can be selected to perform video transcoding processing on the video data to be processed, so as to obtain transcoded video data and realize targeted video data processing.

[0081] In one embodiment, based on the attribute characteristics of the video data to be processed, the complexity characteristics of the video data to be processed, and the scene characteristics of the video data to be processed, a method of determining the video transcoding parameters for the video data to be processed may include:

[0082] Based on the video resolution, bit rate, inter-frame complexity and intra-frame complexity of the video data to be processed, the encoding bit rate parameters and frame rate parameters of the video data to be processed are determined; based on the scene category of the video data to be processed, the scene encoding parameters are determined; the encoding bit rate parameters, frame rate parameters and scene encoding parameters are determined as video transcoding parameters of the video data to be processed.

[0083] Among them, the encoding bit rate parameter may refer to a bit rate value, the frame rate parameter may refer to a frame rate value, and the scene encoding parameter may include encoding parameters corresponding to the sky, encoding parameters corresponding to the sea, encoding parameters corresponding to the grassland, and so on. In a specific implementation, for example, the video transcoding parameters corresponding to each resolution and bit rate may be pre-set, the video transcoding parameters corresponding to each inter-frame complexity and intra-frame complexity may be set, and the video transcoding parameters corresponding to each scene category may be set. When the resolution, bit rate, inter-frame complexity, intra-frame complexity, and scene category of the video data to be processed are obtained, the matching video transcoding parameters may be selected from the preset video transcoding parameters. The video transcoding parameters may include transcoding resolution, transcoding bit rate, transcoding inter-frame complexity, transcoding intra-frame complexity, scene encoding parameters, and so on, so that after the video data to be processed is transcoded, the transcoded video data corresponding to the transcoding resolution, transcoding bit rate, transcoding inter-frame complexity, transcoding intra-frame complexity, scene encoding parameters, and so on may be obtained, thereby realizing targeted video data transcoding and improving the effect of transcoding video data. By transcoding the video data to be processed, the transcoded video data can be sent to the terminal device later, which can reduce the bit rate of the video data, reduce the bandwidth cost, and improve the efficiency of video data transmission.

[0084] Optionally, video transcoding parameters can be determined based on metadata information, time / spatial complexity information, audio and video content understanding / content tag information. The video transcoding parameter decision logic can be as follows: based on the video resolution, bit rate and time / spatial complexity information, the video encoding complexity can be determined, thereby determining the appropriate encoding bit rate, encoding frame rate, etc. For example, the higher the video resolution, the higher the bit rate and the higher the time / spatial complexity information, the higher the video encoding complexity, the higher the encoding bit rate and the higher the encoding frame rate. Furthermore, based on content understanding and content tag information, the scene information of the video data can be determined, and based on the scene information, the scene encoding parameters suitable for the scene can be output.

[0085] In the embodiment of the present application, video transcoding may refer to decoding the video data to be processed and then encoding it. Since the video decoding method is generally unified, but the video encoding can be encoded according to different video situations, the video transcoding method, such as the video encoding method, can be determined in combination with the features contained in the video data, so as to achieve targeted video processing. Optionally, the video transcoding method may include but is not limited to scene adaptive encoding and region of interest (ROI) encoding. Scene adaptive encoding may refer to encoding in combination with the scene to which the video data to be processed belongs, such as selecting the scene encoding parameters corresponding to the video data to be processed, and using the corresponding scene encoding parameters for video encoding processing. Region of interest encoding may refer to video encoding processing of the area to be processed extracted from the video data to be processed. The region of interest can be extracted from the video data to be processed, for example, the area containing characters, scenery or other elements in the video data to be processed can be used as the region of interest. By using the scene adaptive encoding method, the scene information in the video data to be processed can be enhanced and the video effect can be improved.

[0086] In the embodiment of the present application, since the processing order corresponding to each video data to be processed is determined based on its heat level, for the video data to be processed with a higher processing order, the corresponding video transcoding parameters can be determined first and video transcoding can be performed, so that the video data to be processed with a higher heat level can be processed as soon as possible, and the transcoded video data can be pushed to improve the video transmission efficiency. For the video data to be processed with a lower processing order, the corresponding video transcoding parameters can be determined later and video transcoding can be performed to avoid occupying video transcoding resources. Since each video data to be processed has a corresponding processing order, the efficiency of video transcoding can be improved by performing video transcoding on the video data to be processed according to the processing order.

[0087] In one implementation, the video processing strategy of the video data to be processed may also include a video enhancement algorithm and video enhancement parameters, and the video processing may also include video enhancement processing, which may be used to enhance the image content or image information in the video data to improve the clarity of the video image. For example, the video enhancement processing may be performed on the video data to be processed in the following manner: based on the complexity characteristics of the video data to be processed and the content characteristics of the video data to be processed, the video enhancement algorithm and video enhancement parameters for the video data to be processed are determined; based on the attribute characteristics of the video data to be processed, the complexity characteristics of the video data to be processed and the scene characteristics of the video data to be processed, the video transcoding parameters for the video data to be processed are determined; according to the processing order, the video enhancement algorithm and the video enhancement parameters are used to perform video enhancement processing on the video data to be processed to obtain the video data to be transcoded; based on the video transcoding parameters, the video transcoding processing is performed on the video data to be transcoded to obtain the target video data.

[0088] Among them, the video data to be transcoded refers to the video data obtained after the video data to be processed is subjected to video enhancement processing. For example, the video enhancement parameters and video enhancement algorithms corresponding to each inter-frame complexity and intra-frame complexity can be pre-set. The inter-frame complexity and intra-frame complexity can be used to indicate whether the video data to be processed requires frame insertion, super-resolution and other processing. Then, the video enhancement parameters and video enhancement algorithms corresponding to super-resolution can be set, as well as the video enhancement parameters and video enhancement algorithms corresponding to frame insertion, and the video enhancement parameters and video enhancement algorithms corresponding to the video quality content such as blur, jitter, block effect, noise, etc. can be set. Therefore, when the video data to be processed is obtained, the video enhancement algorithm and video enhancement parameters can be determined from the preset video enhancement parameters and video enhancement algorithms according to the complexity characteristics of the video data to be processed, such as the inter-frame complexity and the intra-frame complexity. For example, for the video data to be processed with higher complexity, a high-complexity video enhancement algorithm and corresponding video enhancement parameters can be selected for video enhancement processing to improve the video quality of the video data to be processed. If the video data to be processed has video quality content such as blur, jitter, block effect, noise, etc., you can select corresponding video enhancement algorithms and video enhancement parameters such as deblurring, de-jittering, deblocking, noise reduction, etc. to perform video enhancement processing, thereby achieving targeted video enhancement processing and improving video quality.

[0089] Optionally, the video data to be processed can also be subjected to video enhancement processing in the following manners: based on the attribute characteristics of the video data to be processed, the complexity characteristics of the video data to be processed, and the content characteristics of the video data to be processed, determine the video enhancement algorithm and video enhancement parameters for the video data to be processed; based on the attribute characteristics of the video data to be processed, the complexity characteristics of the video data to be processed, and the scene characteristics of the video data to be processed, determine the video transcoding parameters for the video data to be processed; according to the processing order, use the video enhancement algorithm and video enhancement parameters to perform video enhancement processing on the video data to be processed to obtain the video data to be transcoded; based on the video transcoding parameters, perform video transcoding processing on the video data to be transcoded to obtain the target video data. By further determining the video enhancement algorithm and video enhancement parameters for the video data to be processed in combination with the attribute characteristics of the video data to be processed, super-resolution can be performed in the video data to be processed to improve the image resolution.

[0090] Optionally, for example, the time domain / spatial domain complexity, basic features / aesthetic features / video quality assessment of the video data to be processed can be performed to decide the video enhancement algorithm and parameters. For example, the enhancement algorithm decision logic can be as follows: According to the basic features / aesthetic features / video quality assessment, the problems with the basic image quality of the video data can be analyzed, so as to select one or more suitable enhancement algorithms. According to the time domain complexity, spatial domain complexity and Metadata information, it can be determined whether the video needs to be processed by super-resolution, interpolation, etc. In the embodiment of the present application, the video pre-processing algorithm may refer to a video enhancement algorithm, and the video pre-processing algorithm may include but is not limited to sharpening, deblurring, removing block effects, super-resolution, de-jittering, noise reduction and other algorithms. By judging whether there are problems of blur, block effects, jitter, and noise in the video data to be processed, the corresponding data enhancement algorithm can be selected for processing, thereby achieving the effects of deblurring, removing block effects, removing jitter, and removing noise of the video data to be processed, thereby improving the quality of the video data.

[0091] In the embodiment of the present application, by performing video enhancement processing on the video data to be processed to obtain the video data to be transcoded, the image quality of the video data to be processed can be improved, and the quality of the video data can be improved. Furthermore, by performing video transcoding processing on the video data after the video enhancement processing, the bit rate of the video data can be reduced, and the subsequent sending of the transcoded video data to the terminal device can reduce the bandwidth cost and improve the efficiency of video data transmission.

[0092] The following is an example of how to perform feature analysis on the video data to be processed and how to perform video processing based on the video features in the target scenario:

[0093] Obtain the video data to be processed in the target scenario and the heat level of the video data to be processed. For example, if the heat level of the video data to be processed is greater than the level threshold, the video data to be processed can be feature analyzed and processed, and the processed target video data can be pushed to the account in the video playback platform. The target scenario can include but is not limited to the live broadcast scenario, the on-demand scenario, or any scenario that requires video transcoding of the video data.

[0094] Specifically, for example, the video clarity analysis is performed on the video data to be processed to obtain the attribute characteristics of the video data to be processed. For example, the attribute characteristics of the video data to be processed include that the resolution of the video data to be processed is the first resolution and the bit rate of the video data to be processed is the first bit rate. Among them, the first resolution is lower than the resolution threshold, and the first bit rate is lower than the bit rate threshold. The complexity analysis is performed on the information domain of the video data to be processed to obtain the complexity characteristics of the video data to be processed. For example, the complexity characteristics of the video data to be processed include that the inter-frame complexity of the video data to be processed is the first inter-frame complexity and the intra-frame complexity of the video data to be processed is the first intra-frame complexity. Among them, the first inter-frame complexity, the first intra-frame complexity, the first resolution and the first bit rate can be used together to indicate that the video data to be processed needs to be super-resolution and interpolated. The video elements of the video data to be processed are analyzed to obtain the content characteristics of the video data to be processed. For example, the content characteristics of the video data to be processed include that the video constituent elements of the video data to be processed include the sky, and the video quality content of the video data to be processed includes blur, jitter and a first image quality score, and the first image quality score is lower than the score threshold. The scene analysis is performed on the video data to be processed to obtain the scene characteristics of the video data to be processed. For example, the scene feature of the video data to be processed is used to reflect that the scene category of the video data to be processed is a landscape category.

[0095] Furthermore, when performing video transcoding processing on the video data to be processed, for example, a first encoding bit rate parameter value and a first frame rate parameter value matching the first resolution, the first bit rate, the first inter-frame complexity, and the first intra-frame complexity can be obtained from the preset video transcoding parameters; the encoding parameter value corresponding to the sky can be obtained from the preset video transcoding parameters, and the first encoding bit rate parameter value, the first frame rate parameter value, and the encoding parameter value corresponding to the sky can be used to perform video transcoding processing on the video data to be processed, so as to obtain the target video data. Furthermore, the target video data can be pushed to an account in the video playback platform so that the account corresponding to the account can view the target video data and perform subsequent operations.

[0096] In the embodiment of the present application, by performing video transcoding on the video data to be processed, the bit rate of the video data can be reduced, the bandwidth cost can be reduced, and the efficiency of video data transmission can be improved.

[0097] In an optional implementation, before transcoding the video data to be processed, the video data to be processed may be first subjected to video enhancement processing. Specifically, the video data to be processed is subjected to video enhancement processing. Since the video quality content of the video data to be processed includes blur, jitter, and the first image quality score can reflect the problems existing in the basic image quality of the video data to be processed, a deblurring algorithm and a dejittering algorithm, as well as video enhancement parameters corresponding to the deblurring algorithm and video enhancement parameters corresponding to the dejittering algorithm may be selected to process the video image to be processed, and a video enhancement algorithm and video enhancement parameters for super-resolution and interpolation are selected to process the video image to be processed, so as to obtain the video data to be transcoded.

[0098] Furthermore, when performing video transcoding processing on the video data to be processed, the video data to be transcoded obtained after the video enhancement processing can be directly transcoded. For example, the first encoding bit rate parameter value and the first frame rate parameter value matching the first resolution, the first bit rate, the first inter-frame complexity, and the first intra-frame complexity can be obtained from the preset video transcoding parameters; the encoding parameter value corresponding to the sky can be obtained from the preset video transcoding parameters, and the first encoding bit rate parameter value, the first frame rate parameter value, and the encoding parameter value corresponding to the sky are used to perform video transcoding processing on the video data to be transcoded to obtain the target video data. Furthermore, the target video data can be pushed to an account in the video playback platform so that the user corresponding to the account can view the target video data and perform subsequent operations.

[0099] In the embodiment of the present application, by performing video enhancement processing on the video data to be processed, the image display effect of the video data to be processed can be increased, so that the video playback is smoother and the user experience is improved.

[0100] In the embodiment of the present application, any video data to be processed can be uploaded. By obtaining the heat characteristics of the uploaded video data to be processed, such as the number of views, the number of likes, the number of reposts or the number of favorites, the heat analysis of the video data to be processed can be performed to determine the heat level of the video data to be processed, and the processing order of the video data to be processed can be determined based on the heat level, so that video transcoding can be triggered based on the heat of the video data to be processed. After triggering video transcoding based on heat, feature analysis (such as Metadata analysis, time domain / spatial domain complexity analysis, basic features / aesthetic features analysis / video quality assessment and audio and video content understanding / content label information cover analysis) can be performed on the video data to be processed to obtain video features, so as to make algorithm decisions based on the video features, such as determining the type of video enhancement algorithm and enhancement parameters, and using the type of video enhancement algorithm and enhancement parameters to perform video enhancement processing on the video data to be processed. Further, the video transcoding parameters are determined based on the video features, and the video transcoding parameters are used to perform video transcoding processing on the video data after video enhancement processing to obtain the target video data.

[0101] In one possible implementation, if the heat level of the video data to be processed is greater than the level threshold, the video data to be processed can be considered to be high heat level video data, and the corresponding video processing workflow can be triggered. The video processing workflow corresponding to the high heat level can be configured with more high-complexity video pre-processing algorithms. For example, high heat level video data can be configured with super-resolution, deblurring, de-jittering, block effect removal, noise reduction, color enhancement, face enhancement, interpolation and other image quality enhancement and repair video pre-processing algorithms, thereby improving the quality of video data. Furthermore, the video processing workflow corresponding to the high heat level can be configured with a high transcoding gear to achieve fast video transcoding.

[0102] In the embodiment of the present application, a heat analysis is performed on any video data to be processed to determine the processing order and video processing strategy corresponding to the video data to be processed, and then the corresponding video processing strategy is adopted to perform video processing on the video data to be processed to obtain the target video data. For other video data among the multiple video data to be processed, the processing method for the video data to be processed can be referred to, so as to obtain the video processing method corresponding to each video data to be processed, so as to perform targeted video processing on each video data to be processed. The embodiment of the present application does not describe this in detail.

[0103] In the embodiment of the present application, by making full use of the video heat analysis service, that is, using the video heat analysis service to perform heat grading on the video data, the video heat and the video processing algorithm are associated. The higher the heat level of the video data, the more chances there are to select a high-complexity video processing algorithm when making algorithm decisions, thereby balancing computing resources and video processing effects. By analyzing the video data to be processed from multiple dimensions, that is, all-round video feature analysis, we can have a more comprehensive understanding of video quality, video complexity, video transcoding and other aspects, so that the processing of video data is more targeted. In addition, by using a high-complexity algorithm to process video data with a higher heat level in combination with its own video data situation, the video quality can be improved. When the processed video data is subsequently played, the video quality is higher and the video effect is better, which can improve the user experience.

[0104] In the embodiment of the present application, by obtaining the heat level of the video data to be processed, determining the processing order of the video data to be processed based on the heat level, determining the processing order by using the heat level of the video data to be processed, and performing video processing in combination with the processing order, the video processing efficiency can be improved. By performing feature analysis on the video data to be processed, the video features of the video quality information and video content information of the video data to be processed can be determined; and then when selecting the video processing strategy corresponding to the video data to be processed, the strategy selection can be combined with the specific video features of the video data, and when the video data to be processed is subsequently processed, targeted video processing can be achieved to improve the accuracy of data processing. Since the video features of the video data to be processed are analyzed from multiple dimensions, the obtained video processing strategy is more targeted, the video processing strategy is also more accurate, and the video processing is performed according to the features contained in the video data. Compared with the use of a unified video processing method for video processing, the amount of data calculation can be reduced, and the video processing efficiency can be improved.

[0105] For further information, see Figure 3a , Figure 3a is a flow chart of another video data processing method provided in an embodiment of the present application; Figure 3a As shown, the video data processing method can be applied to a computer device, and the video data processing method includes but is not limited to the following steps:

[0106] S201, obtaining video data to be processed and obtaining heat levels of the video data to be processed, and determining a processing order of the video data to be processed based on the heat levels.

[0107] S202: Perform feature analysis on the video data to be processed to determine video features of the video data to be processed.

[0108] S203, obtaining a video processing strategy corresponding to the video feature, and performing video processing on the video data to be processed using the video processing strategy according to the processing order to obtain target video data.

[0109] In the embodiment of the present application, the specific implementation of steps S201 to S203 can refer to the implementation of the above-mentioned steps S101 to S103, which will not be repeated here.

[0110] S204: Obtain a video push strategy for target video data.

[0111] In the embodiment of the present application, by performing video processing such as video enhancement processing and video transcoding processing on the video data to be processed, the video effect can be enhanced, the video quality can be improved, and the bit rate of the video data can be reduced, so that the transcoded video data, i.e., the target video data, can be pushed to the terminal device, and the video data transmission efficiency can be improved. When playing the target video data, a high-quality video effect can be presented.

[0112] After obtaining the target video data, the video push strategy for the target video data can be obtained, so as to push the video in combination with the video push strategy. The video push strategy may include a preset push strategy and a tag push strategy. The preset push strategy may refer to pushing videos to all accounts or randomly pushing videos. The account may refer to a user account registered on the video playback platform. By logging into the account, video data can be viewed, forwarded, uploaded, etc. on the video playback platform, thereby enabling data interaction on the video playback platform. The tag push strategy may refer to targeted video push in combination with the account tags associated with the account. Account tags can be used to reflect the interest tags of users associated with the account, such as the video data categories that the user likes and the video data categories that the user does not like, etc.

[0113] S205: If the video push strategy is a preset push strategy, the target video data is pushed to all accounts in the video playback platform.

[0114] Among them, by pushing the target video data to all accounts on the video playback platform, all users associated with the account can click to play the target video data. Compared with playing the video data to be processed, the image effect in the target video data is better and the video quality is higher, thereby improving the user experience.

[0115] S206, if the video push strategy is a tag push strategy, determine the account tags associated with all accounts in the video playback platform, determine the account tags that match the video tags of the video data to be processed, and push the target video data to the accounts associated with the matching account tags.

[0116] The video tags of the video data to be processed may include but are not limited to the video tags selected by the video uploading account, or the video tags obtained by content recognition of the video data to be processed. The video tags may include but are not limited to food, scenery, film and television, animals, makeup and other tags. By obtaining the video tags of the video data to be processed and the account tags associated with all accounts on the video playback platform, account tags that match the video tags of the video data to be processed can be screened out from all accounts on the video playback platform, so as to push targeted videos to the accounts associated with the matching account tags, avoid pushing video data that the account has marked as disliked, and push the account's favorite video data, thereby improving the user experience.

[0117] It is understandable that the collection, use and processing of data such as account tags, account attention, account numbers, etc. involved in the embodiments of the present application require the permission of relevant users and comply with relevant laws, regulations and standards.

[0118] In the embodiment of the present application, by obtaining the heat level of the video data to be processed, determining the processing order of the video data to be processed based on the heat level, determining the processing order by using the heat level of the video data to be processed, and performing video processing in combination with the processing order, the video processing efficiency can be improved. By performing feature analysis on the video data to be processed, the video features of the video quality information and video content information of the video data to be processed can be determined; and then when selecting the video processing strategy corresponding to the video data to be processed, the strategy selection can be combined with the specific video features of the video data, and when the video data to be processed is subsequently processed, targeted video processing can be achieved to improve the accuracy of data processing. Since the video features of the video data to be processed are analyzed from multiple dimensions, the obtained video processing strategy is more targeted, the video processing strategy is also more accurate, and the video processing is performed according to the features contained in the video data. Compared with the use of a unified video processing method for video processing, the amount of data calculation can be reduced, and the video processing efficiency can be improved. Further, by combining the video push strategy to push the target video data, the accuracy of the video push can be improved.

[0119] For further information, see Figure 3b , Figure 3b is a flowchart of another video data processing method provided in an embodiment of the present application; Figure 3b As shown, the video data processing method can be applied to a computer device, and the video data processing method includes but is not limited to the following steps:

[0120] S301, in response to the to-be-processed video data having a heat level greater than a level threshold, performing feature analysis on the to-be-processed video data to determine video features of the to-be-processed video data.

[0121] In the embodiment of the present application, when the computer device obtains the to-be-processed video data whose heat level is greater than the level threshold, the computer device can perform feature analysis on the to-be-processed video data in response to the to-be-processed video data whose heat level is greater than the level threshold to determine the video features of the to-be-processed video data. The video features are used to indicate at least one of the video quality information or the video content information of the to-be-processed video data.

[0122] In one implementation, a computer device may obtain the to-be-processed video data whose heat level is greater than a level threshold value and sent by a heat judgment terminal, thereby performing feature analysis on the to-be-processed video data to determine the video features of the to-be-processed video data. The heat judgment terminal may, for example, refer to a terminal device for judging the heat level of the to-be-processed video data. The heat judgment terminal judges the heat of the to-be-processed video data. When it is determined that the heat of the to-be-processed video data is greater than the level threshold value, it indicates that the to-be-processed video data may be a popular video. The to-be-processed video data may then be uploaded to the computer device for feature analysis, and targeted video processing may be performed in combination with the video features of the to-be-processed video data. By performing targeted video processing on popular videos, the video quality may be improved, thereby improving the user experience.

[0123] Optionally, the heat determination terminal determines the heat of the video data to be processed to obtain a video determination result, and the video determination result is used to indicate whether the heat of the video data to be processed is greater than the level threshold. Furthermore, the heat determination terminal can also upload the video data to be processed and the video determination result to a computer device, and when the computer device receives the video data to be processed and the video determination result, it can determine whether to perform feature analysis on the video data to be processed based on the video determination result.

[0124] In the embodiment of the present application, by determining whether the video data to be processed is hot data and obtaining the video determination result, the computer device can further determine whether it is necessary to perform feature analysis on the video data to be processed. For example, some video data to be processed is not hot data, but the computer device can specify feature analysis on it, thereby realizing targeted video processing in combination with the features of the video. For example, video data released by accounts used by experts in certain important fields, etc.

[0125] In another implementation, the computer device may obtain the video data to be processed, determine the heat level of the video data to be processed, and if the heat level of the video data to be processed is greater than the level threshold, then in response to the video data to be processed whose heat level is greater than the level threshold, perform feature analysis on the video data to be processed to determine the video features of the video data to be processed. By performing feature analysis and targeted video processing on video data with a higher heat level, the video quality can be improved, thereby improving the user experience.

[0126] Optionally, if the to-be-processed video data whose heat level is less than or equal to the level threshold is obtained, the computer device may not perform feature analysis on the to-be-processed video data to determine the video features of the to-be-processed video data. For example, a unified video processing method may be used to process the video data at this time, without the need to perform targeted processing in combination with the video features of the to-be-processed video data, thereby reducing the amount of data processing.

[0127] In the embodiment of the present application, the method of performing feature analysis on the video data to be processed to determine the video features of the video data to be processed can refer to the specific implementation method of the above-mentioned step S102, which will not be repeated here.

[0128] S302, obtaining a video processing strategy corresponding to the video feature, and performing video processing on the video data to be processed using the video processing strategy to obtain target video data.

[0129] In the embodiment of the present application, since different video processing strategies can be set for different video features, targeted video processing can be implemented for the video data to be processed, thereby improving the video display effect. Among them, the video processing strategy includes at least one of video enhancement processing and video transcoding processing. That is to say, when the heat level of the video data to be processed is greater than the level threshold, the video data to be processed can be processed by video enhancement processing, or the video data to be processed can be processed by video transcoding processing, or the video data to be processed can be processed by video enhancement processing and video transcoding processing. Which specific processing method is used for processing can be determined in combination with the video features of the video data to be processed.

[0130] In the embodiment of the present application, the manner of obtaining the video processing strategy corresponding to the video feature and adopting the video processing strategy to perform video processing on the video data to be processed can refer to the specific implementation manner of the above-mentioned step S103, which will not be repeated here.

[0131] In the embodiment of the present application, by responding to the to-be-processed video data whose heat level is greater than the level threshold, feature analysis is performed on the to-be-processed video data to determine the video features of the to-be-processed video data, and then targeted video processing is performed on the to-be-processed video data in combination with the video processing strategy corresponding to the video features, thereby improving the video quality and enhancing the display effect of the video data. Since feature analysis does not need to be performed on all the to-be-processed video data, the video processing efficiency can be improved.

[0132] The method of the embodiment of the present application is introduced above, and the device of the embodiment of the present application is introduced below.

[0133] See also Figure 4a , Figure 4a: is a schematic diagram of the composition structure of a video data processing device provided in an embodiment of the present application. The above-mentioned video data processing device can be deployed on a computer device; the video data processing device can be used to execute the corresponding steps in the video data processing method provided in an embodiment of the present application. The video data processing device 40 includes:

[0134] The video acquisition unit 401 is used to acquire the video data to be processed, and acquire the heat level of the video data to be processed, and determine the processing order of the video data to be processed based on the heat level;

[0135] A video analysis unit 402, configured to perform feature analysis on the video data to be processed to determine a video feature of the video data to be processed, wherein the video feature is used to indicate at least one of video quality information or video content information of the video data to be processed;

[0136] The video processing unit 403 is used to obtain the video processing strategy corresponding to the video feature, and use the video processing strategy to perform video processing on the video data to be processed according to the processing order to obtain target video data; the video processing strategy includes video transcoding parameters, and the video processing includes transcoding processing.

[0137] Optionally, the video acquisition unit 401 is specifically used to:

[0138] Acquire a popularity feature of the video data to be processed, where the popularity feature includes at least one of a playback volume, a like volume, a forwarding volume, or a collection volume of the video data to be processed;

[0139] The heat level of the video data to be processed is determined based on the heat characteristics of the video data to be processed, and the heat level is positively correlated with the processing order of the video data to be processed.

[0140] Optionally, the video features include attribute features of the video data to be processed, complexity features of the video data to be processed, content features of the video data to be processed, and scene features of the video data to be processed; the video analysis unit 402 is specifically used to:

[0141] Performing video clarity analysis on the video data to be processed to obtain attribute characteristics of the video data to be processed, where the attribute characteristics of the video data to be processed are used to reflect the resolution and bit rate of the video data to be processed;

[0142] Performing complexity analysis on the information domain of the video data to be processed to obtain complexity characteristics of the video data to be processed, where the complexity characteristics of the video data to be processed are used to reflect the inter-frame complexity and intra-frame complexity of the video data to be processed;

[0143] Analyzing the video elements of the video data to be processed to obtain content features of the video data to be processed, where the content features of the video data to be processed are used to reflect the video constituent elements and video quality content of the video data to be processed;

[0144] A scene analysis is performed on the video data to be processed to obtain scene features of the video data to be processed, where the scene features of the video data to be processed are used to reflect the scene category of the video data to be processed.

[0145] Optionally, the video processing unit 403 is specifically configured to:

[0146] Determining video transcoding parameters for the video data to be processed based on the attribute characteristics of the video data to be processed, the complexity characteristics of the video data to be processed, and the scene characteristics of the video data to be processed;

[0147] The video data to be processed is subjected to video transcoding processing using the video transcoding parameters in the processing sequence to obtain target video data.

[0148] Optionally, the video processing unit 403 is specifically configured to:

[0149] Determine encoding bit rate parameters and frame rate parameters of the video data to be processed based on the video resolution and bit rate of the video data to be processed, and the inter-frame complexity and intra-frame complexity of the video data to be processed;

[0150] Determining a scene encoding parameter based on the scene category of the video data to be processed;

[0151] The encoding bit rate parameter, the frame rate parameter and the scene encoding parameter are determined as video transcoding parameters of the video data to be processed.

[0152] Optionally, the video processing strategy further includes a video enhancement algorithm and video enhancement parameters, and the video processing further includes video enhancement processing; the video processing unit 403 is specifically used to:

[0153] Determining a video enhancement algorithm and video enhancement parameters for the video data to be processed based on the complexity characteristics of the video data to be processed and the content characteristics of the video data to be processed;

[0154] Determining video transcoding parameters for the video data to be processed based on the attribute characteristics of the video data to be processed, the complexity characteristics of the video data to be processed, and the scene characteristics of the video data to be processed;

[0155] Performing video enhancement processing on the video data to be processed by using the video enhancement algorithm and the video enhancement parameters according to the processing sequence to obtain video data to be transcoded;

[0156] The video data to be transcoded is subjected to video transcoding processing based on the video transcoding parameters to obtain target video data.

[0157] Optionally, the video data processing device 40 further includes a video pushing unit 404, and the video pushing unit 404 is used to:

[0158] Obtain a video push strategy for the target video data, the video push strategy including a preset push strategy and a tag push strategy;

[0159] If the video push strategy is the preset push strategy, the target video data is pushed to all accounts in the video playback platform;

[0160] If the video push strategy is a tag push strategy, the account tags associated with all accounts in the video playback platform are determined, the account tags that match the video tags of the video data to be processed are determined, and the target video data is pushed to the accounts associated with the matching account tags.

[0161] It should be noted that Figure 4a For the contents not mentioned in the corresponding embodiments, please refer to the description of the method embodiments, which will not be repeated here.

[0162] In the embodiment of the present application, by obtaining the heat level of the video data to be processed, determining the processing order of the video data to be processed based on the heat level, determining the processing order by using the heat level of the video data to be processed, and performing video processing in combination with the processing order, the video processing efficiency can be improved. By performing feature analysis on the video data to be processed, the video features of the video quality information and video content information of the video data to be processed can be determined; and then when selecting the video processing strategy corresponding to the video data to be processed, the strategy selection can be combined with the specific video features of the video data, and when the video data to be processed is subsequently processed, targeted video processing can be achieved to improve the accuracy of data processing. Since the video features of the video data to be processed are analyzed from multiple dimensions, the obtained video processing strategy is more targeted, the video processing strategy is also more accurate, and the video processing is performed according to the features contained in the video data. Compared with the use of a unified video processing method for video processing, the amount of data calculation can be reduced, and the video processing efficiency can be improved.

[0163] See also Figure 4b , Figure 4b 4 is a schematic diagram of the composition structure of another video data processing device provided in an embodiment of the present application. The above-mentioned video data processing device can be deployed on a computer device; the video data processing device can be used to execute the corresponding steps in the video data processing method provided in an embodiment of the present application. The video data processing device 41 includes:

[0164] The video response unit 405 is used to perform feature analysis on the video data to be processed in response to the video data to be processed whose heat level is greater than the level threshold, so as to determine the video features of the video data to be processed; the video features are used to indicate at least one of the video quality information or the video content information of the video data to be processed;

[0165] The strategy acquisition unit 406 is used to acquire the video processing strategy corresponding to the video feature, and use the video processing strategy to perform video processing on the video data to be processed to obtain target video data; the video processing strategy includes at least one of video enhancement processing and video transcoding processing.

[0166] It should be noted that Figure 4b For the contents not mentioned in the corresponding embodiments, please refer to the description of the method embodiments, which will not be repeated here.

[0167] In the embodiment of the present application, by responding to the to-be-processed video data whose heat level is greater than the level threshold, feature analysis is performed on the to-be-processed video data to determine the video features of the to-be-processed video data, and then targeted video processing is performed on the to-be-processed video data in combination with the video processing strategy corresponding to the video features, thereby improving the video quality and enhancing the display effect of the video data. Since feature analysis does not need to be performed on all the to-be-processed video data, the video processing efficiency can be improved.

[0168] See also Figure 5 , Figure 5 Schematic diagram of the structure of a computer device provided in an embodiment of the present application. Figure 5 As shown, the above-mentioned computer device 50 may include: a processor 501, a network interface 504 and a memory 505. In addition, the above-mentioned computer device 50 may also include: a user interface 503, and at least one communication bus 502. Among them, the communication bus 502 is used to realize the connection and communication between these components. Among them, the user interface 503 may include a display screen (Display), a keyboard (Keyboard), and the optional user interface 503 may also include a standard wired interface and a wireless interface. The network interface 504 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface). The memory 505 may be a high-speed RAM memory, or it may be a non-volatile memory (non-volatile memory), such as at least one disk storage. The memory 505 may optionally also be at least one storage device located away from the aforementioned processor 501. As Figure 5 As shown, the memory 505 as a computer-readable storage medium may include an operating system, a network communication module, a user interface module, and a device control application program.

[0169] exist Figure 5In the computer device 50 shown, the network interface 504 can provide a network communication function; the user interface 503 is mainly used to provide an input interface for the user; and the processor 501 can be used to call the device control application stored in the memory 505 to achieve:

[0170] Obtaining video data to be processed, and obtaining a heat level of the video data to be processed, and determining a processing order of the video data to be processed based on the heat level;

[0171] Performing feature analysis on the video data to be processed to determine a video feature of the video data to be processed, where the video feature is used to indicate at least one of video quality information or video content information of the video data to be processed;

[0172] A video processing strategy corresponding to the video feature is obtained, and the video processing strategy is used to perform video processing on the video data to be processed according to the processing order to obtain target video data; the video processing strategy includes video transcoding parameters, and the video processing includes video transcoding processing.

[0173] It should be understood that the computer device 50 described in the embodiment of the present application can execute the above Figure 2 , Figure 3a and Figure 3b The description of the video data processing method in the corresponding embodiment can also be performed as described above. Figure 4a and Figure 4b The description of the video data processing device in the corresponding embodiment will not be repeated here. In addition, the description of the beneficial effects of the same method will not be repeated here either.

[0174] In the embodiment of the present application, by obtaining the heat level of the video data to be processed, determining the processing order of the video data to be processed based on the heat level, determining the processing order by using the heat level of the video data to be processed, and performing video processing in combination with the processing order, the video processing efficiency can be improved. By performing feature analysis on the video data to be processed, the video features of the video quality information and video content information of the video data to be processed can be determined; and then when selecting the video processing strategy corresponding to the video data to be processed, the strategy selection can be combined with the specific video features of the video data, and when the video data to be processed is subsequently processed, targeted video processing can be achieved to improve the accuracy of data processing. Since the video features of the video data to be processed are analyzed from multiple dimensions, the obtained video processing strategy is more targeted, the video processing strategy is also more accurate, and the video processing is performed according to the features contained in the video data. Compared with the use of a unified video processing method for video processing, the amount of data calculation can be reduced, and the video processing efficiency can be improved.

[0175] The embodiment of the present application also provides a computer-readable storage medium, which stores a computer program, which includes program instructions, which, when executed by a computer, causes the computer to execute the method of the aforementioned embodiment, and the computer may be part of the computer device mentioned above. For example, the processor 501 mentioned above. As an example, the program instructions may be deployed on a computer device for execution, or deployed on multiple computer devices located at one location for execution, or, executed on multiple computer devices distributed at multiple locations and interconnected by a communication network, and multiple computer devices distributed at multiple locations and interconnected by a communication network may constitute a blockchain network.

[0176] Those skilled in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above methods. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0177] The above disclosure is only the preferred embodiment of the present application, which certainly cannot be used to limit the scope of rights of the present application. Therefore, equivalent changes made according to the claims of the present application are still within the scope covered by the present application.

Claims

1. A video data processing method, characterized in that: The method comprises: Acquire video data to be processed, and acquire the heat level of the video data to be processed, and determine the processing order of the video data to be processed based on the heat level; Performing feature analysis on the video data to be processed to determine video features of the video data to be processed, wherein the video features are used to indicate at least one of video quality information or video content information of the video data to be processed; the video features include attribute features of the video data to be processed, complexity features of the video data to be processed, content features of the video data to be processed, and scene features of the video data to be processed; the complexity features of the video data to be processed are used to reflect the inter-frame complexity and intra-frame complexity of the video data to be processed; Determining a video enhancement algorithm and video enhancement parameters for the video data to be processed based on the complexity characteristics of the video data to be processed and the content characteristics of the video data to be processed; Determining video transcoding parameters for the video data to be processed based on the attribute characteristics of the video data to be processed, the complexity characteristics of the video data to be processed, and the scene characteristics of the video data to be processed; Performing video enhancement processing on the video data to be processed using the video enhancement algorithm and the video enhancement parameters in the processing order to obtain video data to be transcoded; wherein the video enhancement processing is used to perform image enhancement on image content or image information in the video data to be processed; The video data to be transcoded is subjected to video transcoding processing based on the video transcoding parameters to obtain target video data.

2. The method according to claim 1, characterized in that: The obtaining the heat level of the video data to be processed includes: Acquire a popularity feature of the video data to be processed, where the popularity feature includes at least one of a playback volume, a like volume, a forwarding volume, or a collection volume of the video data to be processed; The heat level of the video data to be processed is determined based on the heat characteristics of the video data to be processed, and the heat level is positively correlated with the processing order of the video data to be processed.

3. The method according to claim 1, characterized in that The performing feature analysis on the video data to be processed to determine the video features of the video data to be processed includes: Performing video clarity analysis on the video data to be processed to obtain attribute characteristics of the video data to be processed, wherein the attribute characteristics of the video data to be processed are used to reflect the resolution and bit rate of the video data to be processed; Performing complexity analysis on the information domain of the video data to be processed to obtain complexity characteristics of the video data to be processed; Analyzing the video elements of the video data to be processed to obtain content features of the video data to be processed, wherein the content features of the video data to be processed are used to reflect the video constituent elements and video quality content of the video data to be processed; A scene analysis is performed on the video data to be processed to obtain scene features of the video data to be processed, where the scene features of the video data to be processed are used to reflect the scene category of the video data to be processed.

4. The method according to claim 1, characterized in that: The determining of the video transcoding parameters for the video data to be processed based on the attribute characteristics of the video data to be processed, the complexity characteristics of the video data to be processed and the scene characteristics of the video data to be processed includes: Determine encoding bit rate parameters and frame rate parameters of the video data to be processed based on the video resolution and bit rate of the video data to be processed, and the inter-frame complexity and intra-frame complexity of the video data to be processed; Determining scene coding parameters based on the scene category of the video data to be processed; The encoding bit rate parameter, the frame rate parameter and the scene encoding parameter are determined as video transcoding parameters of the video data to be processed.

5. The method according to any one of claims 1 to 4, characterized in that: The method further comprises: Obtaining a video push strategy for the target video data, wherein the video push strategy includes a preset push strategy and a tag push strategy; If the video push strategy is a preset push strategy, the target video data is pushed to all accounts in the video playback platform; If the video push strategy is a tag push strategy, the account tags associated with all accounts in the video playback platform are determined, the account tags that match the video tags of the video data to be processed are determined, and the target video data is pushed to the accounts associated with the matching account tags.

6. A video data processing method, characterized in that: The method comprises: In response to the video data to be processed whose heat level is greater than the level threshold, feature analysis is performed on the video data to be processed to determine the video features of the video data to be processed; the video features are used to indicate at least one of the video quality information or the video content information of the video data to be processed; the video features include the attribute features of the video data to be processed, the complexity features of the video data to be processed, the content features of the video data to be processed and the scene features of the video data to be processed; the complexity features of the video data to be processed are used to reflect the inter-frame complexity and intra-frame complexity of the video data to be processed; Determining a video enhancement algorithm and video enhancement parameters for the video data to be processed based on the complexity characteristics of the video data to be processed and the content characteristics of the video data to be processed; Determining video transcoding parameters for the video data to be processed based on the attribute characteristics of the video data to be processed, the complexity characteristics of the video data to be processed, and the scene characteristics of the video data to be processed; Performing video enhancement processing on the video data to be processed using the video enhancement algorithm and the video enhancement parameters in a processing order to obtain video data to be transcoded; wherein the video enhancement processing is used to perform image enhancement on image content or image information in the video data to be processed; The video data to be transcoded is subjected to video transcoding processing based on the video transcoding parameters to obtain target video data.

7. A video data processing device, characterized in that: The device comprises: A video acquisition unit, used to acquire video data to be processed, and to acquire a heat level of the video data to be processed, and to determine a processing order of the video data to be processed based on the heat level; A video analysis unit, configured to perform feature analysis on the video data to be processed to determine video features of the video data to be processed, wherein the video features are used to indicate at least one of video quality information or video content information of the video data to be processed; the video features include attribute features of the video data to be processed, complexity features of the video data to be processed, content features of the video data to be processed, and scene features of the video data to be processed; the complexity features of the video data to be processed are used to reflect the inter-frame complexity and intra-frame complexity of the video data to be processed; A video processing unit is used to determine a video enhancement algorithm and video enhancement parameters for the video data to be processed based on the complexity characteristics of the video data to be processed and the content characteristics of the video data to be processed; determine video transcoding parameters for the video data to be processed based on the attribute characteristics of the video data to be processed, the complexity characteristics of the video data to be processed and the scene characteristics of the video data to be processed; perform video enhancement processing on the video data to be processed using the video enhancement algorithm and the video enhancement parameters in the processing sequence to obtain the video data to be transcoded; wherein the video enhancement processing is used to perform image enhancement on the image content or image information in the video data to be processed; and perform video transcoding processing on the video data to be transcoded based on the video transcoding parameters to obtain target video data.

8. A video data processing device, characterized in that: The device comprises: A video response unit, configured to perform feature analysis on the video data to be processed in response to the video data to be processed whose heat level is greater than a level threshold, so as to determine the video features of the video data to be processed; the video features are used to indicate at least one of the video quality information or the video content information of the video data to be processed; the video features include the attribute features of the video data to be processed, the complexity features of the video data to be processed, the content features of the video data to be processed and the scene features of the video data to be processed; the complexity features of the video data to be processed are used to reflect the inter-frame complexity and intra-frame complexity of the video data to be processed; A strategy acquisition unit is used to determine a video enhancement algorithm and video enhancement parameters for the video data to be processed based on the complexity characteristics of the video data to be processed and the content characteristics of the video data to be processed; determine video transcoding parameters for the video data to be processed based on the attribute characteristics of the video data to be processed, the complexity characteristics of the video data to be processed and the scene characteristics of the video data to be processed; perform video enhancement processing on the video data to be processed using the video enhancement algorithm and the video enhancement parameters in a processing order to obtain video data to be transcoded; wherein the video enhancement processing is used to perform image enhancement on image content or image information in the video data to be processed; and perform video transcoding processing on the video data to be transcoded based on the video transcoding parameters to obtain target video data.

9. A computer device, characterized in that: include: Processor, memory, and network interface; The processor is connected to the memory and the network interface, wherein the network interface is used to provide a data communication function, the memory is used to store program code, and the processor is used to call the program code so that the computer device executes the method described in any one of claims 1 to 5, or executes the method described in claim 6.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program is suitable for being loaded and executed by a processor, so that a computer device having the processor executes the method according to any one of claims 1 to 5, or executes the method according to claim 6.

11. A computer program product, characterized in that The computer program product comprises computer instructions, which are suitable for being loaded by a processor and executing the method according to any one of claims 1 to 5, or executing the method according to claim 6.

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