Video narrowband high-definition adjustment method and system, intelligent terminal and storage medium

Abstract

The invention relates to a video narrowband high-definition adjustment method and system, an intelligent terminal and a storage medium, and relates to the technical field of video playing processing, and the method comprises the steps: collecting a network signal when a user watches a video, and carrying out the analysis, so as to determine the network quality intensity of the user; when the user network quality intensity is lower than the preset good network quality intensity, performing playing feature sampling on the watched video to determine the number of video dynamic feature samples; if the number of the video dynamic feature samples is lower than the preset reference dynamic sample number, matching a down-regulation frame rate and a down-regulation code rate in a preset definition database based on the network quality intensity; and performing definition adjustment on the played video based on the adjustment frame rate and the adjustment code rate. The method and the device have the effect of improving the video watching experience of a user with a poor broadband condition.

Description

Technical Field

[0001] The present application relates to the technical field of video playback processing, and in particular to a method, system, intelligent terminal and storage medium for adjusting narrow-band high-definition video. Background Art

[0002] With the development of network technology, video playback and live video playback have occupied a very important part in people's daily life, and video clarity technology and broadband stability technology play a decisive role in people's video viewing comfort.

[0003] In the related technologies, the standard format of efficient coding can significantly reduce the size of video files and retain the image quality, but the coding time is relatively long and the requirements for coding are also high, which may affect the efficiency of real-time video processing and transmission. At the same time, some older devices may not support the latest coding standards, resulting in playback compatibility issues. Efficient coding and video enhancement technologies require high computing resources, which may increase the burden on servers and client devices.

[0004] Regarding the above-mentioned related technologies, although bandwidth prediction and adaptive bit rate control technologies can optimize bandwidth utilization, fluctuations in the network environment may still cause video playback interruption or quality degradation, resulting in poor video viewing quality for users under lower bandwidth conditions. Summary of the invention

[0005] In order to improve the video viewing experience of users with poor broadband conditions, the present application provides a video narrowband high-definition adjustment method, system, intelligent terminal and storage medium.

[0006] In a first aspect, the present application provides a video narrowband high-definition adjustment method, which adopts the following technical solution: A video narrowband high-definition adjustment method, comprising: Collect and analyze network signals when users watch videos to determine the quality of the user's network; When the user network quality strength is lower than the preset good network quality strength, the playback feature sampling of the watched video is performed to determine the number of video dynamic feature samples; If the number of video dynamic feature samples is lower than the preset number of benchmark dynamic samples, the frame rate and bit rate in the preset definition database are adjusted down based on the network quality strength match; The clarity of the played video is adjusted based on the frame rate and bit rate.

[0007] Optionally, when determining the number of video dynamic feature samples, the following is also included: When the number of video dynamic feature samples is greater than the preset number of reference dynamic samples, the video is analyzed for playback type to determine the video type; If the video type is the same as the preset live broadcast type, the live broadcast buffering process of the current video is determined based on the user network quality strength analysis; When the live broadcast buffering process is lower than the real-time live broadcast process, historical slices are extracted based on the live broadcast cached video, and the historical slices are inserted into the live broadcast for playback; When the live broadcast cache process is greater than the preset standard cache process, the cached live video is played, and the live video process is adjusted according to the preset process adjustment strategy until the video playback process is the same as the real-time playback process.

[0008] Optional process adjustment policies include: Analyze the real-time playback process and the video playback process to determine the interval process and collect the video slices to be played in the interval process; Perform video image analysis and speech semantic analysis based on the video slices to be played to determine the slice value and select high-value slices; High-value slices among the video slices to be played are screened for compression to determine the compressed slice video and play it.

[0009] Optionally, when matching the downgraded frame rate and downgraded bit rate in the preset definition database, including: Analyze the screen of the video watched by the user to determine the static screen of the video; Analyze the static images of the video and the video playing process to determine the playing time of the static images in the video playing process; Analyze based on the static picture playback time to determine the total static picture playback time; Match the frame rate adjustment weight value and the bit rate adjustment weight value corresponding to the total duration of static picture playback in the preset weight database; Calculate based on the frame rate adjustment weight value and the bit rate adjustment weight value to determine the optimized frame rate reduction and the optimized bit rate reduction; The frame rate reduction and frame rate reduction swap will be optimized, and the bit rate reduction and bit rate reduction swap will be optimized.

[0010] Optionally, also include: Collect and analyze the online viewing information of users when they watch videos to determine the user's activity level; Comparing the user activity with a preset benchmark activity to generate a clarity adjustment instruction or a clarity to be adjusted instruction; The video clarity is adjusted by lowering the frame rate and the bit rate based on the clarity adjustment instruction, and the video buffer is maintained based on the clarity to be adjusted instruction.

[0011] Optionally, also include: Analyze network fluctuation parameters of network signals to determine network fluctuation peaks; When the fluctuation peak value is smaller than the preset severe fluctuation range, the network recovery time corresponding to the network fluctuation peak value in the preset network fluctuation database is matched; The network recovery time is determined by calculation based on the network recovery duration, and the video frame rate and video bit rate are restored at the network recovery time.

[0012] In the second aspect, the present application provides a video narrowband high-definition adjustment system, which adopts the following technical solution: A video narrowband high-definition adjustment system, comprising: The network analysis module collects and analyzes the network signals when users watch videos to determine the quality strength of the user's network; The video analysis module samples the playback characteristics of the watched video to determine the number of video dynamic feature samples when the user network quality strength is lower than the preset good network quality strength; If the number of video dynamic feature samples is lower than the preset number of benchmark dynamic samples, the frame rate and bit rate in the preset definition database are adjusted down based on the network quality strength match; The picture adjustment module adjusts the clarity of the played video based on adjusting the frame rate and bit rate.

[0013] In a third aspect, the present application provides a smart terminal, which adopts the following technical solution: An intelligent terminal comprises a memory and a processor, wherein the memory stores a computer program which can be loaded by the processor and execute any one of the above-mentioned video narrowband high-definition adjustment methods.

[0014] In a fourth aspect, the present application provides a computer storage medium capable of storing corresponding programs, which has the characteristic that the video frame rate is not easily affected by the network quality when the user watches the video, and adopts the following technical solutions: A computer-readable storage medium stores a computer program that can be loaded by a processor and execute any one of the above-mentioned video narrowband high-definition adjustment methods.

[0015] In summary, the present application includes at least one of the following beneficial technical effects: 1. When the user is watching a video and the network quality is poor, the number of motion samples of the video is analyzed to determine whether the video is dynamic or static, and the frame rate and bit rate of the static video are adjusted so that the video does not need to load a lot of network cache and the clarity of the video playback can be maintained; 2. Analyze whether the user is watching a live video. When the network quality is poor, use the static slices in the live video to play it. When the network quality is restored, increase the frame rate and bit rate of the video, which helps improve the user's viewing experience and is not easily affected by network fluctuations. 3. Optimize the video frame rate and bit rate so that the video can maintain smooth playback while further reducing the demand on the network and improving the video playback processing effect under fluctuating network conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a method flow chart of steps S100 to S103 in this application.

[0017] Figure 2 It is a method flow chart of steps S200 to S203 in this application.

[0018] Figure 3 It is a method flow chart of steps S300 to S302 in this application.

[0019] Figure 4 It is a method flow chart of steps S400 to S405 in this application.

[0020] Figure 5 It is a method flow chart of steps S500 to S502 in this application. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solutions and advantages of this application more clear, the following Figure 1-5 It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0022] The embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings.

[0023] The embodiment of the present application discloses a method for adjusting narrow-band high-definition video.

[0024] Reference Figure 1 The method flow of the video narrowband high-definition adjustment method includes the following steps: Step S100: collecting and analyzing network signals when a user is watching a video to determine the strength of the user's network quality; By collecting the client network signal of the user watching the video, the network signal quality strength can be known for further analysis.

[0025] Step S101: when the user network quality strength is lower than a preset good network quality strength, sampling the playback characteristics of the watched video to determine the number of video dynamic feature samples; Good network quality strength means that the client's network can meet the network conditions for high-definition playback, such as the state of stable signal transmission under 5G network. When the user's network quality strength is lower than good network quality strength, it means that there will be certain network fluctuations when watching videos at this time, affecting the loading smoothness.

[0026] By extracting the playback features of the video, we can get the dynamic feature images in the video. The video dynamic features represent the features of people, objects, etc. in the video, which can indicate that the video is dynamic rather than static. The number of video dynamic features is counted and defined as the number of video dynamic feature samples.

[0027] Step S102: if the number of video dynamic feature samples is lower than the preset reference dynamic sample number, the frame rate and bit rate in the preset definition database are adjusted down based on the network quality strength matching; If the number of video dynamic feature samples is lower than the number of baseline dynamic samples, it means that there are more static images in the played video. The number of baseline dynamic samples indicates the number of feature samples of a dynamic video and is set by the staff. The clarity database is pre-established by the staff and stores different network quality strengths and corresponding reduced frame rates and bit rates. When the network quality strength is entered into the clarity database, the corresponding reduced frame rate and bit rate are automatically searched and output.

[0028] Step S103: adjusting the clarity of the played video based on the adjusted frame rate and the adjusted bit rate.

[0029] The clarity of the video playback is adjusted according to the corresponding adjusted frame rate and bit rate of the output, so that the clarity of the video can adapt to the current network quality strength, reduce the jamming caused by incompatibility, and improve the smoothness of video playback.

[0030] Reference Figure 2 , when determining the number of video dynamic feature samples, it also includes: Step S200: When the number of video dynamic feature samples is greater than the preset reference dynamic sample number, the video is analyzed for playback type to determine the video type; When the number of video dynamic feature samples is greater than the number of benchmark dynamic samples, it means that there are more dynamic scenes in the video. By analyzing the playback type of the video, it can be known that the video type is a live type or a pre-downloaded type video with more dynamic scenes.

[0031] Step S201: If the video type is the same as the preset live broadcast type, determine the live broadcast buffering process of the current video according to the user network quality strength analysis; When the video is a live video, the demand for network quality is relatively high. In order to balance the playback smoothness and playback clarity of the live video, further adjustment is required. At this time, the live buffering process of the video is queried.

[0032] Step S202: when the live broadcast buffering process is lower than the real-time live broadcast process, extract historical slices according to the live broadcast cached video, and insert the historical slices into the live broadcast for playback; When the live broadcast buffering process is lower than the real-time live broadcast process, it means that the network quality at this time cannot smoothly maintain the live broadcast progress while meeting the clarity of the current live video.

[0033] Historical slices are partial live video slices stored during the live buffering process. By inserting historical slices into the live video for playback, the live broadcast can be played continuously while the live buffering is completed.

[0034] Step S203: When the live broadcast cache process is greater than the preset standard cache process, the cached live video is played, and the live video process is adjusted according to the preset process adjustment strategy until the video playback process is the same as the real-time playback process.

[0035] The standard cache process means that the cache of the live video can be played smoothly for at least one minute in the future. At this time, the live cache process is compared and analyzed, and the cached video is played. The specific steps of the process adjustment strategy are further explained in the subsequent steps, which are used to adjust the video playback process so that the cached playback progress is the same as the real-time playback process.

[0036] Reference Figure 3 , the process adjustment strategies include: Step S300: Analyze the real-time playback process and the video playback process to determine the interval process and collect the video slices to be played of the interval process; By comparing the real-time playback process with the video playback process, we can know whether the cached video playback process is consistent with the real-time playback process, as well as the interval process between the two playback processes. We can further collect the video slices to be played in the interval process for further adjustment.

[0037] Step S301: Performing video image analysis and speech semantic analysis based on the video slices to be played to determine the slice values ​​and select high-value slices; By analyzing the video image and speech semantics of the video to be played, the cached content of the live video to be played later can be learned, thereby knowing the correlation between the video and the live content. Video slices with higher correlation are defined as high-value slices, and slices with lower correlation are defined as low-value slices.

[0038] Step S302: screening high-value slices in the video slices to be played for compression, so as to determine the compressed slice video and play it.

[0039] By compressing high-value slices and deleting low-value slices other than high-value slices, the video playback time occupied by compressed video slices is shortened, which helps to quickly shorten the process interval between the video playback process and the real-time live broadcast process.

[0040] Reference Figure 4 , when matching the downgraded frame rate and downgraded bit rate in the preset definition database, including: Step S400: analyzing the video screen of the user to determine the static screen of the video; By analyzing the static images of the video, we can know the playing time of each static image in the video, so as to further adjust the frame rate and bit rate.

[0041] Step S401: Analyze based on the video static picture and the video playing process to determine the static picture playing time in the video playing process; The playing time of the static picture is marked so that the total playing time of the static picture can be known.

[0042] Step S402: Analyze based on the static picture playback time to determine the total static picture playback time; The calculation of the total duration of static picture playback is to facilitate the analysis of corresponding adjustment parameters when the frame rate and bit rate are adjusted later.

[0043] Step S403: matching the frame rate adjustment weight value and the bit rate adjustment weight value corresponding to the total duration of static picture playback in the preset weight database; By pre-establishing a weight database and storing the total playback time of different static pictures, as well as the frame rate adjustment weight values ​​and bit rate adjustment weight values ​​corresponding to the total playback time of different static pictures, when the total playback time of the static pictures is input, the corresponding frame rate adjustment weight value and bit rate adjustment weight value can be automatically matched and output for call.

[0044] Step S404: Calculate based on the frame rate adjustment weight value and the bit rate adjustment weight value to determine the optimized lowered frame rate and the optimized lowered bit rate; The optimized reduced frame rate is obtained by multiplying the frame rate adjustment weight value and the reduced frame rate, and the optimized reduced bit rate is obtained by multiplying the bit rate adjustment weight value and the reduced bit rate.

[0045] Step S405: swapping the optimized reduced frame rate with the reduced frame rate, and swapping the optimized reduced bit rate with the reduced bit rate.

[0046] By updating and replacing the bit rate and frame rate, the frame rate and bit rate can be adjusted according to the playback time of the static picture, so that the playback of the dynamic picture is not easily affected, which helps to improve the smoothness and clarity of the video playback.

[0047] Reference Figure 5 , also includes: Step S500: collecting and analyzing the online viewing information of the user when watching the video to determine the user's activity level; Online viewing information means that the user is in the viewing state when watching the video. When the user is in the viewing state during the video playback, it means that the user activity is reduced. The online viewing information can be obtained by collecting and analyzing the interactive information such as the user's likes and barrage sending of the video. Different operations will form corresponding user activity.

[0048] Step S501: comparing the user activity with a preset benchmark activity to generate a clarity adjustment instruction or a clarity to be adjusted instruction; The baseline activity indicates that the user is currently in an interactive video state, such as the user activity corresponding to sending a viewing barrage. If the user activity is greater than the baseline activity, a clarity adjustment instruction is issued to allow the user to have a better viewing experience. Otherwise, a clarity adjustment instruction is issued.

[0049] Step S502: adjusting the video definition by lowering the frame rate and the bit rate based on the definition adjustment instruction, and maintaining the video buffer based on the definition to be adjusted instruction.

[0050] When receiving a definition adjustment instruction, the video is adjusted according to the frame rate and bit rate reduction so that the video can be played smoothly in the corresponding network quality. When receiving a definition adjustment instruction, the video is buffered so that subsequent users can watch the video with higher definition.

[0051] Additionally, it includes: Step S600: Analyze network fluctuation parameters of the network signal to determine the network fluctuation peak value; By performing numerical analysis on network fluctuation parameters, the peak value of network fluctuation can be obtained to facilitate subsequent analysis and calls.

[0052] Step S601: When the fluctuation peak value is smaller than the preset severe fluctuation range, the network recovery duration corresponding to the network fluctuation peak value in the preset network fluctuation database is matched; Severe fluctuations indicate that the network signal changes greatly in a short period of time, which is not conducive to maintaining smooth video playback. The network fluctuation database is pre-established by staff and stores different fluctuation peaks and corresponding network recovery times. When the network fluctuation peak is input, the corresponding network recovery time is automatically matched and output. The network recovery time indicates the time required to restore network stability after a period of fluctuation in the statistical network fluctuation peak.

[0053] Step S602: Restore the video frame rate and video bit rate based on the network recovery time.

[0054] When the duration corresponding to the network recovery time is over, the network is in a stable state, and the video frame rate and video bit rate are adjusted to be restored to correspond to the clarity.

[0055] Based on the same inventive concept, an embodiment of the present invention provides a video narrowband high-definition adjustment system, including: The network analysis module collects and analyzes the network signals when users watch videos to determine the quality strength of the user's network; The video analysis module samples the playback characteristics of the watched video to determine the number of video dynamic feature samples when the user network quality strength is lower than the preset good network quality strength; If the number of video dynamic feature samples is lower than the preset number of benchmark dynamic samples, the frame rate and bit rate in the preset definition database are adjusted down based on the network quality strength match; The picture adjustment module adjusts the clarity of the played video based on adjusting the frame rate and bit rate.

[0056] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device and unit described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0057] An embodiment of the present invention provides a computer-readable storage medium storing a computer program that can be loaded by a processor and executes video narrowband high-definition adjustment.

[0058] Computer storage media include, for example, various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0059] Based on the same inventive concept, an embodiment of the present invention provides an intelligent terminal, including a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and execute a video narrowband high-definition adjustment method.

[0060] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device and unit described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0061] The above are all preferred embodiments of the present application, and are not intended to limit the protection scope of the present application. Any feature disclosed in this specification (including the abstract and drawings), unless otherwise stated, can be replaced by other equivalent or alternative features with similar purposes. That is, unless otherwise stated, each feature is only an example of a series of equivalent or similar features.

Claims

1. A video narrowband high-definition adjustment method, characterized in that: include: Collect and analyze network signals when users watch videos to determine the quality of the user's network; When the user network quality strength is lower than the preset good network quality strength, the playback feature sampling of the watched video is performed to determine the number of video dynamic feature samples; If the number of video dynamic feature samples is lower than the preset number of benchmark dynamic samples, the frame rate and bit rate in the preset definition database are adjusted down based on the network quality strength match; The clarity of the played video is adjusted based on the frame rate and bit rate.

2. The video narrowband high definition adjustment method according to claim 1, characterized in that: When determining the number of video dynamic feature samples, the following are also included: When the number of video dynamic feature samples is greater than the preset number of reference dynamic samples, the video is analyzed for playback type to determine the video type; If the video type is the same as the preset live broadcast type, the live broadcast buffering process of the current video is determined based on the user network quality strength analysis; When the live broadcast buffering process is lower than the real-time live broadcast process, historical slices are extracted based on the live broadcast cached video, and the historical slices are inserted into the live broadcast for playback; When the live broadcast cache process is greater than the preset standard cache process, the cached live video is played, and the live video process is adjusted according to the preset process adjustment strategy until the video playback process is the same as the real-time playback process.

3. The video narrowband high definition adjustment method according to claim 2, characterized in that: Process throttling strategies include: Analyze the real-time playback process and the video playback process to determine the interval process and collect the video slices to be played in the interval process; Perform video image analysis and speech semantic analysis based on the video slices to be played to determine the slice value and select high-value slices; High-value slices among the video slices to be played are screened for compression to determine the compressed slice video and play it.

4. The video narrowband high definition adjustment method according to claim 1, characterized in that: When matching the downgraded frame rate and downgraded bit rate in the preset definition database, including: Analyze the screen of the video watched by the user to determine the static screen of the video; Analyze the static images of the video and the video playing process to determine the playing time of the static images in the video playing process; Analyze based on the static picture playback time to determine the total static picture playback time; Match the frame rate adjustment weight value and the bit rate adjustment weight value corresponding to the total duration of static picture playback in the preset weight database; Calculate based on the frame rate adjustment weight value and the bit rate adjustment weight value to determine the optimized frame rate reduction and the optimized bit rate reduction; The frame rate reduction and frame rate reduction swap will be optimized, and the bit rate reduction and bit rate reduction swap will be optimized.

5. The video narrowband high definition adjustment method according to claim 4, characterized in that: Also includes: Collect and analyze the online viewing information of users when they watch videos to determine the user's activity level; Comparing the user activity with a preset benchmark activity to generate a clarity adjustment instruction or a clarity to be adjusted instruction; The video clarity is adjusted by lowering the frame rate and the bit rate based on the clarity adjustment instruction, and the video buffer is maintained based on the clarity to be adjusted instruction.

6. The video narrowband high definition adjustment method according to claim 5, characterized in that: Also includes: Analyze network fluctuation parameters of network signals to determine network fluctuation peaks; When the fluctuation peak value is smaller than the preset severe fluctuation range, the network recovery time corresponding to the network fluctuation peak value in the preset network fluctuation database is matched; The network recovery time is determined by calculation based on the network recovery duration, and the video frame rate and video bit rate are restored at the network recovery time.

7. A video narrowband high-definition adjustment system, characterized in that: include: The network analysis module collects and analyzes the network signals when users watch videos to determine the quality strength of the user's network; The video analysis module samples the playback characteristics of the watched video to determine the number of video dynamic feature samples when the user network quality strength is lower than the preset good network quality strength; If the number of video dynamic feature samples is lower than the preset number of benchmark dynamic samples, the frame rate and bit rate in the preset definition database are adjusted down based on the network quality strength match; The picture adjustment module adjusts the clarity of the played video based on adjusting the frame rate and bit rate.

8. An intelligent terminal, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and execute the video narrowband high-definition adjustment method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer program is stored which can be loaded by a processor and execute the video narrowband high definition adjustment method according to any one of claims 1 to 6.

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