CN116847123A - Video later editing and video synthesis optimization method - Google Patents

Video later editing and video synthesis optimization method Download PDF

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Publication number
CN116847123A
CN116847123A CN202310958973.5A CN202310958973A CN116847123A CN 116847123 A CN116847123 A CN 116847123A CN 202310958973 A CN202310958973 A CN 202310958973A CN 116847123 A CN116847123 A CN 116847123A
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video
editing
intelligent
audio
synthesis
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Inventor
余骐楠
潘安
陈思远
李倩
汪琥
张梦晗
孔銮铉
温泉
罗昕美
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Nanquan Mutual Entertainment Wuhan Culture Media Co ltd
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Nanquan Mutual Entertainment Wuhan Culture Media Co ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
    • H04N21/23—Processing of content or additional data; Elementary server operations; Server middleware
    • H04N21/233—Processing of audio elementary streams
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
    • H04N21/23—Processing of content or additional data; Elementary server operations; Server middleware
    • H04N21/234—Processing of video elementary streams, e.g. splicing of video streams or manipulating encoded video stream scene graphs
    • H04N21/23418—Processing of video elementary streams, e.g. splicing of video streams or manipulating encoded video stream scene graphs involving operations for analysing video streams, e.g. detecting features or characteristics
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
    • H04N21/23—Processing of content or additional data; Elementary server operations; Server middleware
    • H04N21/236—Assembling of a multiplex stream, e.g. transport stream, by combining a video stream with other content or additional data, e.g. inserting a URL [Uniform Resource Locator] into a video stream, multiplexing software data into a video stream; Remultiplexing of multiplex streams; Insertion of stuffing bits into the multiplex stream, e.g. to obtain a constant bit-rate; Assembling of a packetised elementary stream
    • H04N21/2368—Multiplexing of audio and video streams
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
    • H04N21/23—Processing of content or additional data; Elementary server operations; Server middleware
    • H04N21/238—Interfacing the downstream path of the transmission network, e.g. adapting the transmission rate of a video stream to network bandwidth; Processing of multiplex streams
    • H04N21/2387—Stream processing in response to a playback request from an end-user, e.g. for trick-play
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
    • H04N21/43—Processing of content or additional data, e.g. demultiplexing additional data from a digital video stream; Elementary client operations, e.g. monitoring of home network or synchronising decoder's clock; Client middleware
    • H04N21/439—Processing of audio elementary streams
    • H04N21/4394—Processing of audio elementary streams involving operations for analysing the audio stream, e.g. detecting features or characteristics in audio streams
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
    • H04N21/43—Processing of content or additional data, e.g. demultiplexing additional data from a digital video stream; Elementary client operations, e.g. monitoring of home network or synchronising decoder's clock; Client middleware
    • H04N21/44—Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream or rendering scenes according to encoded video stream scene graphs
    • H04N21/44008—Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream or rendering scenes according to encoded video stream scene graphs involving operations for analysing video streams, e.g. detecting features or characteristics in the video stream

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  • Engineering & Computer Science (AREA)
  • Multimedia (AREA)
  • Signal Processing (AREA)
  • Television Signal Processing For Recording (AREA)
  • Studio Circuits (AREA)

Abstract

The invention relates to the technical field of film and television media, in particular to a video post-editing and video synthesis optimization method, which is characterized in that key scenes and features are automatically identified through content analysis and feature extraction, visual features, semantic information and emotion content are extracted, a video is divided into different scenes by means of semantic association and scene division, semantic information is extracted from audio, subtitles and the like, more accurate scene division is realized, intelligent editing and plot promotion are carried out to automatically split the video into paragraphs or shots, visual smoothness and emotion continuity are maintained, and dynamic audio and video synthesis ensures that the rhythm, emotion and emotion of the audio and the video are matched, so that more infectious viewing experience is provided. The intelligent special effect and the transition automatically generate proper visual special effects according to scenes and emotion characteristics, the visual appeal of the video is enhanced, and the multi-mode data in the video is fused together by multi-mode data fusion to perform joint optimization, so that the video quality is improved.

Description

Video later editing and video synthesis optimization method
Technical Field
The invention relates to the technical field of video media, in particular to a video post editing and video synthesis optimization method.
Background
With the development of the film and television industry and the rising of media, film and television editing gradually becomes an important technology to serve in film making work, but the traditional film and television editing flow usually adopts a manual editing mode, a software editing mode is adopted as an auxiliary mode, the editing efficiency of films with large volume or large quantity is lower in the process of synchronous editing, the editing quality of the final film and television is uneven, and aiming at the problem, an automatic film and television editing technology is generated, but the existing automatic editing technology also needs manual intervention and selection of specific video features, and the feature video can not be automatically extracted and selected after the feature to be edited is selected, so that the automatic video editing processing is not realized fundamentally.
The search CN202211150109.4 discloses a video post-editing and video synthesis optimization method, which specifically comprises the steps of S1, splitting video key frames of an original video file, S2, establishing dynamic video feature extraction for the split video key frames, S3, and carrying out video synthesis on the extracted dynamic video segments; the video composition establishes automatic splicing optimization of dynamic video clips.
But the present inventors have found that this technical solution still has at least the following drawbacks:
first, video post-editing and compositing techniques, while capable of basic editing and special effects processing, are still limited in terms of intelligent processing, lack of comprehensive video content analysis and semantic understanding capabilities, resulting in possible lack of emotional consistency and creative results for editing and compositing. Secondly, the prior art has insufficient performance in terms of user interaction and personalization, lacks flexible personalization options, and the user cannot customize the style of the video clip according to personal preference and demand, thus limiting personalization and customization of the viewing experience. Third, the prior art has to be improved in terms of fusion of augmented reality and virtual scenes, and has a certain limitation in the capability of seamlessly fusing virtual scenes with actual video content, which limits the innovativeness of video content creative and visual experience
Disclosure of Invention
Accordingly, the present invention is directed to a method for optimizing video post-editing and video composition.
Based on the above purpose, the invention provides a video post-editing and video synthesis optimization method.
A video post editing and video synthesis optimizing method specifically comprises the following steps:
s1, carrying out content analysis on an original video, automatically identifying key scenes and features, and extracting visual features, semantic information and emotion content from the video.
S2, dividing the video into different scenes or topics based on the video content analysis result. Semantic information is extracted from audio, subtitle or audio transcription of video by using natural language processing technology and is associated with visual features, so that more accurate scene division is realized.
S3, automatically splitting the video into paragraphs or shots by an intelligent clipping algorithm according to semantic association and scene division results, and sequentially adjusting according to scenario requirements. During the editing process, the visual fluency and emotion continuity are maintained so as to promote the plot development of the video.
S4, intelligently synthesizing different paragraphs or shots after video editing with dynamic audio. By adopting the audio analysis technology, the rhythm, emotion and emotion of the audio and the video are ensured to be matched, and more powerful viewing experience is provided.
S5, introducing an intelligent special effect generation algorithm, and automatically generating proper visual special effects, such as transition, filter and color adjustment, according to scene and emotion characteristics so as to enhance visual attractiveness of the video.
S6, fusing the multi-mode data (visual, audio and text) in the video to perform joint optimization. Through cross-modal association, more accurate feature extraction and synthesis are realized, and video quality is improved.
S7, introducing a user interaction mechanism, and allowing a user to customize the video clip style according to own preference and demand. The personalized options may include music selection, special effect customization, and scenario advancing speed to meet the needs of different users.
S8, combining the augmented reality technology, fusing the virtual scene into the video, and realizing richer and innovative visual experience.
S9, introducing an automatic quality evaluation algorithm, evaluating the generated optimized video, automatically adjusting clipping and synthesizing parameters according to the evaluation result, and continuously optimizing the video quality.
S10, a distributed computing technology is adopted to accelerate the video editing and synthesizing process, and high efficiency and quick response are guaranteed.
Further, the intelligent editing and scenario advancing step further includes:
a. and according to semantic association and scene division results, the intelligent clipping algorithm performs shot splitting according to the content of the video, and automatically selects the optimal shot sequence so as to promote the development of video scenario.
b. In the process of splitting and orderly adjusting shots, the intelligent clipping algorithm ensures smooth visual transition between shots so as to keep continuity and fluency of watching.
Further, the step of synthesizing the dynamic audio and video further comprises:
a. through an audio analysis technology, an intelligent audio synthesis algorithm automatically selects proper audio materials to carry out intelligent synthesis according to the rhythm, emotion and emotion of video content.
b. The intelligent audio synthesis algorithm dynamically adjusts the mixing ratio and volume of the audio materials according to the rhythm and emotion of the video so as to ensure high matching of the audio and video contents.
Further, the intelligent special effect and transition step further comprises:
a. an intelligent special effect generation algorithm is introduced, and proper visual special effects, such as transition, filters and color adjustment, are automatically generated according to scene and emotion characteristics.
b. The intelligent special effect generation algorithm dynamically adjusts the degree and duration of the special effect according to the emotion change and visual effect requirement of the video content so as to enhance the visual appeal of the video.
Further, the multi-modal data fusion step further includes:
a. and fusing the multi-mode data of the visual features, the audio features and the text features in the video by using a cross-mode association technology, and forming a comprehensive feature representation.
b. In the video synthesis process, the intelligent algorithm performs optimization adjustment according to the comprehensive characteristic representation so as to improve video quality and viewing experience.
Further, the step of user interaction and personalization further comprises:
a. the user interaction module provides an interface for the user to select personalized options including music selection, special effect customization and plot advancing speed.
b. According to the personalized options selected by the user, the intelligent algorithm automatically adjusts the video post-editing and synthesizing parameters so as to meet the personalized requirements of the user.
Further, the step of fusing the augmented reality and the virtual scene further comprises:
a. and an augmented reality technology is introduced, and virtual scenes and actual video contents are fused, so that a richer and innovative visual experience is created.
b. The intelligent algorithm dynamically adjusts the augmented reality effect according to the video content and the characteristics of the virtual scene to provide a realistic and attractive visual experience.
Further, the automatic quality assessment and optimization step further comprises:
a. an automatic quality assessment algorithm is introduced to assess the generated optimized video, including aspects of video fluency, visual appeal and audio quality.
b. And according to the result of the automatic quality evaluation, the intelligent algorithm automatically adjusts the post-processing parameters of the video and continuously optimizes the video quality.
Further, the steps of distributed computing and fast processing further include:
a. and decomposing the video post-processing task into a plurality of subtasks by using a distributed computing technology, and processing the subtasks on a plurality of computing nodes in parallel to accelerate the video editing and synthesizing process.
b. Through the rapid processing technology, the response speed and the processing efficiency of the video post-processing are improved, and the user experience is ensured.
The invention has the beneficial effects that:
1. by means of technologies such as video content analysis and feature extraction, semantic association and scene division, key scenes and features can be automatically identified, and intelligent editing and synthesis are achieved. The continuity of video scenario and emotion continuity are guaranteed, and meanwhile, the visual attraction and the infection force of the video are enhanced by utilizing intelligent special effects and audio synthesis, so that the video quality is remarkably improved.
2. By introducing user interaction and personalization modules, users can customize video clip styles according to personal preferences and needs, including music selection, special effect customization, and scenario advancing speed. Such personalized options will meet the needs of different users, providing a more customized and personalized viewing experience.
3. Virtual scenes are fused into an actual video by combining the augmented reality technology, so that a richer, innovative and lifelike visual experience is created for audiences. This will bring a new viewing experience, providing more space for the creative and presentation of the video content, and immersing the viewer in the world of the movie work.
Drawings
In order to more clearly illustrate the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only of the invention and that other drawings can be obtained from them without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of the operational logic of an embodiment of the present invention; .
Detailed Description
The present invention will be further described in detail with reference to specific embodiments in order to make the objects, technical solutions and advantages of the present invention more apparent.
As shown in fig. 1, a method for optimizing video post-editing and video synthesis specifically includes the following steps:
s1, carrying out content analysis on an original video, automatically identifying key scenes and features, and extracting visual features, semantic information and emotion content from the video.
S2, dividing the video into different scenes or topics based on the video content analysis result. Semantic information is extracted from audio, subtitle or audio transcription of video by using natural language processing technology and is associated with visual features, so that more accurate scene division is realized.
S3, automatically splitting the video into paragraphs or shots by an intelligent clipping algorithm according to semantic association and scene division results, and sequentially adjusting according to scenario requirements. During the editing process, the visual fluency and emotion continuity are maintained so as to promote the plot development of the video.
S4, intelligently synthesizing different paragraphs or shots after video editing with dynamic audio. By adopting the audio analysis technology, the rhythm, emotion and emotion of the audio and the video are ensured to be matched, and more powerful viewing experience is provided.
S5, introducing an intelligent special effect generation algorithm, and automatically generating proper visual special effects, such as transition, filter and color adjustment, according to scene and emotion characteristics so as to enhance visual attractiveness of the video.
S6, fusing the multi-mode data (visual, audio and text) in the video to perform joint optimization. Through cross-modal association, more accurate feature extraction and synthesis are realized, and video quality is improved.
S7, introducing a user interaction mechanism, and allowing a user to customize the video clip style according to own preference and demand. The personalized options may include music selection, special effect customization, and scenario advancing speed to meet the needs of different users.
S8, combining the augmented reality technology, fusing the virtual scene into the video, and realizing richer and innovative visual experience.
S9, introducing an automatic quality evaluation algorithm, evaluating the generated optimized video, automatically adjusting clipping and synthesizing parameters according to the evaluation result, and continuously optimizing the video quality.
S10, a distributed computing technology is adopted to accelerate the video editing and synthesizing process, and high efficiency and quick response are guaranteed.
Further, the intelligent editing and scenario advancing step further includes:
a. and according to semantic association and scene division results, the intelligent clipping algorithm performs shot splitting according to the content of the video, and automatically selects the optimal shot sequence so as to promote the development of video scenario.
b. In the process of splitting and orderly adjusting shots, the intelligent clipping algorithm ensures smooth visual transition between shots so as to keep continuity and fluency of watching.
Further, the step of synthesizing the dynamic audio and video further comprises:
a. through an audio analysis technology, an intelligent audio synthesis algorithm automatically selects proper audio materials to carry out intelligent synthesis according to the rhythm, emotion and emotion of video content.
b. The intelligent audio synthesis algorithm dynamically adjusts the mixing ratio and volume of the audio materials according to the rhythm and emotion of the video so as to ensure high matching of the audio and video contents.
Further, the intelligent special effect and transition step further comprises:
a. an intelligent special effect generation algorithm is introduced, and proper visual special effects, such as transition, filters and color adjustment, are automatically generated according to scene and emotion characteristics.
b. The intelligent special effect generation algorithm dynamically adjusts the degree and duration of the special effect according to the emotion change and visual effect requirement of the video content so as to enhance the visual appeal of the video.
Further, the multi-modal data fusion step further includes:
a. and fusing the multi-mode data of the visual features, the audio features and the text features in the video by using a cross-mode association technology, and forming a comprehensive feature representation.
b. In the video synthesis process, the intelligent algorithm performs optimization adjustment according to the comprehensive characteristic representation so as to improve video quality and viewing experience.
Further, the step of user interaction and personalization further comprises:
a. the user interaction module provides an interface for the user to select personalized options including music selection, special effect customization and plot advancing speed.
b. According to the personalized options selected by the user, the intelligent algorithm automatically adjusts the video post-editing and synthesizing parameters so as to meet the personalized requirements of the user.
Further, the step of fusing the augmented reality and the virtual scene further comprises:
a. and an augmented reality technology is introduced, and virtual scenes and actual video contents are fused, so that a richer and innovative visual experience is created.
b. The intelligent algorithm dynamically adjusts the augmented reality effect according to the video content and the characteristics of the virtual scene to provide a realistic and attractive visual experience.
Further, the automatic quality assessment and optimization step further comprises:
a. an automatic quality assessment algorithm is introduced to assess the generated optimized video, including aspects of video fluency, visual appeal and audio quality.
b. And according to the result of the automatic quality evaluation, the intelligent algorithm automatically adjusts the post-processing parameters of the video and continuously optimizes the video quality.
Further, the steps of distributed computing and fast processing further include:
a. and decomposing the video post-processing task into a plurality of subtasks by using a distributed computing technology, and processing the subtasks on a plurality of computing nodes in parallel to accelerate the video editing and synthesizing process.
b. Through the rapid processing technology, the response speed and the processing efficiency of the video post-processing are improved, and the user experience is ensured.
Through the video post editing and video synthesis optimization method, the specific operation logic structure flow is as follows: first, we need to perform content analysis on the original video. This is to let the computer "see" the video, automatically identify the key scenes and features in the video, such as which parts are important, which are emotional rich, and which are to be emphasized. Then, we use computer vision and deep learning techniques to extract visual features, semantic information, emotional content, etc. from the video, which will become the basis for subsequent processing, with the results of content analysis and feature extraction, we next split the video into different scenes or topics. Doing so helps us better understand the structure and storyline of the video. We also need to extract semantic information from audio, subtitle or audio transcript of video using natural language processing techniques. The semantic information is associated with the previous visual features, so that the scene is divided more accurately, the meaning of the video is understood, and the intelligent editing can be started by the scene division and semantic association results. The intelligent clipping algorithm automatically breaks the video into paragraphs or shots and adjusts the sequence according to the scenario needs. Therefore, the continuity and emotion continuity of the video scenario can be maintained, the story of the whole video is smoother and attractive, and next, different paragraphs or shots after video editing and dynamic audio are required to be intelligently synthesized. This is to make the picture and audio of the video match perfectly, and make the sound match with the emotion and rhythm of the picture. The audio analysis technology can help us select proper audio materials to ensure that audio and video contents are highly matched, so that audiences have more infectious viewing experience, and by introducing an intelligent special effect generation algorithm, we can automatically generate proper visual special effects according to scenes and emotion characteristics. These special effects may be transitions, filters, color adjustments, etc., making the video more attractive and visually impact. The method also dynamically adjusts the degree and duration of the special effect according to the emotion change and visual effect requirement of the video content so as to enhance the ornamental effect of the video, and then fuses the multi-mode data (visual, audio, text and the like) in the video for joint optimization. Through cross-modal association, more accurate feature extraction and synthesis can be realized, the quality and viewing experience of the video are further improved, and a user interaction mechanism is introduced, so that a user can customize the video clip style according to own preference and demand. The user can select favorite music, customize special effects, adjust the plot propulsion speed and the like. The personalized options can meet the requirements of different users, so that the watching experience is more personalized and customized, and the virtual scene is fused into the video by combining the augmented reality technology, so that a richer and innovative visual experience is created. Through fusion of virtual scenes, video content can be more vivid and interesting, viewers are immersed in the video content, viewing experience is improved, an automatic quality evaluation algorithm is introduced, and the generated optimized video is evaluated. Through the automatic evaluation result, the clipping and synthesizing parameters can be automatically adjusted, the video quality is continuously optimized, the video effect is more excellent, and finally, in order to deal with large-scale video processing, a distributed computing technology is adopted, the video clipping and synthesizing process is accelerated, and high efficiency and quick response are ensured. This allows more efficient and smooth video processing.
The present invention is intended to embrace all such alternatives, modifications and variances which fall within the broad scope of the appended claims. Therefore, any omission, modification, equivalent replacement, improvement, etc. of the present invention should be included in the scope of the present invention.

Claims (9)

1.一种视频后期剪辑与视频合成优化方法，其特征在于，具体包括以下步骤：1. A video post-editing and video synthesis optimization method, which is characterized by including the following steps: S1、对原始视频进行内容分析，自动识别关键场景和特征，并从视频中提取视觉特征、语义信息和情感内容；S1. Perform content analysis on the original video, automatically identify key scenes and features, and extract visual features, semantic information and emotional content from the video; S2、基于视频内容分析结果，将视频分割为不同的场景或话题；S2. Based on the video content analysis results, segment the video into different scenes or topics; S3、根据语义关联和场景划分结果，智能剪辑算法自动拆分视频为段落或镜头，并根据剧情需要进行顺序调整；S3. Based on the semantic association and scene division results, the intelligent editing algorithm automatically splits the video into paragraphs or shots, and adjusts the sequence according to the needs of the plot; S4、将视频剪辑后的不同段落或镜头与动态音频进行智能合成，采用音频分析技术，确保音频与视频的节奏、情感和情绪相匹配；S4. Intelligent synthesis of different paragraphs or shots after video editing with dynamic audio, using audio analysis technology to ensure that the audio matches the rhythm, emotion and emotion of the video; S5、引入智能特效生成算法，根据场景和情感特征自动生成合适的视觉特效；S5. Introduce an intelligent special effects generation algorithm to automatically generate appropriate visual special effects based on scene and emotional characteristics; S6、将视频中的多模态数据融合起来进行联合优化；S6. Fusion of multi-modal data in the video for joint optimization; S7、引入用户交互机制，允许用户根据自己的喜好和需求定制视频剪辑风格；S7. Introduce a user interaction mechanism to allow users to customize the video editing style according to their own preferences and needs; S8、结合增强现实技术，将虚拟场景融合到视频中；S8, combined with augmented reality technology, integrates virtual scenes into videos; S9、引入自动质量评估算法，对生成的优化视频进行评估；S9. Introduce an automatic quality assessment algorithm to evaluate the generated optimized video; S10、采用分布式计算技术，加速视频剪辑与合成过程。S10 uses distributed computing technology to accelerate the video editing and synthesis process. 2.根据权利要求1所述的一种视频后期剪辑与视频合成优化方法，其特征在于，所述智能剪辑与剧情推进步骤进一步包括：2. A video post-editing and video synthesis optimization method according to claim 1, characterized in that the steps of intelligent editing and plot advancement further include: a.根据语义关联和场景划分结果，智能剪辑算法根据视频的内容进行镜头拆分，并自动选择最佳镜头顺序。a. Based on the semantic association and scene division results, the intelligent editing algorithm splits the shots according to the content of the video and automatically selects the best shot order. b.在进行镜头拆分和顺序调整的过程中，智能剪辑算法确保镜头之间的视觉过渡平滑。b. During the process of shot splitting and sequence adjustment, the intelligent editing algorithm ensures smooth visual transitions between shots. 3.根据权利要求1所述的一种视频后期剪辑与视频合成优化方法，其特征在于，所述动态音视频合成步骤进一步包括：3. A video post-editing and video synthesis optimization method according to claim 1, characterized in that the dynamic audio and video synthesis step further includes: a.通过音频分析技术，智能音频合成算法根据视频内容的节奏、情感和情绪，自动选择合适的音频素材进行智能合成。a. Through audio analysis technology, the intelligent audio synthesis algorithm automatically selects appropriate audio materials for intelligent synthesis based on the rhythm, emotion and mood of the video content. b.智能音频合成算法根据视频的节奏和情感动态调整音频素材的混合比例和音量。b. The intelligent audio synthesis algorithm dynamically adjusts the mixing ratio and volume of the audio material according to the rhythm and emotion of the video. 4.根据权利要求1所述的一种视频后期剪辑与视频合成优化方法，其特征在于，所述智能特效与过渡步骤进一步包括：4. A video post-editing and video synthesis optimization method according to claim 1, characterized in that the intelligent special effects and transition steps further include: a.引入智能特效生成算法，根据场景和情感特征自动生成合适的视觉特效。a. Introduce an intelligent special effects generation algorithm to automatically generate appropriate visual special effects based on scene and emotional characteristics. b.智能特效生成算法根据视频内容的情感变化和视觉效果需求，动态调整特效的程度和持续时间。b. The intelligent special effects generation algorithm dynamically adjusts the degree and duration of special effects based on the emotional changes of the video content and visual effect requirements. 5.根据权利要求1所述的一种视频后期剪辑与视频合成优化方法，其特征在于，所述多模态数据融合步骤进一步包括：5. A video post-editing and video synthesis optimization method according to claim 1, characterized in that the multi-modal data fusion step further includes: a.利用跨模态关联技术，将视频中的视觉特征、音频特征和文本特征多模态数据进行融合，并形成综合特征表示。a. Use cross-modal correlation technology to fuse multi-modal data of visual features, audio features and text features in the video and form a comprehensive feature representation. b.在视频合成过程中，智能算法根据综合特征表示进行优化调整。b. During the video synthesis process, the intelligent algorithm optimizes and adjusts based on the comprehensive feature representation. 6.根据权利要求1所述的一种视频后期剪辑与视频合成优化方法，其特征在于，所述用户交互与个性化步骤进一步包括：6. A video post-editing and video synthesis optimization method according to claim 1, characterized in that the user interaction and personalization steps further include: a.用户交互模块提供界面供用户选择个性化选项。a. The user interaction module provides an interface for users to select personalized options. b.根据用户选择的个性化选项，智能算法对视频后期剪辑与合成参数进行自动调整。b. According to the personalized options selected by the user, the intelligent algorithm automatically adjusts the post-production editing and synthesis parameters of the video. 7.根据权利要求1所述的一种视频后期剪辑与视频合成优化方法，其特征在于，所述增强现实与虚拟场景融合步骤进一步包括：7. A video post-editing and video synthesis optimization method according to claim 1, characterized in that the step of integrating augmented reality and virtual scenes further includes: a.引入增强现实技术，将虚拟场景与实际视频内容进行融合。a. Introduce augmented reality technology to integrate virtual scenes with actual video content. b.智能算法根据视频内容和虚拟场景的特征，动态调整增强现实效果。b. Intelligent algorithms dynamically adjust the augmented reality effect based on the video content and the characteristics of the virtual scene. 8.根据权利要求1所述的一种视频后期剪辑与视频合成优化方法，其特征在于，所述自动质量评估与优化步骤进一步包括：8. A video post-editing and video synthesis optimization method according to claim 1, characterized in that the automatic quality assessment and optimization steps further include: a.引入自动质量评估算法，对生成的优化视频进行评估。a. Introduce an automatic quality assessment algorithm to evaluate the generated optimized videos. b.根据自动质量评估的结果，智能算法自动调整视频后期处理参数。b. Based on the results of automatic quality assessment, intelligent algorithms automatically adjust video post-processing parameters. 9.根据权利要求1所述的一种视频后期剪辑与视频合成优化方法，其特征在于，所述分布式计算与快速处理步骤进一步包括：9. A video post-editing and video synthesis optimization method according to claim 1, characterized in that the distributed computing and rapid processing steps further include: a.利用分布式计算技术，将视频后期处理任务分解为多个子任务，并在多个计算节点上并行处理，加速视频剪辑与合成过程。a. Use distributed computing technology to decompose video post-processing tasks into multiple subtasks and process them in parallel on multiple computing nodes to accelerate the video editing and synthesis process. b.通过快速处理技术，提高视频后期处理的响应速度和处理效率。b. Improve the response speed and processing efficiency of video post-processing through fast processing technology.
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