Video cover processing method and device, electronic equipment and storage medium

Abstract

The invention provides a video cover processing method and device, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence, in particular to the technical field of video processing. The specific implementation scheme is as follows: acquiring a target video; screening out a target picture frame from the target video; performing content analysis, image enhancement and image synthesis on the target picture frame to obtain a cover image, and performing content extraction on the target picture frame to obtain a natural language processing text corresponding to the target picture frame; and obtaining a target video cover image according to the cover image and the natural language processing text. According to the method and the device, the video cover which has visual attraction and is rich in information can be automatically created, so that the identifiability and the emotional resonance of the video content are improved, the clicking and watching willingness of the user is increased, and the efficiency and the individuation level of video content management are greatly improved.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence technology, specifically to the field of video processing technology, and in particular to a video cover processing method, device, electronic device and storage medium. Background Art

[0002] With the development of Internet technology, in order to facilitate users to understand the content of the video, a video cover can be generated for the video and displayed to the user.

[0003] Video cover Taking album video cover as an example, in the related art, the video covers in the album may sometimes be unsatisfactory. For example, they may be incomplete characters, incomplete objects, or blurred pictures due to focusing errors during shooting. These unsatisfactory covers cannot effectively evoke the audience's memory and emotional associations with the video content. An ideal video cover should be a clear, complete image that accurately reflects the theme of the video. It should be able to immediately arouse the audience's interest, prompting them to click in and relive those beautiful moments that have been recorded. Therefore, how to generate high-quality video covers to make them more vivid and expressive is of great significance to improving user experience and enhancing the attractiveness of content. Summary of the invention

[0004] The present invention provides a video cover processing method, device, electronic device and storage medium.

[0005] According to one aspect of the present disclosure, a video cover processing method is provided, the method comprising:

[0006] Get the target video;

[0007] Filtering a target picture frame from the target video;

[0008] Performing content analysis, image enhancement and image synthesis on the target picture frame to obtain a cover image, and performing content extraction on the target picture frame to obtain a natural language processing text corresponding to the target picture frame;

[0009] A target video cover image is obtained according to the cover image and the natural language processing text.

[0010] According to another aspect of the present disclosure, a video cover processing device is provided, comprising:

[0011] An acquisition module, used to acquire a target video;

[0012] A screening module, used for screening out target picture frames from the target video;

[0013] A processing module, configured to perform content analysis, image enhancement and image synthesis on the target picture frame to obtain a cover image, and to perform content extraction on the target picture frame to obtain a natural language processing text corresponding to the target picture frame;

[0014] A generation module is used to obtain a target video cover image based on the cover image and the natural language processing text.

[0015] According to a third aspect of the present disclosure, there is provided an electronic device, including:

[0016] at least one processor; and

[0017] a memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute any method in any of the above technical solutions.

[0019] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to execute any one of the methods described in the above technical solutions.

[0020] According to a fifth aspect of the present disclosure, a computer program product is provided, including a computer program, wherein the computer program implements any one of the methods described in the above technical solutions when executed by a processor.

[0021] The present disclosure provides a video cover processing method, device, equipment and storage medium. The present disclosure obtains a target video and selects key picture frames from it, then performs in-depth content analysis, image enhancement and image synthesis on these key picture frames to generate a cover image, and generates a natural language processing text corresponding to the frame through content extraction, and finally fuses the cover image with the text to obtain the target video cover image. In this way, a video cover that is both visually attractive and rich in information can be automatically created, which not only improves the recognizability and emotional resonance of the video content, increases the user's willingness to click and watch, but also greatly improves the efficiency and personalization level of video content management.

[0022] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0024] Figure 1 is a schematic diagram of the steps of the video cover processing method in an embodiment of the present disclosure;

[0025] Figure 2 is a flowchart of a video cover processing method in an embodiment of the present disclosure;

[0026] Figure 3 A principle block diagram of a video cover processing device in an embodiment of the present disclosure;

[0027] Figure 4 It is a block diagram of an electronic device used to implement the video cover processing method of the embodiment of the present disclosure. DETAILED DESCRIPTION

[0028] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0029] Traditional video cover creation methods are often limited to selecting a static frame from the video as the cover image, which has obvious limitations. Since only a fixed frame can be selected, this method often fails to capture the most representative and emotionally expressive moments in the video, resulting in the cover image being difficult to meet high standards in terms of clarity and completeness. Furthermore, this single-frame cover image cannot fully reflect the rich content and deep-seated themes of the entire video, making it difficult to arouse users' curiosity and emotional resonance.

[0030] In order to solve the above technical problems, the present disclosure provides a video cover processing method, see Figure 1 As shown, Figure 1 : is a schematic diagram of the steps of the video cover processing method in an embodiment of the present disclosure. The method can be applied to the server side. The method includes:

[0031] Step S101, obtaining a target video.

[0032] Specifically, "obtaining a target video" means that at the beginning of the video processing process, the server receives or accesses a video file that needs to be generated for a cover in some way. In the solution disclosed in this disclosure, the target video refers to a video with a blurred cover and incomplete content. In this solution, obtaining the target video is the first step to achieve automatic video cover generation, laying the foundation for subsequent intelligent analysis and creative generation.

[0033] Step S102, filtering out target picture frames from the target video.

[0034] Specifically, "selecting target picture frames from the target video" means that during the video cover generation process, the server uses a specific algorithm to automatically identify and extract the most representative and visually attractive single or multiple picture frames from the entire video content. This process usually involves video decoding, converting the video stream into a continuous sequence of image frames, and then using computer vision techniques such as scene detection, motion analysis, or deep learning-based object recognition to evaluate the content of each frame and select the picture frames that best represent the theme and emotion of the video based on preset criteria (for example, frames containing major characters or events, frames with high picture quality, etc.). These selected picture frames will serve as the basis for generating video covers, and subsequent operations such as content analysis, image enhancement and synthesis can be performed to ultimately generate a cover image that accurately reflects the video content and attracts the audience's attention. This step is a key link in achieving automated and intelligent video cover generation, which makes the generation of cover images more accurate and efficient.

[0035] In this way, by filtering out target image frames from the target video, the server can automatically identify and extract the most representative and visually attractive moments in the video, providing key visual materials for generating video covers. This process not only improves the relevance and attractiveness of the cover image, but also more effectively conveys the core content and emotions of the video through carefully selected image frames, thereby improving user experience and increasing video viewing rates. In addition, the automated frame screening process also greatly improves the efficiency of video content processing, saving time and costs for manual editing.

[0036] Step S103, performing content analysis, image enhancement and image synthesis on the target picture frame to obtain a cover image, and performing content extraction on the target picture frame to obtain a natural language processing text corresponding to the target picture frame.

[0037] Specifically, the process of performing content analysis, image enhancement, and image synthesis on the target picture frame to obtain the cover image involves using advanced computer vision techniques to identify key elements in the picture frame, such as people, objects, and scenes. Subsequently, the visual quality of these frames is improved through image enhancement techniques, such as super-resolution, color correction, and contrast adjustment. Image synthesis refers to the fusion of information from multiple frames, or the combination of elements such as text and graphics with images to create a comprehensive cover image that represents the video content. At the same time, the target picture frame is subjected to content extraction, and natural language processing techniques such as sentiment analysis and keyword extraction are used to generate text that matches the content of the picture frame. The natural language processing text obtained in this way can provide descriptive information about the content of the picture frame, enhance the information content and emotional expression of the cover image, and make the final video cover image not only visually attractive, but also able to convey more video content information through text, thereby enhancing users' understanding and interest in the video content.

[0038] In this way, the cover image is obtained by performing content analysis, image enhancement and image synthesis on the target picture frame, and the corresponding natural language processing text is generated from the target picture frame. This is conducive to creating a video cover that is both visually attractive and rich in information. The cover image is clearer and more complete after enhancement and synthesis, and can better represent the video content, while the corresponding text provides additional information and emotional links, enhancing the narrative ability and interactivity of the cover. This method of combining vision and text not only improves the recognizability of video content, but also increases users' curiosity and desire to watch, thereby improving user experience and the dissemination efficiency of video content.

[0039] Step S104, obtaining a target video cover image according to the cover image and natural language processing text.

[0040] Specifically, "obtaining a target video cover image based on a cover image and natural language processing text" involves the process of combining a cover image generated by image processing technology with text information generated by natural language processing technology. In this process, the cover image is created by analyzing and enhancing key frames selected from the video, which conveys the core content of the video in a visual form. The natural language processing text is generated from the same video frames through content extraction technology, which provides a description and emotional color of the video content in the form of text. Combining the two is to merge the visual effect of the image with the information connotation of the text to create the final video cover image. This cover image can not only attract the audience's attention through visual elements, but also provide more information about the video content through text elements, thereby enhancing the expressiveness and appeal of the cover image. Achieving this process usually requires image processing and text rendering technology, as well as the ability to accurately align and layout the two to ensure that the image and text are visually coordinated and complementary in information.

[0041] In this way, by combining the cover image and natural language processing text to obtain the target video cover image, this feature can produce a cover image that is both visually appealing and informative, which effectively combines visual elements and text information to more comprehensively convey the video content. This fusion not only enhances the expressiveness and appeal of the video cover and improves the recognizability of the video content, but also increases the user's curiosity and emotional connection with the video content by providing additional text descriptions, which may increase the video's viewing rate and user engagement. In addition, this automated cover generation method also improves the efficiency of content production and saves the time and cost of manual editing.

[0042] The present disclosure provides a video cover processing method, device, equipment and storage medium. The present disclosure obtains a target video and selects key picture frames from it, then performs in-depth content analysis, image enhancement and image synthesis on these key picture frames to generate a cover image, and generates a natural language processing text corresponding to the frame through content extraction, and finally fuses the cover image with the text to obtain the target video cover image. In this way, a video cover that is both visually attractive and rich in information can be automatically created, which not only improves the recognizability and emotional resonance of the video content, increases users' willingness to click and watch, but also greatly improves the efficiency and personalization level of video content management.

[0043] In some optional embodiments, filtering out a target picture frame from a target video includes:

[0044] Extract consecutive picture frames from the target video;

[0045] Evaluate the clarity and integrity of the picture frame to obtain a quality evaluation result of the picture frame;

[0046] The target picture frame is determined according to the quality assessment result.

[0047] Specifically, extracting continuous picture frames from the target video refers to the process of decomposing the video content into a series of static images in a time sequence, which represent different moments of the video. This process is usually implemented through video decoding technology, in which each frame is a snapshot in the video stream. Next, the clarity and integrity of these picture frames are evaluated. This step involves using image processing algorithms to analyze the quality indicators such as resolution, noise level, and picture integrity of each frame to obtain the quality assessment results of the picture frames. Finally, based on these quality assessment results, the server can determine which frames best meet the standards for generating high-definition and complete cover images, and thus select the target picture frames. These target picture frames will be used in the subsequent cover generation process to ensure that the final cover image can accurately and attractively represent the video content.

[0048] The implementation of this solution relies on automated image analysis technology and quality control algorithms, which aims to efficiently select the best picture frames from the video and provide a basis for generating high-quality video covers.

[0049] In this way, by extracting continuous picture frames from the target video and performing a detailed quality assessment of the clarity and integrity of these frames, this process can ensure that a series of high-quality image data is obtained. Subsequently, the target picture frames are determined based on these evaluation results, and the most qualified frames can be effectively screened out as candidates for video covers. This method improves the selection accuracy of cover images, ensuring that the cover images are not only visually clear and complete, but also accurately reflect the core content of the video, thereby improving the overall quality of the video cover and the audience's first impression, and increasing the attractiveness and desire to watch the video content. In addition, this automated frame selection mechanism based on quality assessment also improves the efficiency and consistency of video cover generation.

[0050] In some optional embodiments, determining the target picture frame according to the quality assessment result includes:

[0051] Obtaining a quality assessment score corresponding to the picture frame in the quality assessment result;

[0052] A picture frame whose quality assessment score exceeds a preset threshold is selected, and the selected picture frame is used as a target picture frame.

[0053] Specifically, obtaining the quality assessment score corresponding to the picture frame in the quality assessment result means using image processing algorithms to quantitatively analyze multiple quality dimensions (such as clarity, contrast, integrity, etc.) of each frame of the image and generate a numerical score for each frame of the image. This score reflects the overall quality level of the image.

[0054] The implementation process of the scheme of this embodiment generally includes automatic scoring using predefined quality assessment criteria or machine learning models. Subsequently, the server sets a quality threshold to determine which picture frames have acceptable quality. Selecting picture frames whose quality assessment scores exceed this preset threshold means selecting only those frames that meet or exceed a specific quality standard as target picture frames for subsequent video cover generation or other related applications. This method ensures that only the highest quality picture frames are selected, thereby improving the visual effect of the video cover and the efficiency of content communication.

[0055] In this way, by obtaining the quality assessment scores corresponding to the picture frames and selecting those picture frames whose scores exceed the preset threshold as the target picture frames, it can ensure that only the highest quality image data is used for the generation of video covers. This method improves the visual quality and content relevance of the cover image, ensuring that the cover image is not only visually appealing but also accurately and clearly conveys the core content of the video. In addition, through the automated quality assessment and screening process, it also improves the efficiency and consistency of video cover generation, reduces the need for manual intervention, and makes the cover generation process more efficient and scalable.

[0056] In some optional embodiments, performing content analysis, image enhancement, and image synthesis on the target picture frame to obtain the cover image includes:

[0057] Analyzing the content of the target picture frame to identify key elements in the target picture frame;

[0058] Perform image enhancement on key elements to obtain enhanced picture frames;

[0059] The enhanced picture frame is input into the image generation model, and the cover image is obtained through the image generation model.

[0060] Specifically, analyzing the content of the target picture frame refers to using computer vision techniques, such as deep learning models, to identify and understand the main visual content in the picture frame. This step can reveal key elements in the picture, such as people, objects, or specific scenes. Once these key elements are identified, image enhancement is performed next, which may include increasing the resolution of the image, adjusting the color balance, enhancing the contrast, or applying other visual effects to make these key elements more prominent and clear. The enhanced picture frame is then input into an image generation model, which may be a deep learning network, such as a generative adversarial network (GAN), which is able to generate a new and more visually attractive cover image based on the input frame. This cover image not only retains the essence of the original video content, but also improves the overall visual effect through enhancement processing, making it more suitable as a video cover and attracting the audience's attention. The entire process is automated and relies on advanced algorithms to achieve efficient content analysis and image generation.

[0061] In this way, by deeply analyzing the content of the target picture frame to identify key elements, then enhancing these elements, and finally inputting the enhanced picture frame into the image generation model to generate the cover image, the quality and attractiveness of the video cover are significantly improved. This automated processing flow not only ensures that the cover image can accurately reflect the core content of the video, but also improves the visual quality of the image through enhancement technology, making the cover image clearer and more vivid. In addition, further optimizing the cover image using the image generation model can create more creative and attractive visual effects, thereby increasing the visibility of the video content and the audience's viewing interest. The automation of the entire process also greatly improves the efficiency of cover image generation and reduces the need and cost of manual editing.

[0062] In some optional embodiments, extracting content from a target picture frame to obtain a natural language processing text corresponding to the target picture frame includes:

[0063] Extracting semantic information of the target image frame;

[0064] A descriptive text is generated based on the semantic information of the target picture frame to obtain a natural language processing text corresponding to the target picture frame.

[0065] Specifically, extracting semantic information from a target picture frame refers to using natural language processing and computer vision techniques, such as deep learning models, to analyze and understand the content in the picture frame and extract meaningful information from it. This process may include identifying objects, scenes, actions, and the relationships between them in the picture, and converting them into data that can be understood by the machine. Generating descriptive text based on this semantic information is to convert this data into human-readable text form, which is usually achieved through well-trained language models that can generate natural language text that accurately describes the picture scene and emotions based on the picture content. The resulting natural language processing text is the descriptive text that corresponds to the target picture frame and can convey the picture content and emotions. This process realizes the conversion from visual data to language description, allowing video content to be searched, understood, and shared in text form, enhancing the accessibility and interactivity of video content.

[0066] In this way, the semantic information of the target image frame is extracted and descriptive text is generated based on this information, so that the deep meaning of the video content can be converted into an easy-to-understand text form. In this way, the key frames of the video are not only presented as visual images, but also supplemented by corresponding natural language processing text, thus providing viewers with a more comprehensive way to understand and feel the video content. This information expression method that combines images and text enhances the searchability and accessibility of video content, and facilitates cross-language and cross-cultural communication. It also improves the visibility of video content in search engines, increases video viewing rate and user engagement. In addition, the automated semantic information extraction and text generation process also improves the efficiency of content processing and reduces the need and cost of manual editing.

[0067] In some optional embodiments, obtaining a target video cover image according to the cover image and the natural language processing text includes:

[0068] The cover image and natural language processing text are fused to obtain the target video cover image.

[0069] Specifically, the process of fusing the cover image and the natural language processing text to obtain the target video cover image refers to combining the previously generated cover image and the corresponding descriptive text in the final stage of video cover generation to create a complete and informative video cover. The cover image is extracted and enhanced from the video using image processing technology, while the natural language processing text is the descriptive text generated after analyzing the video content using NLP (natural language processing) technology. When fusing, the font, size, color and layout of the text are taken into account to ensure that the text is visually coordinated with the image and does not interfere with the main visual elements of the image.

[0070] This process may involve graphic design software or specialized algorithms that can automatically place the text in the best position on the image and adjust the text style to match the overall style and atmosphere of the video. Ultimately, the combination of cover image and text - the target video cover image - can not only attract the audience's attention, but also quickly convey the theme and emotion of the video, thereby increasing the video's appeal and viewing rate.

[0071] In this way, the cover image and natural language processing text are fused to obtain the characteristics of the target video cover image, so that a cover that is both visually attractive and informative can be created. This method of combining images and text can more comprehensively convey the core information and emotional atmosphere of the video content. The cover image provides an intuitive visual element, while the natural language processing text adds a descriptive and emotional dimension to the image. The combination of the two not only enhances the expressiveness of the video cover, but also improves the recognizability and attractiveness of the video content, which may increase users' click-through rate and viewing time. In addition, this fusion method also makes the cover image more effective in search engine optimization (SEO) because it combines the advantages of visual and text information, improving the visibility and competitiveness of the video in search results.

[0072] In order to understand the technical solution of this application as a whole, see Figure 2 , Figure 2 It is a flow chart of the video cover processing method in the embodiment of the present disclosure. Specifically, after the application (APP) is started, the AI ​​(artificial intelligence) model automatically identifies the video with blurred and incomplete cover images to obtain the target video, and selects the target picture frame whose clarity and completeness meet the requirements from the target video. The target picture frame is subjected to content analysis, image enhancement and image synthesis to obtain the cover image, and the target picture frame is subjected to content extraction to obtain the natural language processing text corresponding to the target picture frame; the cover image and the natural language processing text are fused to obtain the target video cover image.

[0073] In this way, high-definition cover images and descriptive texts that are highly relevant to the video content, visually appealing, and rich in information can be generated, thereby improving the recognition of video content, enhancing users' emotional connections, improving user experience, and increasing video click-through rates and viewing rates, while also improving the efficiency and personalization of video content management.

[0074] The following describes an apparatus embodiment of the present application, which can be used to execute the video cover processing method in the above-mentioned embodiment of the present application. For details not disclosed in the apparatus embodiment of the present application, please refer to the above-mentioned embodiment of the video cover processing method of the present application.

[0075] The present disclosure also provides a video cover processing device 300, such as Figure 3 As shown, including:

[0076] An acquisition module 301 is used to acquire a target video;

[0077] A screening module 302 is used to screen out target picture frames from a target video;

[0078] The processing module 303 is used to perform content analysis, image enhancement and image synthesis on the target picture frame to obtain a cover image, and to extract content from the target picture frame to obtain a natural language processing text corresponding to the target picture frame;

[0079] The generation module 304 is used to obtain a target video cover image based on the cover image and natural language processing text.

[0080] In some optional embodiments, the screening module 302 screens out a target picture frame from the target video, including:

[0081] Extract consecutive picture frames from the target video;

[0082] Evaluate the clarity and integrity of the picture frame to obtain a quality evaluation result of the picture frame;

[0083] The target picture frame is determined according to the quality assessment result.

[0084] In some optional embodiments, the screening module 302 determines the target picture frame according to the quality assessment result, including:

[0085] Obtaining a quality assessment score corresponding to the picture frame in the quality assessment result;

[0086] A picture frame whose quality assessment score exceeds a preset threshold is selected, and the selected picture frame is used as a target picture frame.

[0087] In some optional embodiments, the processing module 303 performs content analysis, image enhancement, and image synthesis on the target picture frame to obtain a cover image, including:

[0088] Analyzing the content of the target picture frame to identify key elements in the target picture frame;

[0089] Perform image enhancement on key elements to obtain enhanced picture frames;

[0090] The enhanced picture frame is input into the image generation model, and the cover image is obtained through the image generation model.

[0091] In some optional embodiments, the processing module 303 extracts content from the target picture frame to obtain a natural language processing text corresponding to the target picture frame, including:

[0092] Extracting semantic information of the target image frame;

[0093] A descriptive text is generated based on the semantic information of the target picture frame to obtain a natural language processing text corresponding to the target picture frame.

[0094] In some optional embodiments, the generating module 304 obtains the target video cover image according to the cover image and the natural language processing text, including:

[0095] The cover image and natural language processing text are fused to obtain the target video cover image.

[0096] In the technical solution disclosed herein, the acquisition, storage and application of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0097] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0098] Figure 4 A schematic block diagram of an example electronic device 400 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0099] like Figure 4 As shown, the electronic device 400 includes a computing unit 401, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 402 or a computer program loaded from a storage unit 408 into a random access memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the device 400 can also be stored. The computing unit 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0100] A number of components in the device 400 are connected to the I / O interface 405, including: an input unit 406, such as a keyboard, a mouse, etc.; an output unit 407, such as various types of displays, speakers, etc.; a storage unit 408, such as a disk, an optical disk, etc.; and a communication unit 409, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 409 allows the device 400 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0101] The computing unit 401 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 401 performs the various methods and processes described above, such as the video cover processing method. For example, in some embodiments, the video cover processing method may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 408. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 400 via the ROM 402 and / or the communication unit 409. When the computer program is loaded into the RAM 403 and executed by the computing unit 401, one or more steps of the applet distribution described above may be performed. Alternatively, in other embodiments, the computing unit 401 may be configured to perform the video cover processing method in any other appropriate manner (e.g., by means of firmware).

[0102] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0103] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0104] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0105] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0106] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0107] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0108] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0109] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A video cover processing method, the method comprising: Get the target video; Filtering a target picture frame from the target video; Performing content analysis, image enhancement and image synthesis on the target picture frame to obtain a cover image, and performing content extraction on the target picture frame to obtain a natural language processing text corresponding to the target picture frame; A target video cover image is obtained according to the cover image and the natural language processing text.

2. The method according to claim 1, wherein: The step of filtering out a target picture frame from the target video includes: Extracting continuous picture frames from the target video; Evaluate the clarity and integrity of the picture frame to obtain a quality evaluation result of the picture frame; A target picture frame is determined according to the quality assessment result.

3. The method according to claim 2, wherein: The determining the target picture frame according to the quality assessment result includes: Obtaining a quality assessment score corresponding to the picture frame in the quality assessment result; A picture frame whose quality assessment score exceeds a preset threshold is selected, and the selected picture frame is used as a target picture frame.

4. The method according to claim 1, wherein: The performing content analysis, image enhancement and image synthesis on the target picture frame to obtain the cover image includes: Analyzing the content of the target picture frame to identify key elements in the target picture frame; Performing image enhancement on the key elements to obtain an enhanced picture frame; The enhanced picture frame is input into an image generation model, and a cover image is obtained through the image generation model.

5. The method according to claim 1, wherein: The step of extracting content from the target picture frame to obtain a natural language processing text corresponding to the target picture frame includes: Extracting semantic information of the target picture frame; A descriptive text is generated based on the semantic information of the target picture frame to obtain a natural language processing text corresponding to the target picture frame.

6. The method according to any one of claims 1 to 5, wherein: The step of obtaining a target video cover image according to the cover image and the natural language processing text includes: The cover image and the natural language processing text are fused to obtain a target video cover image.

7. A video cover processing device, comprising: An acquisition module, used to acquire a target video; A screening module, used for screening out target picture frames from the target video; A processing module, configured to perform content analysis, image enhancement and image synthesis on the target picture frame to obtain a cover image, and to perform content extraction on the target picture frame to obtain a natural language processing text corresponding to the target picture frame; A generation module is used to obtain a target video cover image based on the cover image and the natural language processing text.

8. The device according to claim 7, wherein: The screening module screens out a target picture frame from the target video, including: Extracting continuous picture frames from the target video; Evaluate the clarity and integrity of the picture frame to obtain a quality evaluation result of the picture frame; A target picture frame is determined according to the quality assessment result.

9. The device according to claim 8, wherein: The screening module determines the target picture frame according to the quality assessment result, including: Obtaining a quality assessment score corresponding to the picture frame in the quality assessment result; A picture frame whose quality assessment score exceeds a preset threshold is selected, and the selected picture frame is used as a target picture frame.

10. The device according to claim 7, wherein: The processing module performs content analysis, image enhancement and image synthesis on the target picture frame to obtain a cover image, including: Analyzing the content of the target picture frame to identify key elements in the target picture frame; Performing image enhancement on the key elements to obtain an enhanced picture frame; The enhanced picture frame is input into an image generation model, and a cover image is obtained through the image generation model.

11. The device according to claim 7, wherein: The processing module extracts content from the target picture frame to obtain a natural language processing text corresponding to the target picture frame, including: Extracting semantic information of the target picture frame; A descriptive text is generated based on the semantic information of the target picture frame to obtain a natural language processing text corresponding to the target picture frame.

12. The device according to any one of claims 7 to 11, wherein: The generating module obtains a target video cover image according to the cover image and the natural language processing text, including: The cover image and the natural language processing text are fused to obtain a target video cover image.

13. An electronic device, comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 6.

14. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-6.

15. A computer program product, comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 6.

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