Browser Video Cover Generation Using Local Frame Evaluation
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Solution Overview
Problem
Existing methods for generating video covers require significant server-side processing, leading to time-consuming cover generation and excessive server resource consumption.
Innovation Solution
A browser-based method utilizing webassembly and image evaluation models to independently extract and evaluate frames from local video files, reducing the need for server involvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If server-side processing is used for frame extraction and image evaluation, then processing capability is sufficient, but server resource consumption increases and cover generation time increases
Solution Approach 1:
The patent extracts the frame extraction and image evaluation functions from the server and implements them in the browser using WebAssembly and TensorFlow.js. The webassembly-video-parser and trained image evaluation model are loaded into the browser, allowing local processing of video frames without server involvement, thus reducing cover generation time and server resource consumption while maintaining processing capability
Solution Approach 2:
The browser performs frame extraction and image evaluation autonomously using locally loaded WebAssembly and TensorFlow.js components. The system serves itself by processing video covers locally without requiring server resources, transforming the traditional server-dependent model into a client-autonomous model that reduces both time and resource consumption
2Reliability
If server-side processing is used for frame extraction and image evaluation, then processing capability is sufficient, but server resource consumption increases
Solution Approach 1:
The patent extracts computational tasks (frame extraction and image evaluation) from the server environment and relocates them to the browser using WebAssembly and TensorFlow.js. This extraction eliminates the need for server resources to process video covers, significantly reducing server energy consumption and resource usage while maintaining adequate processing capability through optimized client-side execution
Solution Approach 2:
The browser-based system performs video cover processing autonomously using locally deployed WebAssembly video parser and TensorFlow.js image evaluation models. This self-service approach eliminates dependency on server resources, reducing both energy consumption and infrastructure requirements while providing sufficient processing capability for video cover generation
Data Source
AI summary
This application provides techniques for generating a video cover. The techniques comprise monitoring whether the browser enters a target page; initializing a main thread and creating a frame extraction thread and an image evaluation thread; monitoring a target action on the target page extracting a plurality of target frames from the local video file using the webassembly video parser running by the frame extraction thread; determining image evaluation parameters of each of the plurality of target frame using the trained image evaluation model running by the image evaluation thread; obtaining the image evaluation parameters of each of the plurality of target frames from the image evaluation thread by the main thread, selecting one or more candidate frames from the plurality of target frames based on the image evaluation parameters of each of the plurality of target frames, and generating the video cover based on the one or more candidate frames.