Highlight Video Clip Ranking for Faster Mobile Editing
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Solution Overview
Problem
Video editing can be challenging and time-consuming for users, limiting the amount of footage they can edit due to skill level and available time, necessitating automation of the editing process.
Innovation Solution
A method and system for generating a highlight video by quantifying video clip quality based on visual and aural features, using low-complexity algorithms to automatically select and rank clips, allowing for real-time or concurrent generation of a summary video.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual video editing is performed, then editing precision and control are improved, but editing time and complexity increase
Solution Approach 1:
The system automatically analyzes video content, identifies highlights, and generates edited videos without requiring manual user intervention. The AI model independently evaluates video clips based on predefined criteria and produces the final edited output, allowing the system to serve itself rather than requiring human editors to manually select and assemble clips.
Solution Approach 2:
The patent replaces manual mechanical video editing operations with automated AI-based processing. Instead of humans manually selecting, trimming, and assembling video clips using traditional editing software, the system uses machine learning models to automatically analyze video content, identify highlight segments, and generate edited videos through computational processes.
2Loss of time
If automated video editing is implemented, then editing time is reduced, but algorithm complexity increases
Solution Approach 1:
The video editing process is divided into distinct sequential stages: video input, clip identification using AI analysis, highlight selection based on scoring criteria, and video generation. This segmentation allows each stage to be optimized independently and simplifies the overall system architecture by breaking down the complex editing task into manageable modular components.
Solution Approach 2:
The system uses adjustable parameters such as clip duration thresholds, quality scoring weights, and highlight ratios to control the automated editing process. By changing these parameters, the system can adapt to different video types and editing requirements without requiring fundamental algorithmic changes, making the system flexible and easier to configure for various applications.
3Ease of operation
If low-complexity algorithms are used, then device accessibility is improved, but processing speed decreases
Solution Approach 1:
Instead of processing every single video frame in detail, the system selectively analyzes representative samples of video content to identify highlights. This partial action approach uses AI models to evaluate key moments and segments, achieving sufficient editing accuracy without the computational burden of analyzing every frame, thereby maintaining speed while reducing complexity.
Data Source
AI summary
A method includes obtaining a video having image frames, and determining, for each respective image frame, a corresponding frame content score based on a visual content thereof. The method also includes selecting, from the image frames, a plurality of video clips, where each respective video clip includes a plurality of consecutive image frames and is selected based on the corresponding frame content scores of the plurality of consecutive image frames thereof. The method additionally includes determining, for each respective video clip, a corresponding clip content score based on the corresponding frame content scores of the plurality of consecutive image frames thereof. The method further includes generating a highlight video that includes the plurality of video clips and a representation of a ranking of the plurality of video clips, where the ranking is based on the corresponding clip content score of each respective video clip.