Video Frame Selection via Cumulative Content Difference Thresholds
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Solution Overview
Problem
Current video content analysis methods require significant computing resources to analyze each frame of an encoded video stream, especially with high definition and high resolution videos, leading to inefficiencies and potential missed detection of brief visual content changes.
Innovation Solution
A method and system for selecting significant frames from an encoded video stream based on content difference, where change information is used to calculate cumulative differences between frames, and frames exceeding a predefined threshold are identified and output for analysis, reducing the number of frames that need to be processed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all frames of the encoded video stream are analyzed for content recognition, then detection accuracy is improved, but computing resources and processing time are excessively consumed
Solution Approach 1:
The video stream is segmented into key frames and non-key frames based on motion detection. Only key frames (those containing significant motion or content changes) are selected for detailed content analysis, while non-key frames are skipped. This segmentation approach maintains detection accuracy for important events while dramatically reducing the number of frames requiring computationally intensive analysis.
Solution Approach 2:
Instead of analyzing every frame with full computational resources, the system applies partial analysis by first performing lightweight motion detection on all frames, then applying full content recognition only to the subset of frames identified as key frames. This partial action approach ensures no important content is missed while avoiding wasteful computation on redundant frames.
2Productivity
If frame selection is performed randomly or periodically, then processing efficiency is improved, but visual content changes may be missed or overlooked
Solution Approach 1:
Motion detection is performed as a preliminary action on all frames before content recognition. This preliminary analysis identifies frames containing significant visual changes or motion, which are then flagged as key frames for subsequent detailed content analysis. This ensures that frames with important content changes are reliably selected for analysis while maintaining processing efficiency.
Solution Approach 2:
The system uses motion detection results as feedback to dynamically determine which frames require content analysis. Frames with detected motion or content changes provide positive feedback for selection, while static frames provide negative feedback for skipping. This feedback mechanism ensures reliable detection of visual content changes while optimizing processing efficiency.
Data Source
AI summary
A computer implemented method of selecting significant frames of a compressed video stream based on content difference, comprising, obtaining change information created by an encoder for an encoded video stream constructed of a plurality of encoded frames and indicative of a difference in a visual content between consecutive frames and performing the following for each of the encoded frames to select a plurality of significant frames: (1) analyze the change information to calculate a cumulative difference between the visual content of the respective encoded frame and the visual content of a most recently selected significant frame previously selected from the plurality of encoded frames and (2) select the respective encoded frame as another significant frame in case the cumulative difference exceeds a predefined threshold. Indication for each of the plurality of significant frames is output to one or more analysis systems adapted to analyze the content of the significant frames.