Video Key Frame Extraction via Bidirectional Cross-Correlation
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Solution Overview
Problem
Conventional methods for extracting key frames from videos either result in information loss due to a single frame representing the entire video or are inefficient for videos of varying lengths, leading to missed key information.
Innovation Solution
A method that involves acquiring image frames in positive and reverse order, determining cross-correlation sequences between these frames, and using the global feature distribution to identify target frames that summarize the video content more accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a single key frame is extracted to represent the entire video, then storage space is saved, but information loss occurs and the video summary becomes insufficient
Solution Approach 1:
The video is segmented into multiple key frames rather than using a single frame. The method extracts several important frames that divide the video content into meaningful segments, each representing different important scenes or events, thereby reducing information loss while maintaining storage efficiency.
Solution Approach 2:
The patent introduces a temporal dimension by processing frames in both positive order (forward through the video) and reverse order (backward through the video). This dual-directional approach adds a new dimension to frame selection, allowing the identification of key frames that are important from both beginning-to-end and end-to-beginning perspectives, thus capturing more comprehensive video information.
2Device complexity
If conventional key frame extraction methods are used, then processing is simple, but efficiency is poor for videos of varying lengths and key information is missed
Solution Approach 1:
The patent employs dynamic frame selection based on cross-correlation calculations rather than fixed interval extraction. The method adaptively determines which frames are most important by calculating correlations between frames in both forward and backward directions, allowing the extraction process to dynamically adjust to videos of varying lengths and content types, thereby improving efficiency without excessive complexity.
Solution Approach 2:
The method changes the parameter of frame selection from static (fixed intervals) to dynamic (correlation-based). By computing cross-correlation sequences and using them to weight frame importance, the system adapts its extraction criteria based on actual video content characteristics, improving productivity for videos of different lengths and genres.
3Productivity
If frames are selected based on simple criteria, then extraction is fast, but accuracy of video summary is reduced
Solution Approach 1:
The patent performs preliminary cross-correlation calculations between all frames in both positive and reverse sequences before final frame selection. This preliminary analysis phase computes correlation sequences that capture the relationships between frames, enabling more accurate selection while maintaining reasonable processing speed through efficient algorithm design.
Solution Approach 2:
The method creates a reverse copy of the frame sequence to calculate cross-correlations from both directions. By generating and analyzing a mirrored version of the original frame sequence, the system captures bidirectional temporal relationships, improving selection accuracy without requiring complex real-time processing during the actual extraction phase.
Data Source
AI summary
A method in an illustrative embodiment includes acquiring a first sequence of a set of image frames of a video arranged in positive order. The method further includes generating, based on the first sequence of a positive-order arrangement, a second sequence of the set of image frames arranged in reverse order. The method further includes determining a first cross correlation sequence of the image frames in the first sequence relative to the image frames in the second sequence; determining a second cross correlation sequence of the image frames in the second sequence relative to the image frames in the first sequence; determining, based on the first cross correlation sequence and the second cross correlation sequence, a global feature distribution for the set of image frames; and finally, determining, based on the global feature distribution, at least one target frame in the set of image frames.