This research introduces a learning-free keyframe selection framework based on perceptual hashing for adaptive sparse guidance in video processing. The method uses DCT-based perceptual hash (pHash) to identify perceptually significant frames through hash distance analysis, with an adaptive thresholding mechanism and multi-scale hashing strategy. It offers O(1) per-frame processing complexity, zero training data requirement, and theoretical guarantees on temporal coverage.
Key findings
Proposes PHASH-SELECT, a learning-free keyframe selection framework based on perceptual hashing.
Utilizes DCT-based perceptual hashing to rapidly identify perceptually significant frames.
Introduces an adaptive thresholding mechanism that adjusts to video content complexity.
Employs a multi-scale hashing strategy to capture global scene changes and local motion patterns.
Achieves O(1) per-frame processing complexity with zero training data and theoretical coverage guarantees.
Limitations & open questions
The proposed method's effectiveness in handling highly dynamic or rapidly changing video content is not yet established.
The robustness of the method against various video compression artifacts and lighting conditions needs further validation.