Shot Boundary Detection via Key Frame Histogram Analysis
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Solution Overview
Problem
Current video fingerprinting techniques are time-consuming and inefficient, particularly in identifying shot boundaries, which delays critical decisions in video management and content verification processes.
Innovation Solution
A method and system for quickly identifying shot boundaries and extracting video fingerprints by analyzing selected frames in videos, utilizing normalized histogram differences and shot signatures, optimized for inter-coded and intra-coded frames, to determine video fingerprints efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If frame by frame analysis is performed to identify shot boundaries, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent divides the video into segments based on I-frames and P-frames, analyzing only key frames rather than every frame. Shot boundaries are detected by comparing histograms of selected frames (I-frames and preceding P-frames) rather than performing frame-by-frame analysis, thus reducing computation time while maintaining detection accuracy.
Solution Approach 2:
The patent performs preliminary analysis by calculating histograms of I-frames and comparing them with previous I-frames to detect shot boundaries before full fingerprint extraction. This preliminary shot boundary detection allows the system to identify transition points without analyzing all intermediate frames, reducing overall processing time.
2Reliability
If all frames are analyzed for fingerprint extraction, then reliability is improved, but productivity decreases
Solution Approach 1:
The patent extracts only the essential information needed for reliable video identification by analyzing I-frames and selected P-frames. The histogram-based feature extraction from these key frames captures sufficient video characteristics for accurate fingerprinting without requiring analysis of all frames, thus maintaining reliability while improving productivity.
Solution Approach 2:
The patent applies partial action by analyzing a subset of frames (I-frames and certain P-frames) rather than all frames. The shot boundary detection mechanism uses histogram comparison of selected frames to identify transition points, providing sufficient accuracy for video identification while significantly reducing processing requirements.
3Measurement precision
If shot boundary detection is performed on every frame, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The patent segments the video analysis into key frames (I-frames and selected P-frames) rather than processing every frame. Shot boundary detection is performed by comparing histograms of these segmented key frames, reducing the number of computations required while maintaining identification accuracy.
Solution Approach 2:
The patent performs preliminary histogram calculation on I-frames and compares them with previous I-frames to detect shot boundaries. This preliminary detection approach identifies transition frames without requiring energy-intensive analysis of all intermediate frames, reducing overall computational energy consumption.
Data Source
AI summary
The present invention relates to computation of digital fingerprint of a video sequence. The invention presents systems and methods for quick identification of shot boundaries and extraction of fingerprints by processing one or more specific frames. The systems and methods are applied on uncompressed video or compressed video having inter-frame or intra-frame compression. The methods comprises of comparing two frames of the video having a gap in between and identifying a specific frame present in between the two frames such that the specific frame may have a shot boundary. Shot boundaries are calculated for the entire video and then a fingerprint is generated using all the shot boundaries present in the video.