Scene Break Prediction Using Previous Scene Characteristics
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Solution Overview
Problem
Conventional cut detection systems in video processing are inefficient due to their failure to account for scene characteristics when predicting scene breaks and do not integrate posterframe identification with cut detection, leading to missed hits and false hits.
Innovation Solution
The Scene Detector analyzes video frames based on characteristics of previously identified scenes to optimize the search for scene breaks, concurrently identifying posterframes by using statistical predictors and histogram intersections to determine actual scene breaks and representative frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional cut detection algorithms are used to locate scene breaks, then the system can identify scene transitions, but the system produces missed hits and false hits due to not considering scene characteristics
Solution Approach 1:
The system performs preliminary scene characterization by computing statistical features (mean, standard deviation, skewness, kurtosis) for luminance, saturation, and hue channels before attempting cut detection. This preliminary analysis of scene characteristics enables more accurate prediction of where scene breaks are likely to occur, reducing both missed hits and false hits in the subsequent detection phase.
2Productivity
If conventional cut detection is performed without considering scene characteristics, then the processing is simpler, but the detection accuracy decreases leading to missed hits and false hits
Solution Approach 1:
The video processing is segmented into distinct phases: (1) scene characterization phase where statistical features are computed for each frame, (2) prediction phase where scene break locations are predicted based on characteristics from phase 1, and (3) verification phase where actual cuts are detected near predicted locations. This segmentation allows the system to focus computational resources efficiently while maintaining high detection accuracy.
3Ease of manufacture
If separate processes are used for cut detection and posterframe identification, then each process can be optimized independently, but the overall processing time and complexity increase
Solution Approach 1:
The system merges cut detection and posterframe identification into a single integrated process. During scene characterization, the same statistical features used for cut detection are also utilized to identify representative frames (posterframes) for each scene. This combination eliminates redundant processing steps and reduces overall processing time while maintaining the ability to perform both functions effectively.
Data Source
AI summary
Methods and apparatus provide for a Scene Detector to optimize the location of scene breaks in a set of video frames. Specifically, the Scene Detector receives a set of video frames and a corresponding content model for each video frame. As the Scene Detector identifies a scene in the set of video frames, the Scene Detector updates statistical predictors with respect to characteristics of that scene's characteristics. The Scene Detector thereby utilizes the updated statistical predictors to identify a video frame that may be the next scene break. The Scene Detector analyzes video frames with respect to the possible next scene break in order to identify the actual second scene break that occurs after the previously identified scene break.