Video Summary Frame Selection Using Neural Costs and Dynamic Programming
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Solution Overview
Problem
Existing video processing techniques lack an efficient method to generate a visual summary of a video, which is desirable for quick user assessment and storage optimization.
Innovation Solution
A summary frame engine using a neural network to compute representation costs between frames and minimize a mathematical function to select a subset of frames that represent the video's content, while allowing user override for inappropriate content exclusion, employing dynamic programming for optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all video frames are stored and processed, then complete video content is preserved, but storage requirements and computational complexity increase significantly
Solution Approach 1:
The patent extracts only the most representative frames (summary frames) from the complete video frame sequence. By identifying and selecting key frames that capture essential video content while discarding redundant frames, the system achieves efficient storage and processing without significant information loss. This extraction approach directly resolves the contradiction by maintaining content representation quality while dramatically reducing storage quantity.
Solution Approach 2:
The system changes the parameter of frame selection from including all frames to selecting only representative frames based on computational metrics. By using representation cost calculations and optimization algorithms to determine which frames to retain, the system transforms the complete frame set into a optimized subset, resolving the storage vs. information preservation contradiction through intelligent parameter-based selection.
2Measurement precision
If all video frames are processed, then comprehensive video analysis is achieved, but computational complexity and processing time increase
Solution Approach 1:
The patent extracts only the most representative frames for processing and analysis. By identifying summary frames that capture essential video content and excluding redundant frames, the system reduces computational complexity while maintaining analysis accuracy. This extraction is achieved through representation cost calculations that identify frames providing the most information value.
Solution Approach 2:
The system applies partial action by processing only a subset of frames (summary frames) rather than all frames. This partial processing approach is sufficient to achieve comprehensive video analysis because the selected summary frames contain the essential information needed for accurate content understanding, thereby reducing computational complexity without sacrificing measurement precision.
3Productivity
If a small number of summary frames are selected, then storage and processing efficiency improve, but video content representation quality may deteriorate
Solution Approach 1:
The system changes the selection criterion from arbitrary or uniform sampling to optimization-based selection using representation cost parameters. By calculating representation costs between frames and using mathematical optimization to minimize total representation cost, the system identifies the smallest subset of frames that best represents the complete video content, thereby maintaining representation quality while improving processing efficiency.
Solution Approach 2:
The system performs preliminary calculations of representation costs between all frame pairs before selecting summary frames. This preliminary action enables informed selection of frames that will provide the best representation quality, ensuring that even with a small number of summary frames, the video content is accurately represented. The preliminary cost analysis guides the selection to avoid information loss.
4Adaptability or versatility
If user override capability is added to exclude inappropriate frames, then content control improves, but system complexity increases
Solution Approach 1:
The system introduces user preferences as an intermediary layer between the automatic summary frame selection process and the final frame selection. User-defined ineligible frames act as constraints that guide the optimization algorithm, allowing content control without requiring complete system redesign. This intermediary approach enables adaptability while maintaining the core automated selection mechanism, thus limiting the increase in system complexity.
Data Source
AI summary
A computer obtains a set of frames of a video. The computer generates, by a neural engine, a data structure of representation costs for the set of frames. The computer determines, by a dynamic programming engine and based on the data structure and at least one constraint, a set of summary frames for the video, wherein each summary frame represents a time contiguous subset of the set of frames. The computer provides an output of the set of summary frames.