Video Sequence Analysis via Disjointed Sub-Sequence Segmentation
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Solution Overview
Problem
Conventional video sequence analysis methods are inefficient due to spatial and temporal sub-sampling, leading to low-quality statistics and increased processing time, especially in multi-pass processing scenarios like video compression and denoising, where the complexity of images and temporal coherence issues result in inaccurate frame rate regulation and limited practical application.
Innovation Solution
The method involves determining unconnected consecutive sub-sequences of images based on the type of processing and content of the video sequence, favoring temporal sub-sampling and extrapolating statistics to reduce analysis time, while adapting to specific processing tasks such as compression or denoising, by adjusting sub-sequence sizes and gaps according to the type of processing and content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If spatial and temporal sub-sampling is used to reduce analysis time, then processing speed is improved, but measurement precision of statistics deteriorates
Solution Approach 1:
The video sequence is divided into multiple sub-sequences, each analyzed separately to extract statistics. This segmentation allows for more targeted and accurate analysis of specific portions while maintaining overall processing efficiency, resolving the contradiction between sampling speed and statistics quality.
Solution Approach 2:
The method performs preliminary analysis on selected sub-sequences to extract key statistics before full processing. By pre-analyzing representative sub-sequences and using their statistics to guide subsequent processing, the system achieves both speed and accuracy without requiring complete sequence analysis.
2Measurement precision
If complete sequence analysis is performed to ensure high-quality statistics, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The method extracts only the essential statistical information from selected sub-sequences rather than performing complete analysis on the entire video sequence. By taking out and analyzing only the critical portions that represent the overall sequence characteristics, high-quality statistics are obtained with significantly reduced analysis time.
Solution Approach 2:
Instead of analyzing the complete sequence, the method performs partial analysis on strategically selected sub-sequences. This partial action is sufficient to extract accurate statistics for frame rate regulation while avoiding the time cost of complete sequence analysis.
3Device complexity
If temporal sub-sampling is increased to reduce complexity, then device complexity is reduced, but reliability of frame rate regulation deteriorates
Solution Approach 1:
The analysis complexity is distributed across multiple local sub-sequences rather than requiring uniform high-level analysis of the entire sequence. Each sub-sequence is analyzed with appropriate complexity based on its local characteristics, maintaining reliable frame rate regulation while reducing overall device complexity requirements.
Data Source
AI summary
Disclosed is a method for analyzing a set of images of a video sequence with a view to performing a processing of the sequence. The method includes: determining, in the video sequence, a plurality of disjointed consecutive sub-sequences of at least one successive image according to the type of processing to be carried out and according to the content of the video sequence; and analyzing the images of each sub-sequence determined in the video sequence.