Shot-Based Video Encoding with Relevant-Frame First Pass
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Solution Overview
Problem
Multi-stage shot-based encoding processes require significant processing power and resources due to encoding all video frames, which can be computationally intensive, especially for high-resolution or high-quality videos, and limiting the number of bitstreams results in suboptimal encoding parameters.
Innovation Solution
Identify and encode only relevant frames in the first-pass using spatial and temporal characteristics from shot detection, reducing the number of frames processed, and using these characteristics to determine optimal encoding parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all video frames are encoded in multi-stage shot-based encoding, then encoding parameter accuracy is improved, but processing time and computational resources increase significantly
Solution Approach 1:
The patent segments the video frames into two categories: relevant frames and non-relevant frames. Relevant frames are those that significantly impact encoding parameter determination, while non-relevant frames can be processed more simply. This segmentation allows the system to focus computational resources on frames that matter most for accuracy, reducing overall processing time while maintaining encoding parameter precision.
Solution Approach 2:
The patent extracts and identifies key characteristics from shot detection (such as shot boundaries, scene changes, and important visual features) to determine which frames are relevant for encoding parameter determination. By extracting only the necessary frame information rather than processing all frames uniformly, the system achieves accurate parameter determination with reduced computational overhead.
2Manufacturing precision
If all video frames are encoded in multi-stage shot-based encoding, then encoding quality is improved, but device complexity and resource requirements increase
Solution Approach 1:
The encoding process is segmented into different processing paths based on frame relevance. Relevant frames undergo comprehensive multi-stage encoding analysis, while non-relevant frames use simplified processing. This segmentation maintains high encoding quality for critical frames without requiring the full complexity of multi-stage processing for all frames, thereby reducing overall device complexity.
Solution Approach 2:
The patent applies local quality by treating different frames with different processing intensities based on their importance. Frames near shot boundaries or containing significant visual changes receive full multi-stage encoding treatment, while intermediate frames use lighter processing. This localized approach ensures high encoding quality where needed while reducing device complexity in less critical areas.
3Loss of time
If the number of bitstreams is limited to reduce processing load, then processing time is reduced, but encoding parameter optimization deteriorates
Solution Approach 1:
The patent extracts the most informative characteristics from a subset of relevant frames to determine encoding parameters, rather than requiring multiple bitstreams from all frames. By taking out only the essential frame information needed for parameter optimization, the system achieves good parameter determination with fewer bitstreams, thus reducing processing time without significant loss of optimization quality.
Solution Approach 2:
The patent performs preliminary shot detection and frame relevance assessment before the encoding stage. This preliminary action identifies which frames will contribute most to encoding parameter determination, allowing the system to prepare and process only those frames in subsequent stages. This advance preparation reduces the number of bitstreams needed while maintaining parameter optimization effectiveness.
Data Source
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AI summary
A multi-stage shot encoding pipeline can be made more efficient by having the first-stage or first-pass encoder encode only the relevant frames of a shot, rather than all of the video frames of the shot. Reducing the video frames of the shot that needs to be processed by the first-stage or first-pass encoder can significantly reduce the processing time and storage requirements. Relevant frames can be identified using spatial and temporal characteristics of the video frames in each shot and select only the video frames that have the most relevant information. The spatial and temporal characteristics are already readily available in shot detection, making the solution almost cost-free to implement.