Adaptive Video Frame Encoding with Temporal Correlation Analysis
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Solution Overview
Problem
Current video encoding solutions, particularly for High-Efficiency Video Coding (HEVC) and Advanced Video Coding (AV1), face challenges in maintaining consistent video quality due to pure compression ratio-based strategies, leading to quality fluctuations and poor performance in clips with varying complexity, especially under low bitrate and limited buffer size conditions.
Innovation Solution
An adaptive video frame encoding system that employs a frame analyzer for look ahead and temporal correlation analysis, a complexity-based bit allocator to estimate initial bit allocation, and a temporal adaptive adjuster to dynamically adjust bit allocation based on relative complexity and temporal correlation, ensuring optimal bit allocation for intra and inter frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If pure compression ratio-based strategies are used for bit allocation, then encoding simplicity is maintained, but video quality consistency deteriorates due to quality fluctuations in varying complexity clips
Solution Approach 1:
The patent implements dynamic bit allocation by analyzing temporal correlation between frames and adjusting bit allocation accordingly. The system transitions from static compression ratio-based allocation to dynamic allocation that adapts to scene complexity changes, using motion estimation and temporal difference analysis to determine optimal bit distribution across frames
Solution Approach 2:
The system changes the allocation parameter from fixed compression ratio to variable bit allocation based on temporal correlation metrics. By calculating temporal differences between frames and identifying high-motion versus low-motion regions, the system dynamically adjusts quantization parameters and bit distribution to maintain quality consistency across varying scene complexities
2Reliability
If more bits are allocated to complex frames, then video quality of individual frames improves, but overall bitrate efficiency deteriorates under limited buffer size conditions
Solution Approach 1:
The patent applies local quality enhancement by selectively allocating bits to specific frames based on their temporal correlation characteristics. Instead of uniform quality improvement, the system identifies frames that benefit most from additional bits (those with low temporal correlation to neighboring frames) and allocates resources locally to those specific frames, maximizing perceived quality within bitrate constraints
Solution Approach 2:
The system uses partial action by allocating bits only to frames where it provides marginal utility. Through temporal correlation analysis, the system identifies frames where additional bits will significantly improve quality (low correlation frames) and withholds bits from frames where improvement would be minimal (high correlation frames), achieving efficient partial allocation rather than excessive uniform allocation
3Measurement precision
If look ahead analysis and temporal correlation analysis are performed on all frames, then bit allocation accuracy improves, but computational complexity increases
Solution Approach 1:
The patent segments the video sequence into regions of interest based on motion activity. By dividing frames into high-motion and low-motion regions and applying different analysis depths to each segment, the system performs detailed temporal correlation analysis only where necessary (at frame boundaries or scene transitions) while using simplified allocation in stable regions, reducing overall computational complexity while maintaining allocation accuracy
Solution Approach 2:
The system performs preliminary lightweight analysis on all frames to identify candidate frames for detailed analysis. A first-pass temporal difference calculation quickly identifies frames with significant changes, which then become candidates for more intensive look-ahead analysis. This two-stage approach performs comprehensive analysis only where needed, improving bit allocation accuracy at critical points while limiting computational complexity through selective detailed analysis
Data Source
AI summary
An example apparatus for encoding video frames includes a frame analyzer to execute a look ahead analysis on a received set of frames. The frame analyzer includes a look ahead video analyzer to down-sample the set of frames and encode the down-sampled frames with a constant quantization parameter (QP). A number of generated bits for each of the frames is used as a complexity measurement for each of the frames. The apparatus further includes an encoder to encode the input frame using an adjusted bit allocation based on a relative complexity of the input frame in relation to a complexity of the set of frames.