Entropy Coding State Segmentation for Video Compression
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Solution Overview
Problem
Current video coding standards face inefficiencies in entropy coding due to frequent re-initialization of entropy models for each frame, which does not adapt effectively to content-specific properties within sub-regions, leading to suboptimal compression and decoding performance.
Innovation Solution
The technique involves storing and reusing entropy coding states from co-located regions in previous frames to derive initialization states for corresponding regions in current frames, allowing for adaptive and content-specific entropy coding, even in the presence of motion or changes in segmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If entropy models are re-initialized for each frame to ensure independence and simplicity, then device complexity is reduced and reliability is improved, but compression efficiency deteriorates due to loss of content-specific statistical adaptation
Solution Approach 1:
The patent divides the video content into co-located regions across multiple frames and maintains separate entropy coding states for each region. This segmentation allows the system to adapt statistical models to specific content regions while managing complexity through organized state storage and selective inheritance mechanisms.
Solution Approach 2:
The patent implements dynamic entropy coding state inheritance where the decoder selectively inherits states from previous frames based on content similarity and motion detection. The system dynamically adjusts which frames contribute states to current regions, optimizing compression efficiency while adapting to changing content characteristics.
2Productivity
If entropy coding states are retained from previous frames to improve compression efficiency, then productivity is improved, but device complexity increases due to state storage and management requirements
Solution Approach 1:
The patent applies local quality by maintaining entropy coding states specifically for co-located regions across frames rather than global states. Each region's statistical model is tailored to its specific content characteristics, improving compression efficiency for heterogeneous video content while limiting memory usage to only necessary regional states.
Solution Approach 2:
The patent implements mechanisms to discard entropy coding states when they are no longer relevant (e.g., when content changes significantly or motion detection indicates frame independence) and recover states from appropriate previous frames when beneficial. This dynamic management optimizes memory utilization while maintaining compression efficiency.
3Productivity
If entropy models adapt to content-specific properties within sub-regions, then compression efficiency is improved, but adaptability requirements increase the complexity of model management
Solution Approach 1:
The patent segments video content into co-located regions and maintains separate adaptive entropy coding states for each segment. This allows the system to adapt to content-specific properties within sub-regions independently, improving compression efficiency for heterogeneous content while managing adaptation complexity through modular regional processing.
Data Source
AI summary
Video coding and decoding techniques are provided in which entropy coding states are stored for regions of video frames of a sequence of video frames, upon completion of coding of those regions. Entropy coding initialization states for regions of a current video frame are derived based on entropy coding states of corresponding regions of a prior video frame in the sequence of video frames. This process may be performed at a video encoder and a video decoder, though some signaling may be sent from the encoder to the decoder to direct the decoder is certain operations.