Entropy Slice Context Sharing for Low-Delay Video Coding
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Solution Overview
Problem
Current video coding technologies face challenges in parallelization due to spatial dependencies between LCUs, leading to coding efficiency losses and increased bitstream burdens, particularly with the HEVC standard and increasing video resolutions, which hinder low-delay processing.
Innovation Solution
A coding concept that adapts probability estimations for entropy decoding and encoding by utilizing previously decoded or encoded parts of entropy slices, allowing for sequential entropy decoding or encoding along specific paths, and incorporating spatial and temporal dependencies to improve coding efficiency and reduce delay.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If spatial dependencies between LCUs are maintained for accurate probability adaptation, then coding efficiency is improved, but parallelization capability deteriorates
Solution Approach 1:
The image is divided into multiple entropy slices that can be processed independently in parallel. Each slice contains a subset of LCUs with interrupted spatial dependencies, allowing simultaneous decoding of multiple slices while maintaining acceptable coding efficiency through probabilistic context modeling within each slice.
Solution Approach 2:
Probability estimations serve as intermediaries that carry spatial dependency information from previously decoded slices to current slice processing. The probability state is passed between slices to maintain coding efficiency without requiring actual spatial dependency, enabling parallelization while preserving compression performance.
2Productivity
If spatial dependencies are interrupted to enable parallelization, then parallelization capability is improved, but coding efficiency deteriorates
Solution Approach 1:
The decoding process uses feedback from previously decoded slices to update probability estimations for current slice processing. This feedback mechanism allows the system to adapt to local image characteristics and maintain high coding efficiency even with interrupted spatial dependencies, as the probability model learns from decoded content.
Solution Approach 2:
The probability estimation parameters are dynamically changed and adapted based on decoded content within each slice. By adjusting probability parameters locally within slices rather than maintaining global spatial dependencies, the system achieves both parallelization capability and coding efficiency through adaptive parameter optimization.
3Measurement precision
If sequential entropy decoding is performed along entropy coding paths, then coding accuracy is improved, but processing delay increases
Solution Approach 1:
The sequential decoding process is segmented into multiple independent entropy slice processing units that can operate in parallel. Each slice follows its own entropy coding path with local probability adaptation, allowing simultaneous processing of multiple segments while maintaining coding accuracy through slice-specific probability modeling.
Solution Approach 2:
The processing architecture transitions from single-thread sequential processing to multi-thread parallel processing by adding the dimension of slice independence. Probability estimations are maintained separately for each slice, enabling parallel execution of entropy decoding paths while preserving coding accuracy through dimension-expanded processing.
Data Source
AI summary
The entropy coding of a current part of a predetermined entropy slice is based on, not only, the respective probability estimations of the predetermined entropy slice as adapted using the previously coded part of the predetermined entropy slice, but also probability estimations as used in the entropy coding of a spatially neighboring, in entropy slice order preceding entropy slice at a neighboring part thereof. Thereby, the probability estimations used in entropy coding are adapted to the actual symbol statistics more closely, thereby lowering the coding efficiency decrease normally caused by lower-delay concepts. Temporal interrelationships are exploited additionally or alternatively.