Entropy Slice Probability 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
1Manufacturing precision
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 on multiple cores while maintaining sufficient coding efficiency through careful slice boundary placement and probability estimation adaptation within each slice.
Solution Approach 2:
Probability estimations are adapted using previously decoded parts within the same entropy slice before decoding subsequent parts. This preliminary adaptation of probability models ensures accurate entropy decoding while maintaining the parallelizable structure of entropy slices, resolving the conflict between coding efficiency and parallelization.
2Productivity
If spatial dependencies are interrupted to enable parallelization, then productivity is improved, but coding efficiency deteriorates
Solution Approach 1:
Different regions of the image (different entropy slices) have different dependency structures. Within each slice, spatial dependencies are interrupted to enable parallelization, while probability adaptation is locally optimized using previously decoded parts of the same slice. This local adaptation maintains coding efficiency despite the interruption of global spatial dependencies.
3Manufacturing precision
If sequential entropy decoding is performed along entropy coding paths, then coding efficiency is maintained, but processing time increases
Solution Approach 1:
The sequential decoding process is segmented into multiple independent entropy slices that can be decoded in parallel. Each slice maintains its internal sequential decoding order along the entropy coding path to preserve coding efficiency, while the overall processing time is reduced through parallel execution of multiple slices on multi-core architectures.
Solution Approach 2:
The decoding problem is transformed from a single-dimensional sequential process to a multi-dimensional parallel process. Multiple entropy slices are processed simultaneously across different processing cores, adding a parallel processing dimension while maintaining the sequential integrity within each slice, thereby reducing overall processing time without sacrificing coding efficiency.
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.