Sample Array Entropy Coding with Neighbor States for Low Delay
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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
The proposed solution involves a decoder and encoder configuration that entropy decodes and encodes sample arrays by adapting probability estimations sequentially along entropy coding paths, using previously decoded or encoded parts to improve coding efficiency and reduce delay, while allowing for parallelization without significant penalties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If spatial dependencies between LCUs are maintained for accurate probability adaptation, then coding efficiency is improved, but parallelization capability deteriorates
Solution Approach 1:
The picture is divided into multiple entropy slices that can be processed in parallel. Each slice contains a subset of LCUs and can be decoded independently to a certain extent, allowing parallel processing while maintaining necessary spatial dependencies within each slice for accurate probability adaptation.
Solution Approach 2:
Different probability adaptation strategies are applied to different regions. Within each entropy slice, full spatial dependencies are maintained for accurate CABAC probability adaptation. Between slices, simplified or interrupted dependencies are used to enable parallel processing, creating local variations in dependency handling.
2Device complexity
If wavefront processing is used to enable parallelization, then parallelization capability is improved, but coding efficiency deteriorates due to dependency interruptions
Solution Approach 1:
The picture is segmented into entropy slices arranged in a wavefront pattern, where slices are processed in parallel but with controlled dependency relationships. This segmentation allows parallel processing while minimizing the impact of dependency interruptions on overall coding efficiency.
Solution Approach 2:
Probability models are initialized and prepared in advance for each entropy slice before parallel processing begins. This preliminary action reduces the impact of dependency interruptions by having ready-to-use probability estimates, thereby maintaining better coding efficiency during parallel wavefront processing.
3Productivity
If sequential entropy decoding is performed to maintain accurate probability estimations, then coding efficiency is improved, but processing delay increases
Solution Approach 1:
The entropy decoding process is segmented into multiple parallel entropy slice processing units. Each unit performs sequential decoding within its assigned slice while maintaining accurate probability estimations, allowing overall parallel processing that reduces delay without sacrificing coding efficiency within each segment.
Solution Approach 2:
Probability estimation states are shared as intermediaries between parallel entropy slice processing units. This allows each unit to maintain accurate local probability models while exchanging necessary information with other units, enabling parallel processing with minimal delay and preserved 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.