Entropy Slice Probability Adaptation for Low-Delay Video Coding
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Solution Overview
Problem
Existing video coding technologies face challenges in achieving low-delay processing while maintaining coding efficiency, particularly in multi-core architectures, due to the interruption of spatial dependencies between coding units and the inefficiencies in probability estimation adaptation during parallelization.
Innovation Solution
Adapting probability estimations for entropy coding by utilizing the statistics from spatially neighboring slices, allowing for low-delay coding by initializing and updating probability estimations based on preceding entropy slices, thereby enabling parallel processing without significant coding efficiency loss.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If wavefront processing is used to enable parallelization, then processing speed is improved, but coding efficiency deteriorates due to interruption of spatial dependencies
Solution Approach 1:
The picture is divided into multiple entropy slices that can be processed in parallel. Each slice is further divided into parts that can be decoded independently, enabling wavefront parallel processing while maintaining manageable dependency scopes within each slice.
Solution Approach 2:
Probability estimations are initialized at the beginning of each entropy slice using pre-defined values before actual coding starts. This preliminary initialization allows parallel processing to begin immediately without waiting for probability adaptation from previous slices, while still maintaining coding efficiency through subsequent adaptation.
2Productivity
If probability estimations are initialized at the beginning of each entropy slice, then parallel processing is enabled, but coding efficiency deteriorates due to deviation from actual symbol statistics
Solution Approach 1:
Probability estimations are initialized at the beginning of each entropy slice using pre-defined values before actual coding starts. This preliminary initialization allows parallel processing to begin immediately without waiting for probability adaptation from previous slices, while still maintaining coding efficiency through subsequent adaptation.
Solution Approach 2:
Probability estimations are continuously adapted during the coding process based on actual symbol statistics encountered. This feedback mechanism allows the system to correct the initial deviation from actual statistics, maintaining coding efficiency despite the preliminary initialization required for parallel processing.
3Manufacturing precision
If spatial dependencies between LCUs are maintained, then coding efficiency is improved, but parallelization becomes difficult
Solution Approach 1:
The picture is divided into multiple entropy slices that can be processed in parallel. Each slice is further divided into parts that can be decoded independently, enabling wavefront parallel processing while maintaining manageable dependency scopes within each slice.
Solution Approach 2:
Probability estimation values serve as intermediaries that carry statistical information across slice boundaries. By passing these probability values from one slice to the next, the system maintains coding efficiency without requiring tight coupling of the actual decoding operations, thus enabling parallelization.
4Loss of time
If probability adaptation is performed using only current entropy slice data, then low-delay coding is achieved, but coding efficiency deteriorates due to insufficient adaptation rate
Solution Approach 1:
Probability estimations are initialized at the beginning of each entropy slice using pre-defined values before actual coding starts. This preliminary initialization allows parallel processing to begin immediately without waiting for probability adaptation from previous slices, while still maintaining coding efficiency through subsequent adaptation.
Solution Approach 2:
Probability estimations are continuously adapted during the coding process based on actual symbol statistics encountered. This feedback mechanism allows the system to correct the initial deviation from actual statistics, maintaining coding efficiency despite the preliminary initialization required for parallel processing.
Data Source
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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.