CABAC Context Model Switching for Higher Compression Ratio
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Solution Overview
Problem
Conventional context-based adaptive binary arithmetic coding (CABAC) methods experience inefficiencies in compression rates due to slow coding when input symbols are unpredicted in the context model, despite high efficiency when symbols are predicted.
Innovation Solution
The method involves selecting a first context model based on statistics of previously input symbols and a second context model with a most probable symbol (MPS) value different from the first, using the first model for encoding when the input symbol matches the MPS or the second model cannot be selected, and using the second model when the input symbol differs from the MPS and can be selected.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single context model is used for CABAC encoding, then the coding structure is simple, but the compression rate decreases when input symbols are unpredicted in the context model
Solution Approach 1:
The patent implements dynamic context model selection where the encoder switches between a first context model and a second context model based on whether the current input symbol is predicted by the first model. This dynamic adaptation allows the system to maintain simple structure when using a single model while achieving high compression rates when symbol patterns change, directly resolving the contradiction between structural simplicity and compression performance.
Solution Approach 2:
The patent changes the parameter of context model selection by introducing a second context model with different Most Probable Symbol (MPS) values. The system selectively switches between context models based on prediction accuracy, thereby changing the operational parameters to optimize compression rate without significantly increasing overall system complexity.
2Productivity
If a second context model is added to improve compression rate for unpredicted symbols, then the compression efficiency improves, but the device complexity increases
Solution Approach 1:
The patent segments the context modeling into two distinct parts: a first context model for handling predicted symbols and a second context model for handling unpredicted symbols. This segmentation allows each model to be optimized for its specific purpose, improving overall compression efficiency while keeping each individual model relatively simple, thus balancing compression performance with manageable complexity.
Solution Approach 2:
The system employs self-service through automatic context model selection based on prediction outcomes. The encoder autonomously determines which context model to use by checking whether the current symbol is predicted by the first model, eliminating the need for complex external control mechanisms and reducing the burden on device complexity while maintaining high compression efficiency.
3Productivity
If context model switching is implemented to handle unpredicted symbols, then the compression rate improves, but the encoding time increases
Solution Approach 1:
The patent applies preliminary action by pre-establishing two context models with different MPS values before encoding begins. This preparation allows the encoder to quickly switch between models during encoding without performing complex computations on the fly, thereby improving compression rate for unpredicted symbols while minimizing the time penalty associated with model switching.
Solution Approach 2:
The system implements skipping by using a streamlined context model selection mechanism that quickly determines whether to switch models based on simple prediction checks. This rushed-through approach to model selection minimizes the time overhead while still capturing the compression benefits of adaptive context modeling, effectively balancing compression rate improvement with encoding speed.
Data Source
AI summary
A method and an apparatus are provided for context-based adaptive binary arithmetic coding (CABAC) based encoding or decoding using improved selection of a context model for a high compression ratio. The method of CABAC-based encoding includes selecting a first context model based on statistics of previously input symbols and a second context model having a value of a most probable symbol (MPS) different from that of the first context model, encoding a current input symbol using the first context model if the current input symbol has a same value as that of the most probable symbol of the first context model or if the second context model cannot be selected, and encoding the current input symbol using the second context model if the current input symbol has a value different from the most probable symbol of the first context model and if the second context model can be selected.