Arithmetic Decoder Context Value Modification
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Solution Overview
Problem
Current audio coding technologies face challenges in achieving a balance between bitrate efficiency and resource efficiency, particularly in portable consumer devices, where the complexity of audio decoders impacts power consumption and performance.
Innovation Solution
The implementation of an arithmetic decoder and encoder that utilize context-dependent mapping rules to decode and encode spectral values, allowing for efficient computation by modifying the representation of context values based on subregion values, thereby reducing computational effort and maintaining information relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If context-dependent mapping rules are used to improve bitrate efficiency, then coding precision is improved, but device complexity increases
Solution Approach 1:
The context state is segmented into multiple subregions, each handled by dedicated context subregion values. This segmentation allows the arithmetic decoder to process different spectral value ranges independently, improving coding precision while managing complexity through structured division of the decoding task.
Solution Approach 2:
The patent modifies the numeric representation of context values by introducing context subregion values that modify specific portions of the context state. This parameter change enables more precise control over the mapping rules without requiring a complete redesign of the context management system, thus improving precision while controlling complexity growth.
2Loss of information
If complex arithmetic decoding is used to improve bitrate efficiency, then loss of information is reduced, but use of energy increases
Solution Approach 1:
Context subregion values are computed in advance based on previously decoded spectral values, allowing the arithmetic decoder to prepare modification data before the actual decoding of current spectral values. This preliminary action reduces the computational burden during the critical decoding phase, lowering energy consumption while maintaining high fidelity.
Solution Approach 2:
The context state automatically updates itself through the modification process, where previously decoded spectral values generate context subregion values that in turn modify the context for subsequent decoding. This self-service mechanism reduces the need for external control logic and repeated computations, improving efficiency and reducing energy use.
3Productivity
If context modification based on subregion values is implemented to improve resource efficiency, then productivity is improved, but device complexity increases
Solution Approach 1:
Different portions of the context state are modified based on local characteristics of the spectral data. Context subregion values are computed specifically for different frequency regions or spectral value ranges, allowing the encoder to apply tailored modification strategies to different parts of the data, improving overall decoding efficiency while managing complexity through localized processing.
Data Source
AI summary
An audio decoder includes an arithmetic decoder for providing decoded spectral values on the basis of an arithmetically-encoded representation of the spectral values and a frequency-domain-to-time-domain converter for providing a time-domain audio representation using the decoded spectral values. The arithmetic decoder selects a mapping rule describing a mapping of a code value onto a symbol code in dependence on a context state described by a numeric current context value, and determines the numeric current context value in dependence on a plurality of previously-decoded spectral values. The arithmetic decoder modifies a number representation of a numeric previous context value, describing a context state associated with one or more previously decoded spectral values, in dependence on a context subregion value, to acquire a number representation of a numeric current context value describing a context state associated with one or more spectral values to be decoded. An audio encoder uses a similar concept.