Arithmetic Decoder Context State Adaptation for Audio Coding
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Solution Overview
Problem
Current audio encoding and decoding technologies face challenges in achieving a balance between bitrate efficiency and resource efficiency, particularly in portable consumer devices that require low power consumption and complexity, due to the impact of spectral noise on coding quality and complexity.
Innovation Solution
The implementation of an arithmetic decoder and encoder that dynamically adjust the context state based on the magnitude of previously decoded or encoded spectral values, using a context-dependent mapping rule to optimize the encoding and decoding process, allowing for efficient detection and modification of context states to improve coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If traditional spectral noiseless coding is used in audio encoders, then bitrate efficiency can be improved, but device complexity and power consumption increase significantly
Solution Approach 1:
The patent segments the spectral values into different groups based on their magnitude characteristics. By dividing the spectral processing into distinct segments (e.g., significant spectral values versus less significant ones), the decoder can apply different processing strategies to each segment, reducing overall computational complexity while maintaining encoding efficiency.
Solution Approach 2:
The patent applies local quality by treating different spectral regions with different processing precision. Specifically, it identifies and processes only the most significant spectral values with full precision, while using simplified processing for less significant values. This localized approach to quality maintains where needed while reducing complexity elsewhere.
2Productivity
If context-dependent mapping rules are used to adapt to signal constellations, then coding efficiency improves, but computational effort increases
Solution Approach 1:
The patent applies partial action by computing context information selectively rather than for all spectral values. It identifies specific signal constellations where context adaptation provides benefit and applies the computationally intensive context-dependent mapping only in those cases, while using simpler mapping rules elsewhere.
Solution Approach 2:
The patent changes parameters dynamically by adjusting the context state based on detected signal characteristics. When specific patterns are detected in the spectral values, the system transitions between different context states, allowing the mapping rules to adapt to the local signal properties without maintaining high computational complexity throughout.
3Measurement precision
If spectral values are quantized according to psychoacoustic models, then perceptual quality improves, but precision of spectral representation decreases
Solution Approach 1:
The patent applies local quality by maintaining high spectral precision for perceptually significant frequency regions while using coarser quantization for less important regions. The psychoacoustic model identifies which spectral components are most important for human perception, and the system preserves precision locally in those regions while accepting greater quantization elsewhere.
Data Source
AI summary
An audio decoder for providing a decoded audio information includes a arithmetic decoder for providing a plurality of 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 is configured to select a mapping rule describing a mapping of a code value onto a symbol code in dependence on a context state. The arithmetic decoder is configured to determine or modify the current context state in dependence on a plurality of previously-decoded spectral values. The arithmetic decoder is configured to detect a group of a plurality of previously-decoded spectral values, which fulfill, individually or taken together, a predetermined condition regarding their magnitudes, and to determine the current context state in dependence on a result of the detection.An audio encoder uses similar principles.