Spectral Coefficient Coding With Shape-Adaptive Entropy Contexts
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Solution Overview
Problem
Existing context-based arithmetic coding for spectral coefficients of audio signals faces limitations due to memory requirements, computational complexity, and robustness to channel errors, leading to lower coding efficiency, especially for tonal signals where the context has to be overly limited to exploit harmonic structures.
Innovation Solution
A context-adaptive entropy coding method that adjusts the relative spectral distance between decoded and to-be-decoded spectral coefficients based on the shape of the audio signal's spectrum, using information such as pitch, inter-harmonic distance, and formant locations to enhance coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If context-based arithmetic coding is used to encode spectral coefficients, then coding efficiency is improved, but memory requirements and computational complexity increase
Solution Approach 1:
The patent applies local quality by using different context models for different spectral regions. Instead of using a single uniform context model for all spectral coefficients, the invention divides the spectrum into multiple regions (e.g., low-frequency, mid-frequency, high-frequency bands) and assigns specific context models to each region based on the local spectral characteristics. This allows the system to achieve high coding efficiency in each local region while keeping the overall system complexity manageable by only maintaining multiple context models rather than a single complex universal model.
Solution Approach 2:
The patent segments the spectral coefficients into different groups based on frequency regions and applies separate context-adaptive arithmetic coding to each segment. The spectrum is divided into multiple bands, and each band is encoded independently with its own context model. This segmentation reduces the memory requirements compared to a single large context model while maintaining high coding efficiency within each segment, directly addressing the contradiction between coding efficiency and resource consumption.
2Productivity
If context size is increased to exploit harmonic structure of tonal signals, then coding gain is improved, but robustness to channel errors deteriorates
Solution Approach 1:
The patent applies local quality by creating context models with different sizes and scopes tailored to specific spectral regions and signal characteristics. For tonal signals in certain frequency regions, larger context windows are used to capture harmonic structures, while for other regions or signal types, smaller context windows are used. This localized adaptation allows the system to achieve high coding gain where harmonic exploitation is beneficial while maintaining robustness in regions where larger contexts would be vulnerable to channel errors.
Solution Approach 2:
The patent implements dynamic context adaptation where the context size and composition are adjusted based on the detected spectral shape and signal characteristics. The system dynamically selects between different context modeling strategies (e.g., small fixed context, large harmonic-aware context, or adaptive context) depending on the local spectral features. This dynamic approach allows the encoder to optimize for coding gain when the signal structure supports it, while falling back to more robust smaller contexts when channel conditions or signal characteristics make large contexts vulnerable.
Data Source
AI summary
A coding efficiency of coding spectral coefficients of a spectrum of an audio signal is increased by en/decoding a currently to be en/decoded spectral coefficient by entropy en/decoding and, in doing so, performing the entropy en/decoding depending, in a context-adaptive manner, on a previously en/decoded spectral coefficient, while adjusting a relative spectral distance between the previously en/decoded spectral coefficient and the currently en/decoded spectral coefficient depending on an information concerning a shape of the spectrum. The information concerning the shape of the spectrum may have a measure of a pitch or periodicity of the audio signal, a measure of an inter-harmonic distance of the audio signal's spectrum and/or relative locations of formants and/or valleys of a spectral envelope of the spectrum, and on the basis of this knowledge, the spectral neighborhood which is exploited in order to form the context of the currently to be en/decoded spectral coefficients may be adapted to the thus determined shape of the spectrum, thereby enhancing the entropy coding efficiency.