Arithmetic Decoding Context Classes for Low-Latency Spectral Coding
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Solution Overview
Problem
The complexity of handling numerous contexts and probability density functions in arithmetic coding leads to increased encoding/decoding latency and memory capacity requirements, necessitating a solution that maintains compression efficiency while reducing these constraints.
Innovation Solution
The method involves using context classes instead of individual contexts to determine probability density functions, achieved by processing preceding spectral coefficients through non-uniform quantization and variance-based selection of quantization schemes, allowing for grouping similar contexts into a single class and reducing the number of required probability density functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a large neighbourhood is used for context-based arithmetic coding to improve compression, then compression efficiency is improved, but the number of contexts and probability density functions increases dramatically
Solution Approach 1:
The patent merges multiple similar contexts into a single context class. Instead of maintaining separate probability density functions for each individual context, the invention groups contexts with similar characteristics together and uses a single representative probability density function for the entire group, thereby reducing the total number of probability density functions required
Solution Approach 2:
The patent changes the parameter representation by using non-uniform quantization of the neighbourhood coefficients. This transforms the continuous or fine-grained context parameters into discrete quantized values, which enables grouping of similar contexts and reduces the effective number of distinct contexts that need to be handled
2Loss of information
If the number of probability density functions is increased to handle more contexts, then compression efficiency is improved, but memory capacity requirements increase
Solution Approach 1:
Multiple context-specific probability density functions are merged into a smaller set of representative probability density functions, one for each context class. This merging directly reduces the memory capacity required to store probability density functions while maintaining compression efficiency through the use of context classes
3Loss of information
If the number of probability density functions is increased to handle more contexts, then compression efficiency is improved, but encoding/decoding latency increases
Solution Approach 1:
By merging contexts into context classes with representative probability density functions, the patent reduces the time required to retrieve and process probability density functions during encoding and decoding, thereby reducing latency while preserving compression performance
Solution Approach 2:
The patent performs non-uniform quantization of neighbourhood coefficients in advance to determine context classes before actual arithmetic coding. This preliminary classification prepares the data structure for efficient probability density function selection, reducing processing time during the main encoding/decoding operation
Data Source
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AI summary
The invention proposes a method and a device for arithmetic decoding of a current spectral coefficient using preceding spectral coefficients. Said preceding spectral coefficients are already decoded and both, said preceding and current spectral coefficients, are comprised in one or more quantized spectra resulting from quantizing time-frequency-transform of video, audio or speech signal sample values. Said method comprises processing the preceding spectral coefficients, using the processed preceding spectral coefficients for determining a context class being one of at least two different context classes, using the determined context class and a mapping from the at least two different context classes to at least two different probability density functions for determining the probability density function, and arithmetic decoding the current spectral coefficient based on the determined probability density function wherein processing the preceding spectral coefficients comprises non-uniformly quantizing absolutes of the preceding spectral coefficients for use in determining of the context class.