Spectral Coefficient Arithmetic Coding with Quantized Context Classes
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Solution Overview
Problem
Arithmetic coding for multimedia data compression faces challenges in managing a large number of contexts and corresponding probability density functions, leading to increased encoding/decoding latency and memory requirements.
Innovation Solution
The method uses preceding spectral coefficients to determine context classes, which are then mapped to probability density functions for arithmetic encoding or decoding, employing non-uniform quantization to group similar contexts and reduce the number of functions needed, thereby decreasing latency and memory requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a large number of contexts are used for arithmetic coding to improve compression ratio, then compression effectiveness is improved, but encoding/decoding latency and memory requirements increase
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 that share similar statistical characteristics and uses a single PDF for all contexts in the group. This merging approach maintains compression effectiveness while significantly reducing the number of PDFs that need to be stored and processed, thereby reducing encoding/decoding latency.
Solution Approach 2:
The patent creates context classes that serve multiple individual contexts simultaneously. A single context class and its associated probability density function are used universally for all contexts within that class, rather than having dedicated PDFs for each context. This multi-functional approach reduces the overall number of PDFs needed while still providing accurate probability modeling for diverse contexts.
2Loss of information
If a large number of contexts are used for arithmetic coding to improve compression ratio, then compression effectiveness is improved, but memory capacity requirements increase
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 that share similar statistical characteristics and uses a single PDF for all contexts in the group. This merging approach maintains compression effectiveness while significantly reducing the number of PDFs that need to be stored and processed, thereby reducing encoding/decoding latency.
Solution Approach 2:
The patent creates context classes that serve multiple individual contexts simultaneously. A single context class and its associated probability density function are used universally for all contexts within that class, rather than having dedicated PDFs for each context. This multi-functional approach reduces the overall number of PDFs needed while still providing accurate probability modeling for diverse contexts.
3Measurement precision
If a large number of probability density functions are maintained to handle different contexts, then coding accuracy is improved, but device complexity increases
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 that share similar statistical characteristics and uses a single PDF for all contexts in the group. This merging approach maintains compression effectiveness while significantly reducing the number of PDFs that need to be stored and processed, thereby reducing encoding/decoding latency.
Solution Approach 2:
The patent changes the parameter of context representation from individual context identifiers to context class identifiers. By transforming the fine-grained context information into coarser context class categories, the system reduces the number of PDFs needed while preserving the essential statistical characteristics required for accurate arithmetic coding.
Data Source
AI summary
The invention proposes a method and a device for arithmetic encoding of a current spectral coefficient using preceding spectral coefficients. Said preceding spectral coefficients are already encoded 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 encoding 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.