Arithmetic Coding of Spectral Coefficients with Quantized Contexts

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Solution Overview

Problem

Arithmetic coding for multimedia data compression faces challenges with high encoding/decoding latency and memory capacity requirements due to the vast number of contexts and corresponding probability density functions needed for effective compression.

Innovation Solution

The method uses preceding spectral coefficients to determine context classes, which are then mapped to probability density functions for encoding/decoding, employing non-uniform quantization to group similar contexts and reduce the complexity of probability density function handling.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of substance

If context based arithmetic coding is used to improve compression, then compression ratio is improved, but encoding/decoding latency and memory capacity requirements increase

Engineering Contradiction:

Improvecompression ratioVSAvoidencoding/decoding latency

Core Design Contradiction:

Loss of substanceVSLoss of time

Solution Approach 1:

The patent segments the continuous spectrum into multiple sub-blocks and processes them in parallel. Each sub-block is encoded/decoded independently using the same arithmetic coding engine, which allows the processing to be divided into smaller tasks that can be executed concurrently, thereby reducing overall latency while maintaining compression effectiveness.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent uses a limited set of representative probability density functions (PDFs) instead of computing exact PDFs for all possible contexts. By selecting a manageable number of representative PDFs that cover the most likely context scenarios, the system achieves good compression without the computational burden of handling all possible contexts, thus reducing latency.

Inventive Principle:

Principle #16Partial or excessive action

2Loss of substance

If context based arithmetic coding is used to improve compression, then compression ratio is improved, but memory capacity requirements increase

Engineering Contradiction:

Improvecompression ratioVSAvoidmemory capacity requirements

Core Design Contradiction:

Loss of substanceVSQuantity of substance

Solution Approach 1:

The patent divides the spectrum into multiple sub-blocks that can be processed independently. This segmentation allows the system to reuse the same probability density functions across different sub-blocks, reducing the total memory required to store context information and PDFs compared to processing the entire spectrum as a single large context.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent creates a set of representative probability density functions that can be universally applied across multiple contexts and sub-blocks. Instead of having separate PDFs for every possible context combination, the same representative PDFs are reused across different sub-blocks and contexts, significantly reducing memory capacity requirements while maintaining compression effectiveness.

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Loss of substance

If larger neighbourhood is used for context definition to improve compression, then compression ratio is improved, but device complexity increases

Engineering Contradiction:

Improvecompression ratioVSAvoidcomplexity of probability density function handling

Core Design Contradiction:

Loss of substanceVSDevice complexity

Solution Approach 1:

The patent segments the large neighbourhood into smaller sub-blocks that can be processed independently. By dividing the neighbourhood into manageable sub-blocks, the system reduces the complexity of handling large contexts while still capturing the essential statistical dependencies within each sub-block, thereby maintaining compression ratio without excessive device complexity.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent uses a limited set of representative probability density functions instead of computing exact PDFs for all possible neighbourhood configurations. By selecting a manageable number of representative PDFs that capture the most important context patterns, the system achieves good compression without the computational complexity of handling all possible neighbourhood variations.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS10516414B2Method and device for arithmetic encoding or arithmetic decoding

Publication Date: 2019.12.24 DOLBY LABORATORIES LICENSING CORP

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.