Arithmetic Coding Context Classes for Low-Latency Spectral Encoding

Solution Overview

Problem

Existing arithmetic coding methods face challenges with high encoding/decoding latency and memory capacity requirements due to the large number of contexts and corresponding probability density functions needed, which are not efficiently managed.

Innovation Solution

The method uses preceding spectral coefficients to determine context classes through non-uniform quantization, reducing the number of contexts by grouping similar probability density functions into a single class, thereby decreasing latency and memory requirements.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of information

If a large number of contexts and corresponding probability density functions are used for arithmetic coding, then compression efficiency is improved, but encoding/decoding latency and memory capacity requirements increase

Engineering Contradiction:

Improvecompression efficiencyVSAvoidencoding/decoding latency

Core Design Contradiction:

Loss of informationVSLoss of time

Solution Approach 1:

The patent merges multiple contexts that share common characteristics into a single unified context. Instead of maintaining separate probability density functions for each individual context, the invention combines similar contexts and applies a single PDF to multiple contexts, thereby reducing the total number of PDFs required while maintaining compression efficiency.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The patent creates a universal context structure where a single probability density function can serve multiple contexts. The unified context is designed to be applicable across different context types, allowing one PDF to perform the function of multiple specialized PDFs, thus reducing memory requirements and processing latency.

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Loss of information

If a large number of contexts and corresponding probability density functions are used for arithmetic coding, then compression efficiency is improved, but memory capacity requirements increase

Engineering Contradiction:

Improvecompression efficiencyVSAvoidmemory capacity requirements

Core Design Contradiction:

Loss of informationVSQuantity of substance

Solution Approach 1:

The patent merges multiple contexts that share common characteristics into a single unified context. Instead of maintaining separate probability density functions for each individual context, the invention combines similar contexts and applies a single PDF to multiple contexts, thereby reducing the total number of PDFs required while maintaining compression efficiency.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The patent creates a universal context structure where a single probability density function can serve multiple contexts. The unified context is designed to be applicable across different context types, allowing one PDF to perform the function of multiple specialized PDFs, thus reducing memory requirements and processing latency.

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Loss of information

If a large number of contexts and corresponding probability density functions are used for arithmetic coding, then compression efficiency is improved, but device complexity increases

Engineering Contradiction:

Improvecompression efficiencyVSAvoidcomplexity of context management

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The patent merges multiple contexts that share common characteristics into a single unified context. Instead of maintaining separate probability density functions for each individual context, the invention combines similar contexts and applies a single PDF to multiple contexts, thereby reducing the total number of PDFs required while maintaining compression efficiency.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The patent creates a universal context structure where a single probability density function can serve multiple contexts. The unified context is designed to be applicable across different context types, allowing one PDF to perform the function of multiple specialized PDFs, thus reducing memory requirements and processing latency.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20260058672A1Method and device for arithmetic encoding or arithmetic decoding

Publication Date: 2026.02.26 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.