Arithmetic Coding Using Cost-Based Probability Model Selection

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Solution Overview

Problem

Existing entropy coding methods, such as VLC and AC, face challenges in accurately and efficiently updating symbol probabilities during the coding process, leading to delays in converging to real probabilities, especially when the source signal characteristics fluctuate.

Innovation Solution

An arithmetic coding method that dynamically updates the probability model by selecting the most suitable model from a set based on coding cost criteria, incorporating switching points to adapt the model in real-time, allowing for rapid convergence to real probabilities and improved coding efficiency.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If the probability model is updated regularly during coding to approach real probability, then the coding efficiency improves, but the convergence time increases when real probability fluctuates

Engineering Contradiction:

Improvecoding efficiencyVSAvoidconvergence time

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent implements dynamic probability model selection by maintaining multiple probability models and switching between them based on coding cost criteria. Instead of using a single static model or gradually updating one model, the system dynamically selects the most appropriate model at each coding step, enabling rapid adaptation to probability fluctuations while maintaining high coding efficiency.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The patent changes the parameter selection approach by introducing a coding cost criterion that evaluates multiple probability models. The system monitors coding costs associated with different models and switches models based on which provides the lowest coding cost, allowing rapid response to probability changes without the convergence delay inherent in gradual updates.

Inventive Principle:

Principle #35Parameter changes

2Device complexity

If a single probability model is used and updated gradually, then the device complexity is low, but the adaptability to signal characteristics deteriorates

Engineering Contradiction:

Improvemodel management complexityVSAvoidadaptability to signal characteristics

Core Design Contradiction:

Device complexityVSAdaptability or versatility

Solution Approach 1:

The patent segments the probability modeling function by maintaining multiple distinct probability models instead of one monolithic model. Each model can represent different signal characteristics or states, and the system selects the appropriate segment (model) based on current signal conditions, thereby improving adaptability while keeping individual model complexity manageable.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent creates a universal probability modeling system that can handle diverse signal characteristics through multiple models. The model selection mechanism based on coding cost criteria provides a universal approach that adapts to different signal types and conditions without requiring separate specialized processing for each case.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS8674859B2Methods for arithmetic coding and decoding and corresponding devices

Publication Date: 2014.03.18 INTERDIGITAL MADISON PATENT HLDG

AI summary

A method for arithmetic coding of symbols in a stream is described. The method comprises the following steps:coding a current symbol with a current probability model, andupdating the current probability model according to the coding of the current symbol.selecting the current probability model in a set of at least two probability models according to a coding cost criterion, andcoding an identifier of the selected probability model.