Adaptive Entropy Model Switching for Low-Bitrate Audio Coding
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Solution Overview
Problem
Current audio compression techniques are inefficient in reducing bit rates while maintaining high quality, as they often require significant computational resources and memory, especially when dealing with multiple entropy models and VLC tables.
Innovation Solution
The use of selectively adaptive entropy models that switch between multiple models to optimize resource usage, clustering probability distributions, and constraining less probable symbol values to common conditional distributions, reduces memory and computational requirements while maintaining encoding gains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple entropy models and VLC tables are used to improve encoding efficiency, then bit rate reduction is improved, but memory usage and computational resources increase
Solution Approach 1:
The patent segments the probability distributions into multiple clusters, where each cluster is represented by a codebook entry. This segmentation allows the system to use multiple entropy models selectively for different types of data patterns, improving encoding efficiency while managing memory usage through organized clustering
Solution Approach 2:
The patent implements dynamic switching between different entropy models based on the characteristics of the input data. The system adaptsively selects which entropy model to use for encoding different symbol sequences, allowing optimal compression performance while avoiding the need to maintain all models simultaneously in memory
2Productivity
If multiple entropy models and VLC tables are used to improve encoding efficiency, then bit rate reduction is improved, but computational resources increase
Solution Approach 1:
The system dynamically selects entropy models based on data characteristics, using adaptive probability distribution clustering to determine which model to apply. This dynamic approach improves encoding efficiency by matching the right model to the right data pattern while avoiding the computational overhead of continuously processing through multiple models
Solution Approach 2:
The patent uses partial action by applying entropy modeling selectively only when and where it provides benefit. The adaptive clustering approach identifies which symbol sequences benefit from entropy encoding and applies the appropriate model only to those cases, rather than uniformly applying multiple models to all data
3Quantity of substance
If less probable symbol values are constrained to common conditional distributions, then memory usage is reduced, but encoding precision may be affected
Solution Approach 1:
The patent applies different levels of probability distribution modeling to different symbol values. Less probable symbols are constrained to share common conditional distributions from codebook entries, while more probable symbols maintain their own specific distributions. This local differentiation reduces memory usage for rare symbols while preserving encoding precision for frequent symbols
Data Source
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AI summary
Techniques and tools for selectively using multiple entropy models in adaptive coding and decoding are described herein. For example, for multiple symbols, an audio encoder selects an entropy model from a first model set that includes multiple entropy models. Each of the multiple entropy models includes a model switch point for switching to a second model set that includes one or more entropy models. The encoder processes the multiple symbols using the selected entropy model and outputs results. Techniques and tools for generating entropy models are also described.