Adaptive Entropy Model Switching for Low-Bitrate Audio Coding
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Solution Overview
Problem
Current audio compression techniques, such as those used in Windows Media Audio and MP3, face challenges in efficiently processing high-quality audio due to resource constraints in computers and networks, leading to suboptimal bit rate management and quality trade-offs.
Innovation Solution
The use of selectively multiple entropy models in adaptive coding and decoding systems, which include entropy model switching and clustering based on cost metrics, allows for efficient bit rate reduction while maintaining high audio quality by optimizing probability distributions and VLC tables.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple entropy models are used to improve coding efficiency, then audio quality is improved, but device complexity increases
Solution Approach 1:
The patent segments the audio coding process by dividing it into multiple entropy model stages (first entropy model and second entropy model). Each stage handles different aspects of the audio data with specialized probability distributions and VLC tables, allowing the system to achieve high coding efficiency while managing complexity through modular organization of the coding process
Solution Approach 2:
The patent implements dynamic switching between different entropy models based on the characteristics of the audio data. The system adaptsively selects which entropy model to use at different coding stages, allowing the complexity of the device to remain manageable while achieving high audio quality through selective application of multiple models rather than using all models simultaneously
2Productivity
If multiple entropy models are used to reduce bit rate, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent segments the bit rate reduction process into multiple entropy modeling stages, where each stage contributes to overall compression efficiency. The first entropy model handles initial compression while the second entropy model provides additional compression for specific data characteristics, achieving high productivity through cumulative effect rather than requiring a single complex model
Solution Approach 2:
The patent applies partial action by using different levels of entropy modeling complexity for different portions of the audio data. Not all audio segments require the full complexity of multiple entropy models, so the system selectively applies the appropriate level of modeling to each segment, improving overall bit rate reduction while keeping device complexity manageable
Data Source
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.