Context-Based Arithmetic Coding for Audio N-Tuple Compression
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Solution Overview
Problem
Current audio coding technologies face inefficiencies in lossless compression and decoding, particularly in handling the sensitivity differences between low and high frequency bands, and in managing context-based encoding and decoding processes.
Innovation Solution
A context-based arithmetic encoding and decoding apparatus/method that determines contexts for N-tuples using neighborhood N-tuples, performs escape code encoding/decoding, and maps probability models to efficiently encode and decode audio signals, reducing memory requirements and improving coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If context-based arithmetic encoding is applied to audio signals, then coding efficiency is improved, but device complexity increases due to multiple context determining units and probability model mapping requirements
Solution Approach 1:
The encoding process is segmented into distinct functional units: N-tuple context determining unit for neighborhood-based context identification, MSB context determining unit for most significant bit context, escape code encoding unit for special case handling, and LSB encoding unit for least significant bits. This segmentation allows each unit to specialize in specific tasks, improving overall coding efficiency while making the complex system more manageable through modular architecture
Solution Approach 2:
The patent introduces a probability model mapping dimension that transforms context information into probability values for arithmetic encoding. By adding this dimensional transformation layer between context determination and actual encoding, the system achieves better compression ratios through more sophisticated probability estimation, while the modular structure prevents excessive complexity accumulation
2Measurement precision
If neighborhood N-tuples are used to determine context, then coding precision is improved through better probability estimation, but memory usage increases due to neighborhood data storage requirements
Solution Approach 1:
The system uses a limited number of neighborhood N-tuples (typically 2-4 neighboring samples) to determine context, rather than using all available historical data. This partial action approach provides sufficient probability estimation precision for effective compression while keeping memory requirements manageable by storing only the essential neighborhood information needed for context determination
Solution Approach 2:
The context determining units pre-process neighborhood N-tuple data to extract relevant contextual information before the actual arithmetic encoding occurs. By performing this preliminary extraction and storing only the essential context parameters rather than raw neighborhood data, the system achieves high coding precision while minimizing memory usage for storing context information
Data Source
AI summary
A context-based arithmetic encoding apparatus and method and a context-based arithmetic decoding apparatus and method are provided. The context-based arithmetic decoding apparatus may determine a context of a current N-tuple to be decoded, determine a Most Significant Bit (MSB) context corresponding to an MSB symbol of the current N-tuple, and determine a probability model using the context of the N-tuple and the MSB context. Subsequently, the context-based arithmetic decoding apparatus may perform a decoding on an MSB based on the determined probability model, and perform a decoding on a Least Significant Bit (LSB) based on a bit depth of the LSB derived from a process of decoding on an escape code.