Context-Based Arithmetic Coding for N-Tuple Audio Compression
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current audio signal coding methods 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, performs escape code encoding/decoding, and uses probability models to encode/decode Most Significant Bits (MSBs) and Least Significant Bits (LSBs, with context reset and mapping units to adapt to frame changes, improving coding efficiency and memory usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If context-based arithmetic encoding is used to improve compression efficiency, then lossless compression performance is improved, but device complexity and memory requirements increase
Solution Approach 1:
The encoding apparatus segments the audio signal processing into distinct functional modules: a context determining unit that identifies contexts for N-tuples, an escape code encoding unit for specific cases, an MSB context determining unit, a probability model mapping unit, and separate MSB and LSB encoding units. This segmentation allows each module to perform a specific function, improving overall compression efficiency while organizing complexity into manageable units.
Solution Approach 2:
The patent applies different encoding strategies to different parts of the data based on local characteristics. The escape code encoding unit handles specific escape code cases differently from regular data, and the MSB and LSB are encoded using different contexts and probability models suited to their respective bit positions. This local differentiation optimizes compression for each data characteristic.
2Measurement precision
If multiple context models are used to improve encoding accuracy, then coding precision is improved, but memory requirements increase
Solution Approach 1:
The patent merges multiple context models into a unified probability model mapping unit that selects and applies appropriate contexts based on the current encoding state. Instead of maintaining separate complete context models in memory, the system combines multiple context determination logic into a single unit that dynamically selects the appropriate context, reducing memory requirements while maintaining coding precision.
Solution Approach 2:
The context selection is made dynamic rather than static. The context determining units dynamically identify which context to use based on the current N-tuple and encoding state, allowing the system to adapt to different data characteristics without pre-loading all possible context models into memory. This dynamic approach maintains precision while reducing memory footprint.
3Adaptability or versatility
If context reset and mapping units are added to adapt to frame changes, then adaptability is improved, but device complexity increases
Solution Approach 1:
The context reset unit performs preliminary actions by resetting contexts at the beginning of each frame or when appropriate, preparing the encoding state for new data. The context mapping unit also performs preliminary mapping of contexts based on frame characteristics before actual encoding begins. These preliminary actions enable the system to adapt to frame changes without adding complex runtime decision logic during the encoding process itself.
Data Source
AI summary
Disclosed are a context-based arithmetic encoding apparatus and method and a context-based arithmetic decoding apparatus and method. 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.