Entropy Encoding Partitioning for Adaptive Syntax Compression
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Solution Overview
Problem
Current entropy coding schemes, such as Huffman codes and arithmetic coding, face challenges in adapting to time-varying source statistics and higher-order probability modeling, leading to inefficiencies in compression efficiency and complexity.
Innovation Solution
The proposed solution involves decomposing syntax elements into multiple source symbols and categorizing them into substreams for VLC and PIPE coding, allowing for better adaptation to statistics and improved compression efficiency through separate encoding and decoding processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If Huffman codes with VLC tables are used for entropy coding, then implementation is simple and efficient, but adaptation to time-varying source statistics is demanding in terms of algorithmic complexity and implementation costs
Solution Approach 1:
The source symbols are divided into multiple substreams based on their statistical characteristics. Each substream is encoded separately using VLC tables, allowing simple implementation while adapting to different statistical patterns through the segmentation itself rather than through complex adaptive mechanisms.
Solution Approach 2:
The invention changes the parameter of symbol probability distribution by modeling different substreams with different probability characteristics. This allows the coding system to adapt to time-varying statistics by selecting appropriate VLC tables for each substream based on its statistical properties, rather than requiring the entire system to adapt dynamically.
2Productivity
If arithmetic coding is used to handle adaptive and higher-order probability modeling, then compression efficiency is improved, but implementation complexity increases substantially compared to VLC
Solution Approach 1:
The source sequence is segmented into multiple substreams with distinct statistical characteristics. Each substream can be encoded using simpler VLC methods, avoiding the need for complex arithmetic coding while maintaining good compression efficiency by treating each substream independently according to its own statistics.
Solution Approach 2:
Instead of applying complex adaptive probability modeling to the entire source sequence, the invention applies simpler coding methods to partial sequences (substreams) that exhibit similar statistical properties. This partial application of simplified methods achieves acceptable compression efficiency without the full complexity burden.
3Measurement precision
If multiple sets of VLC tables are used for higher-order probability modeling, then coding accuracy is improved, but implementation complexity and memory requirements increase
Solution Approach 1:
The source is divided into substreams based on statistical characteristics, and each substream uses its own VLC table. This segmentation approach achieves accurate probability modeling for each substream independently, avoiding the need for a single complex multi-dimensional VLC table that would require excessive memory and complexity.
Solution Approach 2:
Different VLC tables are used for different substreams based on their local statistical properties. Each substream receives customized coding parameters appropriate to its specific characteristics, achieving high modeling accuracy locally without requiring a globally complex solution.
Data Source
AI summary
Decomposing a value range of the respective syntax elements into a sequence of n partitions with coding the components of z laying within the respective partitions separately with at least one by VLC coding and with at least one by PIPE or entropy coding is used to greatly increase the compression efficiency at a moderate coding overhead since the coding scheme used may be better adapted to the syntax element statistics. Accordingly, syntax elements are decomposed into a respective number n of source symbols si with i=1 . . . n, the respective number n of source symbols depending on as to which of a sequence of n partitions into which a value range of the respective syntax elements is sub-divided, a value z of the respective syntax elements falls into, so that a sum of values of the respective number of source symbols si yields z, and, if n>1, for all i=1 . . . n−1, the value of si corresponds to a range of the ith partition.