Entropy Encoding Partitioning for Adaptive Compression Tradeoffs
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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 an entropy encoding apparatus that decomposes syntax elements into multiple source symbols, with one subsequence being VLC encoded and another subsequence being PIPE or arithmetic encoded, allowing for better adaptation to syntax element statistics and improved compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If Huffman codes with VLC tables are used for entropy coding, then implementation is simple and easy to realize, but adaptation to time-varying source statistics is demanding in terms of algorithmic complexity and implementation costs
Solution Approach 1:
The patent segments the source symbols into multiple categories based on their statistical characteristics. Different VLC tables are constructed for different symbol categories, allowing each category to be encoded with an optimized table that adapts to its specific statistical properties while maintaining overall simplicity.
Solution Approach 2:
The patent implements dynamic adaptation by updating VLC tables based on time-varying source statistics. The system can switch between different VLC tables or update table entries adaptively as source statistics change over time, combining simplicity with adaptability.
2Adaptability or versatility
If arithmetic coding is used for entropy coding, then adaptation to time-varying source statistics and higher-order probability modeling is improved, but implementation complexity increases substantially
Solution Approach 1:
The patent uses pre-computed VLC tables that can be quickly switched between rather than performing complex arithmetic coding operations. These tables act as disposable lookup structures that provide adaptive encoding without the computational burden of arithmetic coding, achieving similar adaptability with much lower complexity.
3Measurement precision
If multiple sets of VLC tables are used for higher-order probability modeling, then modeling accuracy improves, but implementation complexity and memory requirements increase
Solution Approach 1:
The patent applies different VLC tables to different categories of source symbols based on their local statistical properties. Instead of using multiple tables for all symbols uniformly, each symbol category receives a specialized table optimized for its specific characteristics, improving modeling accuracy while minimizing the total number of tables required.
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.