Partitioned Entropy Coding for Syntax Elements With Mixed VLC and PIPE
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Solution Overview
Problem
Existing 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, then implementation is simple and efficient, but adaptation to time-varying source statistics is demanding and complex
Solution Approach 1:
The source symbols are divided into two subsequences: one encoded with VLC for simple implementation, and another encoded with PIPE/arithmetic coding for better adaptability. This segmentation allows each encoding method to handle appropriate portions of the data, resolving the contradiction between simplicity and adaptability.
Solution Approach 2:
Different encoding strategies are applied to different subsequences of source symbols based on their statistical characteristics. The VLC encoder handles symbols requiring simple implementation, while the PIPE/arithmetic encoder handles symbols requiring adaptive coding, optimizing local quality for each subset.
2Adaptability or versatility
If arithmetic coding is used, then adaptation to time-varying statistics and higher-order probability modeling is improved, but implementation complexity increases substantially
Solution Approach 1:
The data stream is segmented into two subsequences, with only the portion requiring high adaptability encoded using complex PIPE/arithmetic methods, while the rest uses simpler VLC. This reduces overall implementation complexity while maintaining adaptability where needed.
Solution Approach 2:
Instead of applying complex adaptive coding to all source symbols, the invention applies it partially only to the second subsequence where adaptability is most beneficial, avoiding unnecessary complexity in portions where simple VLC suffices.
3Device complexity
If PIPE coding is used, then complexity is reduced compared to arithmetic coding, but compression efficiency for highly skewed distributions is lower
Solution Approach 1:
The invention applies PIPE coding to the first subsequence where it provides sufficient compression efficiency at lower complexity, and reserves arithmetic coding for the second subsequence where highly skewed distributions require the superior compression efficiency of arithmetic methods.
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.