Entropy Coding with Partitioned Syntax Elements and Mixed VLC-PIPE Streams
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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 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 manufacture
If Huffman codes with VLC tables are used, then implementation is efficient and easy to construct, but adaptation to time-varying source statistics is demanding in terms of algorithmic complexity and implementation costs
Solution Approach 1:
The invention segments the source symbols into multiple categories based on their probability characteristics. Each category is assigned a separate VLC table, allowing the system to adapt to time-varying statistics by selecting appropriate categories and their corresponding VLC tables, thus resolving the contradiction between ease of construction and adaptability.
Solution Approach 2:
The invention introduces dynamic adaptation mechanisms where the probability distribution and VLC table selection can change over time based on the observed source statistics. This allows the Huffman coding system to adapt to time-varying source statistics while maintaining the efficiency of VLC-based implementation.
2Adaptability or versatility
If arithmetic coding is used, then handling of adaptive and higher-order probability modeling is more consistent and adequate, but implementation complexity is substantially higher than VLC
Solution Approach 1:
The invention segments the probability modeling task into multiple categories, each handled by a dedicated VLC table. This segmentation allows the system to achieve adaptive probability modeling similar to arithmetic coding while maintaining the lower implementation complexity of VLC by avoiding a single complex arithmetic coding engine.
Solution Approach 2:
The invention applies different coding strategies (different VLC tables) to different segments of the data based on their local probability characteristics. This local quality approach allows adequate handling of adaptive probability modeling by matching the coding scheme to the local statistics, while keeping overall complexity manageable through the use of simpler VLC tables rather than a universal arithmetic coder.
3Measurement precision
If multiple sets of VLC tables are used for higher-order probability modeling, then modeling accuracy improves, but system complexity increases
Solution Approach 1:
The invention segments the probability modeling into hierarchical categories (first-order, second-order, etc.), where each level uses its own VLC table. This segmentation allows the system to achieve higher-order probability modeling accuracy while managing complexity by organizing VLC tables in a structured hierarchy rather than requiring all tables simultaneously.
Solution Approach 2:
The invention implements higher-order probability modeling partially by using multiple VLC tables only when necessary, rather than always using the most complex modeling approach. This allows the system to achieve improved modeling accuracy for difficult cases while maintaining moderate complexity for typical cases, thus resolving the contradiction between precision and device complexity.
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.