Entropy Table Compression Using Interpolative Coding
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Solution Overview
Problem
Existing entropy encoding methods struggle to efficiently compress the symbol occurrence table used in entropy encoding, leading to increased overall compression size of the compressed sequence.
Innovation Solution
A decoding method that uses an integer value f to adaptively decode the table of symbol occurrences, involving the calculation of cumulative tables and iterative decoding of entries within subdivided decoding ranges, allowing for efficient compression of the symbol occurrence table.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the symbol occurrence table is included in the compressed sequence, then lossless decoding is enabled, but the overall compression size increases
Solution Approach 1:
The patent extracts only the essential information needed to reconstruct the symbol occurrence table by encoding it as a single integer value f derived from the cumulative frequency array. This extracted representation is then stored in the compressed sequence instead of the full table, enabling lossless reconstruction while minimizing space usage.
Solution Approach 2:
The patent transforms the symbol occurrence table from its original multi-element array form into a single integer parameter f through mathematical transformation (computing cumulative frequencies and encoding them). This parameter change allows the table to be represented in a compact form that fits within the compressed sequence without losing reconstruction capability.
2Quantity of substance
If a compact integer encoding of the symbol occurrence table is used, then compression size is reduced, but decoding complexity increases
Solution Approach 1:
The patent performs preliminary action by pre-computing the cumulative frequency array and deriving the integer encoding f during the encoding phase. This preparation work is done upfront so that during decoding, the receiver only needs to perform the reverse mathematical operations to reconstruct the table, rather than dealing with the full table complexity.
Solution Approach 2:
The patent introduces an intermediary mathematical transformation process that converts between the compact integer representation and the full symbol occurrence table. This intermediary mechanism (the cumulative frequency computation and reversal algorithm) acts as a bridge that simplifies the decoding process compared to directly storing and transmitting the full table.
Data Source
AI summary
A symbol sequence is entropy-encoded using a table of symbol occurrences, and an integer value f is used to encode the table of symbol occurrences. The integer value f is used to adaptively decode the table of symbol occurrences, including (i) decoding each entry of a cumulative table of symbol occurrences by successively subdividing decoding ranges of the cumulative table of symbol occurrences at their respective middle indexes and, for each decoding range: calculating, from decoded entries of the cumulative table of symbol occurrences, an entry at a first index+f mod(an entry at a last index−the entry at the first index+1) to decode an entry at the respective middle index of the decoding range, and calculating f div(the entry at the last index−the entry at the first index+1) to update f; (ii) calculating the table of symbol occurrences from the decoded entries of the cumulative table of symbol occurrences; and applying the decoded table of symbol occurrences to entropy-decode the received encoded symbol sequence.