Quantized Indexing: Beyond Arithmetic Coding
Ratko V. Tomic

TL;DR
Quantized Indexing offers a faster, more space-efficient universal entropy coding method with lower redundancy and improved speed over traditional arithmetic coding, especially for shorter outputs and less predictable data.
Contribution
This paper introduces Quantized Indexing, a novel enumerative coding method that surpasses arithmetic coding in speed and efficiency, with reduced redundancy and new algorithmic enhancements.
Findings
QI is 10-20 times faster than arithmetic coding.
Redundancy of QI is lower than arithmetic coding for short outputs.
QI reduces arithmetic precision, execution time, and table sizes by a factor of O(n).
Abstract
Quantized Indexing is a fast and space-efficient form of enumerative (combinatorial) coding, the strongest among asymptotically optimal universal entropy coding algorithms. The present advance in enumerative coding is similar to that made by arithmetic coding with respect to its unlimited precision predecessor, Elias coding. The arithmetic precision, execution time, table sizes and coding delay are all reduced by a factor O(n) at a redundancy below 2*log(e)/2^g bits/symbol (for n input symbols and g-bit QI precision). Due to its tighter enumeration, QI output redundancy is below that of arithmetic coding (which can be derived as a lower accuracy approximation of QI). The relative compression gain vanishes in large n and in high entropy limits and increases for shorter outputs and for less predictable data. QI is significantly faster than the fastest arithmetic coders, from factor 6 in…
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