Compressing combinatorial objects
Information Theory
2016-01-15 v1 Combinatorics
math.IT
Abstract
Most of the world's digital data is currently encoded in a sequential form, and compression methods for sequences have been studied extensively. However, there are many types of non-sequential data for which good compression techniques are still largely unexplored. This paper contributes insights and concrete techniques for compressing various kinds of non-sequential data via arithmetic coding, and derives re-usable probabilistic data models from fairly generic structural assumptions. Near-optimal compression methods are described for certain types of permutations, combinations and multisets; and the conditions for optimality are made explicit for each method.
Cite
@article{arxiv.1601.03689,
title = {Compressing combinatorial objects},
author = {Christian Steinruecken},
journal= {arXiv preprint arXiv:1601.03689},
year = {2016}
}
Comments
8 pages