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HOLZ: High-Order Entropy Encoding of Lempel-Ziv Factor Distances

Citation Author(s):
Dominik Köppl, Gonzalo Navarro, Nicola Prezza
Submitted by:
Dominik Koeppl
Last updated:
18 February 2022 - 5:37am
Document Type:
Presentation Slides
Document Year:
2022
Event:
Categories:

Abstract

We propose a new representation of the offsets of the Lempel-Ziv (LZ) factorization
based on the co-lexicographic order of the text's prefixes.
The selected offsets tend to approach the k-th order empirical entropy.
Our evaluations show that this choice is superior to
the rightmost and bit-optimal LZ parsings on datasets with small high-order entropy.

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