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

Dominik Köppl, Gonzalo Navarro, and Nicola Prezza

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.