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

HOLZ: High-Order Entropy Encoding of {Lempel--Ziv} Factor Distances
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HOLZ：{Lempel--Ziv} 因子距离的高阶熵编码

DOI：
10.1109/dcc52660.2022.00016
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发表时间：
2022
期刊：
Proc. DCC
影响因子：
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通讯作者：
Dominik Koeppl and Gonzalo Navarro and Nicola Prezza
Dominik Koeppl and Gonzalo Navarro and Nicola Prezza
中科院分区：
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文献类型：
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作者：
Tomohiro I;Dominik Koeppl;Dominik Koeppl;Dominik Koeppl and Simon J. Puglisi and Rajeev Raman;Daiki Hashimoto and Diptarama Hendrian and Dominik Koeppl and Ryo Yoshinaka and Ayumi Shinohara;Christina Boucher and Dominik Koeppl and Herman Perera and Massimiliano Rossi;Hideo Bannai and Keisuke Goto and Masakazu Ishihata and Shunsuke Kanda and Dominik Koeppl and Takaaki Nishimoto;Paolo Ferragina and Giovanni Manzini and Travis Gagie and Dominik Koeppl and Gonzalo Navarro and Manuel Striani and Francesco Tosoni;Koeppl Dominik;Koeppl Dominik;Dominik Koeppl and Gonzalo Navarro and Nicola Prezza

文献摘要

相似文献

我们提出了一种基于文本前缀的联合词典顺序的 Lempel-Ziv (LZ) 分解偏移量的新表示。所选择的偏移量倾向于接近k阶经验熵。我们的评估表明，这种选择优于高阶熵较小的数据集上的最右边和位最优的 LZ 解析。
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.