The Lempel-Ziv factorization (LZ77) and the Run-Length encoded Burrows-Wheeler Transform (RLBWT) are two important tools in text compression and indexing, being their sizes z and r closely related to the amount of text self-repetitiveness. In this paper we consider the problem of converting the two representations into each other within a working space proportional to the input and the output. Let n be the text length. We show that RLBW T can be converted to LZ77 in O(n log r) time and O(r) words of working space. Conversely, we provide an algorithm to convert LZ77 to RLBW T in O n(log r + log z) time and O(r + z) words of working space. Note that r and z can be constant if the text is highly repetitive, and our algorithms can operate with (up to) exponentially less space than naive solutions based on full decompression.
Policriti, Alberto; Prezza, Nicola. (2017). From LZ77 to the run-length encoded burrows-wheeler transform, and back. In 28th Annual Symposium on Combinatorial Pattern Matching, CPM 2017, Leibniz International Proceedings in Informatics, LIPIcs (pp. 1- 10). Isbn: 9783959770392. Doi: 10.4230/LIPIcs.CPM.2017.17. https://drops.dagstuhl.de/opus/portals/lipics/index.php?semnr=16037.
From LZ77 to the run-length encoded burrows-wheeler transform, and back
Prezza, Nicola
2017
Abstract
The Lempel-Ziv factorization (LZ77) and the Run-Length encoded Burrows-Wheeler Transform (RLBWT) are two important tools in text compression and indexing, being their sizes z and r closely related to the amount of text self-repetitiveness. In this paper we consider the problem of converting the two representations into each other within a working space proportional to the input and the output. Let n be the text length. We show that RLBW T can be converted to LZ77 in O(n log r) time and O(r) words of working space. Conversely, we provide an algorithm to convert LZ77 to RLBW T in O n(log r + log z) time and O(r + z) words of working space. Note that r and z can be constant if the text is highly repetitive, and our algorithms can operate with (up to) exponentially less space than naive solutions based on full decompression.| File | Dimensione | Formato | |
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