TY - CHAP

T1 - A Simpler Analysis of Burrows-Wheeler Based Compression.

AU - Lewenstein, Moshe

AU - Valiente, Gabriel

AU - Kaplan, Haim

AU - Landau, Shir

AU - Verbin, Elad

PY - 2006

Y1 - 2006

N2 - In this paper we present a new technique for worst-case analysis of compression algorithms which are based on the Burrows-Wheeler Transform. We deal mainly with the algorithm purposed by Burrows and Wheeler in their first paper on the subject [6], called bw0. This algorithm consists of the following three steps: 1) Compute the Burrows-Wheeler transform of the text, 2) Convert the transform into a sequence of integers using the move-to-front algorithm, 3) Encode the integers using Arithmetic code or any order-0 encoding (possibly with run-length encoding). We prove a strong upper bound on the worst-case compression ratio of this algorithm. This bound is significantly better than bounds known to date and is obtained via simple analytical techniques. Specifically, we show that for any input string s, and μ> 1, the length of the compressed string is bounded by μ·s Hk(s) + log(ζ(μ)) ·s + gk where Hk is the k-th order empirical entropy, gk is a consta

AB - In this paper we present a new technique for worst-case analysis of compression algorithms which are based on the Burrows-Wheeler Transform. We deal mainly with the algorithm purposed by Burrows and Wheeler in their first paper on the subject [6], called bw0. This algorithm consists of the following three steps: 1) Compute the Burrows-Wheeler transform of the text, 2) Convert the transform into a sequence of integers using the move-to-front algorithm, 3) Encode the integers using Arithmetic code or any order-0 encoding (possibly with run-length encoding). We prove a strong upper bound on the worst-case compression ratio of this algorithm. This bound is significantly better than bounds known to date and is obtained via simple analytical techniques. Specifically, we show that for any input string s, and μ> 1, the length of the compressed string is bounded by μ·s Hk(s) + log(ζ(μ)) ·s + gk where Hk is the k-th order empirical entropy, gk is a consta

UR - http://www.scopus.com/inward/record.url?scp=33746101839&partnerID=8YFLogxK

U2 - 10.1007/11780441_26

DO - 10.1007/11780441_26

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SN - 9783540354550

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 282

EP - 293

BT - Combinatorial Pattern Matching

PB - Springer Verlag

T2 - 17th Annual Symposium on Combinatorial Pattern Matching, CPM 2006

Y2 - 5 July 2006 through 7 July 2006

ER -