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Tesi etd-02082012-012143


Thesis type
Tesi di laurea magistrale
Author
FARRUGGIA, ANDREA
URN
etd-02082012-012143
Title
Multi-Objective Optimization Design for LZSS
Struttura
SCIENZE MATEMATICHE, FISICHE E NATURALI
Corso di studi
INFORMATICA E NETWORKING
Commissione
relatore Prof. Ferragina, Paolo
relatore Prof. Frangioni, Antonio
controrelatore Prof. Prencipe, Giuseppe
Parole chiave
  • data compression
  • lzss
  • multiobjective compression
Data inizio appello
24/02/2012;
Consultabilità
parziale
Data di rilascio
24/02/2052
Riassunto analitico
In this thesis we explore the idea of controlling the LZSS compression ratio/decompression speed trade-off in a principled way.<br>Our approach lies on two ideas. <br>The first one is to model a LZSS parsing of a text T as a path on a (weighted) graph G(T), which has a vertex for each character in T and an edge for each phrase in the dictionary. <br>The second idea is to model explicitly the amount of compression space and decompression time taken up by a parsing through resource models. <br>Both ideas are not new: the notion of transposing LZ77 parsings to paths in a suitably defined graph G has been originally illustrated by Schuegraf, while Ferragina et al. labeled each edge of G with the cost in bits of the associated codeword. <br>In this thesis, we further extend G by labeling each edge with the encoding size in bits and the decoding time of the codeword, as given by the resource models.<br>This model allows us to define, in a precise way, the time-constrained LZSS parsing problem, namely, the problem of determining the parsing which minimizes the compressed size given a bound on the decompression time, as a constrained single source shortest path problem on G. <br>The proposed strategy to attack the problem is an heuristic based on the computation of the Lagrangian dual, i.e., the computation of the “best” Lagrangian relaxation of the time constraint.<br>In this way we reduce the problem to computing several times the bit-optimal LZSS parsing problem, so we can take advantage of its efficient resolution algorithms. <br>Next, we apply these ideas in the implementation of a compressor which employs the time-constrained LZSS strategy. <br>The treatment includes the description of a fast encoder and the derivation of an accurate time model for the target processor, a Core 2 Duo P8600.<br>The experimental results show that the idea is promising. In fact, the compressor exhibits remarkable performances in its capacity of controlling the time / space trade-off, mainly due to the high accuracy of the time model and the excellent performances of the Lagrangian Dual Heuristic, whose solutions are very close to the optimal one. <br>Moreover, results are impressive even on an absolute scale, since the compressor exhibits equal-or-better compression ratios than gzip and decompression times comparable with Snappy, the state-of-the-art of fast LZSS compressors.
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