Keywords and Synonyms
LZ compression ; Ziv–Lempel compression ; Parsing-based compression
Problem Definition
The problem of lossless data compression is the problem of compactly representing data in a format that admits the faithful recovery of the original information. Lossless data compression is achieved by taking advantage of the redundancy which is often present in the data generated by either humans or machines.
Dictionary-based data compression has been “the solution” to the problem of lossless data compression for nearly 15 years. This technique originated in two theoretical papers of Ziv and Lempel [15,16] and gained popularity in the “80s” with the introduction of the Unix tool compress (1986) and of the gifimage format (1987). Although today there are alternative solutions to the problem of lossless data compression (e. g., Burrows-Wheeler compression and Prediction by Partial Matching), dictionary-based compression is still widely used in everyday applications: consider...
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Arroyuelo, D., Navarro, G., Sadakane, K.: Reducing the space requirement of LZ-index. In: Proc. 17th Combinatorial Pattern Matching conference (CPM), LNCS no. 4009, pp. 318–329, Springer (2006)
Charikar, M., Lehman, E., Liu, D., Panigraphy, R., Prabhakaran, M., Sahai, A., Shelat, A.: The smallest grammar problem. IEEE Trans. Inf. Theor. 51, 2554–2576 (2005)
Cormode, G., Muthukrishnan, S.: Substring compression problems. In: Proc. 16th ACM-SIAM Symposium on Discrete Algorithms (SODA '05), pp. 321–330 (2005)
Crochemore, M., Landau, G., Ziv-Ukelson, M.: A subquadratic sequence alignment algorithm for unrestricted scoring matrices. SIAM J. Comput. 32, 1654–1673 (2003)
Ferragina, P., Manzini, G.: Indexing compressed text. J. ACM 52, 552–581 (2005)
Kosaraju, R., Manzini, G.: Compression of low entropy strings with Lempel–Ziv algorithms. SIAM J. Comput. 29, 893–911 (1999)
Krishnan, P., Vitter, J.: Optimal prediction for prefetching in the worst case. SIAM J. Comput. 27, 1617–1636 (1998)
Lifshits, Y., Mozes, S., Weimann, O., Ziv-Ukelson, M.: Speeding up HMM decoding and training by exploiting sequence repetitions. Algorithmica to appear doi:10.1007/s00453-007-9128-0
Matias, Y., Şahinalp, C.: On the optimality of parsing in dynamic dictionary based data compression. In: Proceedings 10th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA '99), pp. 943–944 (1999)
Navarro, G.: Indexing text using the Ziv–Lempel trie. J. Discret. Algorithms 2, 87–114 (2004)
Navarro, G., Tarhio, J.: LZgrep: A Boyer-Moore string matching tool for Ziv–Lempel compressed text. Softw. Pract. Exp. 35, 1107–1130 (2005)
Şahinalp, C., Rajpoot, N.: Dictionary-based data compression: An algorithmic perspective. In: Sayood, K. (ed.) Lossless Compression Handbook, pp. 153–167. Academic Press, USA (2003)
Salomon, D.: Data Compression: the Complete Reference, 4th edn. Springer, London (2007)
Savari, S.: Redundancy of the Lempel–Ziv incremental parsing rule. IEEE Trans. Inf. Theor. 43, 9–21 (1997)
Ziv, J., Lempel, A.: A universal algorithm for sequential data compression. IEEE Trans. Inf. Theor. 23, 337–343 (1977)
Ziv, J., Lempel, A.: Compression of individual sequences via variable-length coding. IEEE Trans. Inf. Theor. 24, 530–536 (1978)
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© 2008 Springer-Verlag
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Gagie, T., Manzini, G. (2008). Dictionary-Based Data Compression. In: Kao, MY. (eds) Encyclopedia of Algorithms. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-30162-4_108
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DOI: https://doi.org/10.1007/978-0-387-30162-4_108
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