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Grammar-based codes or Grammar-based compression are compression algorithms based on the idea of constructing a context-free grammar (CFG) for the string to be compressed. Examples include universal lossless data compression algorithms.^[1] To compress a data sequence $x=x_{1}\cdots x_{n}$ , a grammar-based code transforms $x$ into a context-free grammar $G$ . The problem of finding a smallest grammar for an input sequence is known to be NP-hard,^[2] so many grammar-transform algorithms are proposed from theoretical and practical viewpoints. Generally, the produced grammar $G$ is further compressed by statistical encoders like arithmetic coding.

Examples and characteristics

The class of grammar-based codes is very broad. It includes block codes, variations of the incremental parsing Lempel-Ziv code,^[3] the multilevel pattern matching (MPM) algorithm,^[4] and many other new universal lossless compression algorithms. Grammar-based codes are universal in the sense that they can achieve asymptotically the entropy rate of any stationary, ergodic source with a finite alphabet.

Practical algorithms

The compression programs of the following are available from external links.

Sequitur^[5] is a classical grammar compression algorithm that sequentially translates an input text into a CFG, and then the produced CFG is encoded by an arithmetic coder.
Re-Pair^[6] is a greedy algorithm using the strategy of most-frequent-first substitution. The compressive performance is powerful, although the main memory space requirement is very large.
GLZA,^[7] which constructs a grammar that may be reducible, i.e., contain repeats, where the entropy-coding cost of "spelling out" the repeats is less than the cost creating and entropy-coding a rule to capture them. (In general, the compression-optimal SLG is not irreducible, and the Smallest Grammar Problem is different from the actual SLG compression problem.)

References

^ Kieffer, J. C.; Yang, E.-H. (2000), "Grammar-based codes: A new class of universal lossless source codes", IEEE Trans. Inf. Theory, 46 (3): 737–754, doi:10.1109/18.841160
^ Charikar, M.; Lehman, E.; Liu, D.; Panigrahy, R.; Prabharakan, M.; Sahai, A.; Shelat, A. (2005), "The Smallest Grammar Problem", IEEE Trans. Inf. Theory, 51 (7): 2554–2576, doi:10.1109/tit.2005.850116
^ Kieffer, J. C.; Yang, E.-H.; Nelson, G.; Cosman, P. (2000), "Universal lossless compression via multilevel pattern matching", IEEE Trans. Inf. Theory, 46 (4): 1227–1245, doi:10.1109/18.850665
^ Ziv, J.; Lempel, A. (1978), "Compression of individual sequences via variable rate coding", IEEE Trans. Inf. Theory, 24 (5): 530–536, doi:10.1109/TIT.1978.1055934
^ Nevill-Manning, C. G.; Witten, I. H. (1997), "Identifying Hierarchical Structure in Sequences: A linear-time algorithm", Journal of Artificial Intelligence Research, 7 (4): 67–82, hdl:10289/1186
^ Larsson, N. J.; Moffat, A. (2000), "Offline Dictionary-Based Compression", IEEE, 88 (11): 1722–1732, doi:10.1109/5.892708
^ Conrad, Kennon J.; Wilson, Paul R. (2016), "Grammatical Ziv-Lempel Compression: Achieving PPM-Class Text Compression Ratios with LZ-Class Decompression Speed", IEEE Data Compression Conference, doi:10.1109/DCC.2016.119

External links

GLZA discussion and paper
Description of grammar-based codes with example
Sequitur codes
Re-Pair codes
Re-Pair codes a version of Gonzalo Navarro.
GrammarViz 2.0 - implementation of Sequitur, Re-Pair, and parallel Re-Pair in Java.

[1] Kieffer, J. C.; Yang, E.-H. (2000), "Grammar-based codes: A new class of universal lossless source codes", IEEE Trans. Inf. Theory, 46 (3): 737–754, doi:10.1109/18.841160

[2] Charikar, M.; Lehman, E.; Liu, D.; Panigrahy, R.; Prabharakan, M.; Sahai, A.; Shelat, A. (2005), "The Smallest Grammar Problem", IEEE Trans. Inf. Theory, 51 (7): 2554–2576, doi:10.1109/tit.2005.850116

[3] Kieffer, J. C.; Yang, E.-H.; Nelson, G.; Cosman, P. (2000), "Universal lossless compression via multilevel pattern matching", IEEE Trans. Inf. Theory, 46 (4): 1227–1245, doi:10.1109/18.850665

[4] Ziv, J.; Lempel, A. (1978), "Compression of individual sequences via variable rate coding", IEEE Trans. Inf. Theory, 24 (5): 530–536, doi:10.1109/TIT.1978.1055934

[5] Nevill-Manning, C. G.; Witten, I. H. (1997), "Identifying Hierarchical Structure in Sequences: A linear-time algorithm", Journal of Artificial Intelligence Research, 7 (4): 67–82, hdl:10289/1186

[6] Larsson, N. J.; Moffat, A. (2000), "Offline Dictionary-Based Compression", IEEE, 88 (11): 1722–1732, doi:10.1109/5.892708

[7] Conrad, Kennon J.; Wilson, Paul R. (2016), "Grammatical Ziv-Lempel Compression: Achieving PPM-Class Text Compression Ratios with LZ-Class Decompression Speed", IEEE Data Compression Conference, doi:10.1109/DCC.2016.119

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 * [[SEQUITUR algorithm|Sequitur]]<ref>{{Citation | last = Nevill-Manning  | first =  C. G.  | last2 = Witten | first2 = I. H. | title = Identifying Hierarchical Structure in Sequences: A linear-time algorithm | journal =  Journal of Artificial Intelligence Research | volume = 7 | pages = 67–82 | year = 1997 | doi = | issue = 4 | hdl=10289/1186}}</ref> is a classical grammar compression algorithm that sequentially translates an input text into a CFG, and then the produced CFG is encoded by an arithmetic coder.
 * [[Re-Pair]]<ref>{{Citation | last = Larsson  | first =  N. J.  | last2 = Moffat | first2 = A. | title = Offline Dictionary-Based Compression | journal = IEEE | volume = 88 | pages = 1722–1732| year = 2000 | issue = 11 | doi=10.1109/5.892708}}</ref> is a greedy algorithm using the strategy of most-frequent-first substitution. The compressive performance is powerful, although the main memory space requirement is very large.
-* [[GLZA]],<ref>{{Citation | last = Conrad  | first =  Kennon J. | last2 = Wilson | first2 = Paul R. | title = Grammatical Ziv-Lempel Compression: Achieving PPM-Class Text Compression Ratios with LZ-Class Decompression Speed | journal = IEEE Data Compression Conference (DCC) | year = 2016 | doi=10.1109/DCC.2016.119}}</ref> which constructs a grammar that may be reducible, i.e., contain repeats, where the entropy-coding cost of "spelling out" the repeats is less than the cost creating and entropy-coding a rule to capture them. (In general, the compression-optimal SLG is not irreducible, and the Smallest Grammar Problem is different from the actual SLG compression problem.)
+* [[GLZA]],<ref>{{Citation | last = Conrad  | first =  Kennon J. | last2 = Wilson | first2 = Paul R. | title = Grammatical Ziv-Lempel Compression: Achieving PPM-Class Text Compression Ratios with LZ-Class Decompression Speed | journal = IEEE Data Compression Conference | year = 2016 | doi=10.1109/DCC.2016.119}}</ref> which constructs a grammar that may be reducible, i.e., contain repeats, where the entropy-coding cost of "spelling out" the repeats is less than the cost creating and entropy-coding a rule to capture them. (In general, the compression-optimal SLG is not irreducible, and the Smallest Grammar Problem is different from the actual SLG compression problem.)
 ==See also==