Skip to main content

Extension and Faster Implementation of the GRP Transform for Lossless Compression

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6129))

Abstract

The GRP transform, or the generalized radix permutation transform was proposed as a parametric generalization of the BWT of the block-sorting data compression algorithm. This paper develops its extension that can be applied with any combination of parameters. By using the technique developed for linear time/space implementation of the sort transform, we propose an efficient implementation for the inverse transformation of the GRP transform. It works for arbitrary parameter values, and can convert the transformed string to the original string in time linear in the string length.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Adjeroh, D., Bell, T., Mukherjee, A.: The Burrows-Wheeler Transform: Data Compression, Suffix Arrays, and Pattern Matching. Springer, Heidelberg (2008)

    Book  Google Scholar 

  2. Burrows, M., Wheeler, D.J.: A block-sorting lossless data compression algorithm. SRC Research Report 124, Digital Systems Research Center, Palo Alto (1994)

    Google Scholar 

  3. Inagaki, K., Tomizawa, Y., Yokoo, H.: Novel and generalized sort-based transform for lossless data compression. In: Karlgren, J., Tarhio, J., Hyyrö, H. (eds.) SPIRE 2009. LNCS, vol. 5721, pp. 102–113. Springer, Heidelberg (2009)

    Google Scholar 

  4. Inagaki, K., Tomizawa, Y., Yokoo, H.: Data compression experiment with the GRP transform (in Japanese). In: 32nd Sympo. on Inform. Theory and its Applications, SITA 2009, Yamaguchi, Japan, pp. 330–335 (2009)

    Google Scholar 

  5. Nong, G., Zhang, S.: Efficient algorithms for the inverse sort transform. IEEE Trans. Computers 56(11), 1564–1574 (2007)

    Article  MathSciNet  Google Scholar 

  6. Nong, G., Zhang, S., Chan, W.H.: Computing inverse ST in linear complexity. In: Ferragina, P., Landau, G.M. (eds.) CPM 2008. LNCS, vol. 5029, pp. 178–190. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  7. Schindler, M.: A fast block-sorting algorithm for lossless data compression. In: DCC 1997, Proc. Data Compression Conf, Snowbird, UT, p. 469 (1997)

    Google Scholar 

  8. Vo, B.D., Manku, G.S.: RadixZip: Linear time compression of token streams. In: Very Large Data Bases: Proc. 33rd Intern. Conf. on Very Large Data Bases, Vienna, pp. 1162–1172 (2007)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Yokoo, H. (2010). Extension and Faster Implementation of the GRP Transform for Lossless Compression. In: Amir, A., Parida, L. (eds) Combinatorial Pattern Matching. CPM 2010. Lecture Notes in Computer Science, vol 6129. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13509-5_30

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-13509-5_30

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13508-8

  • Online ISBN: 978-3-642-13509-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics