IDEAS home Printed from https://ideas.repec.org/h/spr/isochp/978-0-306-48102-4_28.html
   My bibliography  Save this book chapter

A Tutorial on Hierarchical Lossless Data Compression

In: Modeling Uncertainty

Author

Listed:
  • John C. Kieffer

    (University of Minnesota)

Abstract

Hierarchical lossless data compression is a compression technique that has been shown to effectively compress data in the face of uncertainty concerning a proper probabilistic model for the data. In this technique, one represents a data sequence x using one of three kinds of structures: (1) a tree called a pointer tree, which generates x via a procedure called “subtree copying”; (2) a data flow graph which generates x via a flow of data sequences along its edges; or (3) a contextfree grammar which generates x via parallel substitutions accomplished with the production rules of the grammar. The data sequence is then compressed indirectly via compression of the structure which represents it. This article is a survey of recent advances in the rapidly growing field of hierarchical lossless data compression. In the article, we illustrate how the three distinct structures for representing a data sequence are equivalent, outline a simple method for designing compact structures for re presenting a data sequence, and indicate the level of compression performance that can be obtained by compression of the structure representing a data sequence.

Suggested Citation

  • John C. Kieffer, 2002. "A Tutorial on Hierarchical Lossless Data Compression," International Series in Operations Research & Management Science, in: Moshe Dror & Pierre L’Ecuyer & Ferenc Szidarovszky (ed.), Modeling Uncertainty, chapter 0, pages 711-733, Springer.
    • Handle: RePEc:spr:isochp:978-0-306-48102-4_28
    DOI: 10.1007/0-306-48102-2_28
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:isochp:978-0-306-48102-4_28. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.