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A Tutorial on Hierarchical Lossless Data Compression

  • John C. Kieffer
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 46)

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.

Keywords

Production Rule Compression Scheme Compression Performance Incoming Edge Pointer Tree 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science + Business Media, Inc. 2002

Authors and Affiliations

  • John C. Kieffer
    • 1
  1. 1.Department of Electrical and Computer EngineeringUniversity of MinnesotaMinneapolis

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