Optimal parsing trees for run-length coding of biased data

Sharon Aviran, Paul H. Siegel, Jack K. Wolf

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

We study coding schemes which encode unconstrained sequences into run-length-limited (d, k)-constrained sequences. We present a general framework for the construction of such (d,k)-codes from variable-length source codes. This framework is an extension of the previously suggested bit stuffing, bit flipping, and symbol sliding algorithms. We show that it gives rise to new code constructions which achieve improved performance over the three aforementioned algorithms. Therefore, we are interested in finding optimal codes under this framework, optimal in the sense of maximal achievable asymptotic rates. However, this appears to be a difficult problem. In an attempt to solve it, we are led to consider the encoding of unconstrained sequences of independent but biased (as opposed to equiprobable) bits. Here, our main result is that one can use the Tunstall source coding algorithm to generate optimal codes for a partial class of (d,k)constraints.

Original languageEnglish (US)
Pages (from-to)841-849
Number of pages9
JournalIEEE Transactions on Information Theory
Volume54
Issue number2
DOIs
StatePublished - Feb 1 2008

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coding
symbol
performance

Keywords

  • (d,k) constraints
  • Bit stuffing
  • Optimal (d,k)-codes
  • Parsing trees
  • Source coding

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Library and Information Sciences

Cite this

Optimal parsing trees for run-length coding of biased data. / Aviran, Sharon; Siegel, Paul H.; Wolf, Jack K.

In: IEEE Transactions on Information Theory, Vol. 54, No. 2, 01.02.2008, p. 841-849.

Research output: Contribution to journalArticle

Aviran, Sharon ; Siegel, Paul H. ; Wolf, Jack K. / Optimal parsing trees for run-length coding of biased data. In: IEEE Transactions on Information Theory. 2008 ; Vol. 54, No. 2. pp. 841-849.
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