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Optimal Parsing Trees for Run-Length Coding of Biased Data
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由 S Aviran 著作被引用 28 次 — Abstract—We study coding schemes which encode uncon- strained sequences into run-length-limited (d, k)-constrained se- quences.
Optimal Parsing Trees for Run-Length Coding of Biased Data
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由 S Aviran 著作2008被引用 28 次 — Abstract: We study coding schemes which encode unconstrained sequences into run-length-limited (d, k)-constrained sequences.
Optimal Parsing Trees for Run-Length Coding of Biased Data
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由 S Aviran 著作2008被引用 28 次 — Abstract—We study coding schemes which encode unconstrained se- quences into run-length-limited (d; k)-constrained sequences. We present.
Optimal Parsing Trees for Run-Length Coding of Biased Data
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Here, the main result is that one can use the Tunstall source coding algorithm to generate optimal codes for a partial class of (d, k) constraints.
Optimal parsing trees for run-length coding of biased data
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We study coding schemes which encode unconstrained sequences into run-length-limited (d, k)-constrained sequences. We present a general framework for the ...
2001 International Press
The Chinese University of Hong Kong
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由 J Abrahams 著作2001被引用 143 次 — In variable-length-to-fixed binary source coding, a parse tree is used to associate a string of binary source symbols with each of a set of J code symbols. A ...
university of california, san diego
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Aviran, P. H. Siegel, and J. K. Wolf, “Optimal parsing trees for run-length coding of biased data,” submitted to IEEE Trans. Inform. Theory, Jan. 2006 ...
Sharon Aviran
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Optimal parsing trees for run-length coding of biased data. S Aviran, PH Siegel, JK Wolf. IEEE transactions on information theory 54 (2), 841-849, 2008. 28 ...
[PDF] Code and parse trees for lossless source encoding
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Optimal Parsing Trees for Run-Length Coding of Biased Data · S. AviranP. SiegelJ. Wolf. Computer Science. IEEE Transactions on Information Theory. 2008. TLDR.
Papers - Computational RNA Biology and Medicine - UC Davis
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Optimal parsing trees for run-length coding of biased data. S. Aviran, P.H. Siegel, and J.K. Wolf IEEE Transactions on Information Theory, 54, 841-849, 2008.