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An Efficient Coding Technique for Stochastic Processes.

Jesús E Garca1, Verónica A González-López1, Gustavo H Tasca1

  • 1Department of Statistics, University of Campinas, Rua Sérgio Buarque de Holanda, 651, Campinas, São Paulo 13083-859, Brazil.

Entropy (Basel, Switzerland)
|January 21, 2022
PubMed
Summary
This summary is machine-generated.

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This study combines Partition Markov Models (PMM) with Huffman coding for data compression. This approach reduces the bits needed for data representation, achieving over 10% compression for SARS-CoV-2 DNA sequences.

Area of Science:

  • Information Theory
  • Computer Science
  • Bioinformatics

Background:

  • Data compression is crucial for efficient information storage and transmission.
  • Traditional methods like Huffman coding rely on accurate data modeling.
  • Partition Markov Models (PMM) offer a more efficient way to model data compared to standard Markov models.

Purpose of the Study:

  • To investigate the combined efficiency of Partition Markov Models (PMM) and Huffman coding for data compression.
  • To demonstrate how PMM-based entropy estimation can minimize expected codeword length.
  • To evaluate the practical performance of this methodology on real-world biological data.

Main Methods:

  • Utilized Partition Markov Models (PMM) to define data characteristics with fewer parameters.
Keywords:
Bayesian Information CriterionHuffman codingentropypartition Markov models

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  • Applied Huffman coding to the data modeled by PMM for optimal bit allocation.
  • Estimated the entropy of the Markov process (Xt) using PMM estimation.

    • Main Results:

    • The combination of PMM and Huffman coding significantly reduces the number of bits required for data representation.
    • An estimator for the minimum expected codeword length per symbol was derived.
    • Achieved at least a 10.4% reduction in data size for SARS-CoV-2 DNA sequences.

    Conclusions:

    • Integrating PMM with Huffman coding provides an efficient data compression strategy.
    • This methodology effectively estimates data entropy and minimizes codeword length.
    • The approach shows practical utility, particularly for compressing biological sequence data.