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Text compression via alphabet re-representation.

P M. Long1, A I. Natsev, J S. Vitter

  • 1Department of Information Systems and Computer Science, National University of Singapore, Singapore, Singapore

Neural Networks : the Official Journal of the International Neural Network Society
|March 29, 2003
PubMed
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This study introduces alphabet re-representation for text compression, using character prediction properties to improve efficiency. An algorithm combining this with neural networks shows competitive performance against established methods.

Area of Science:

  • Computer Science
  • Information Theory
  • Machine Learning

Background:

  • Text compression is crucial for efficient data storage and transmission.
  • Traditional methods often lack adaptability to complex text patterns.
  • Predictive modeling is key to advanced compression techniques.

Purpose of the Study:

  • To introduce and evaluate a novel alphabet re-representation technique for text compression.
  • To leverage character predictive properties for enhanced compression ratios.
  • To integrate this technique with neural networks for practical application.

Main Methods:

  • Developing an alphabet re-representation scheme based on character prediction.
  • Implementing a compression algorithm utilizing this scheme and neural networks.

Related Experiment Videos

  • Comparing performance against standard compression algorithms (e.g., gzip, PPMC).

    • Main Results:

    • The proposed alphabet re-representation method, when combined with neural networks, achieves competitive compression performance.
    • The approach demonstrates potential for improving upon existing text compression techniques.
    • Performance was benchmarked against UNIX compress, gzip, PPMC, and another neural network method.

    Conclusions:

    • Alphabet re-representation offers a promising avenue for advancing text compression.
    • Neural networks effectively support this predictive character-based compression strategy.
    • The method shows viability for practical, high-performance data compression.