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Image Compression Based on Soft Computing Techniques

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Book cover Parallel Processing and Applied Mathematics (PPAM 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3019))

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Abstract

In this paper a new algorithm for image compression, named predictive vector quantization (PVQ), is developed based on competitive neural networks quantizer and neural networks predictor. The modified closed-loop PVQ methodology is developed. The experimental results are presented and the performance of the algorithm is discussed. A comparison of two feed-forward neural network structures applied for predictor is discussed.

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, Czestochowa University of Technology, Al. Armii Krajowej 36, 42-200, Czestochowa, Poland

    Robert Cierniak

Authors
  1. Robert Cierniak
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Editor information

Editors and Affiliations

  1. Institute of Computational and Information Sciences, Czestochowa University of Technology,  

    Roman Wyrzykowski

  2. Computer Science Department, University of Tennessee, TN 37996-3450, Knoxville, USA

    Jack Dongarra

  3. Systems Research Institute, Polish Academy of Science, Warsaw, Poland

    Marcin Paprzycki

  4. Informatics & Mathematical Modeling, Technical University of Denmark, DK-2800, Lyngby, Denmark

    Jerzy Waśniewski

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© 2004 Springer-Verlag Berlin Heidelberg

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Cierniak, R. (2004). Image Compression Based on Soft Computing Techniques. In: Wyrzykowski, R., Dongarra, J., Paprzycki, M., Waśniewski, J. (eds) Parallel Processing and Applied Mathematics. PPAM 2003. Lecture Notes in Computer Science, vol 3019. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24669-5_80

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  • DOI: https://doi.org/10.1007/978-3-540-24669-5_80

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-21946-0

  • Online ISBN: 978-3-540-24669-5

  • eBook Packages: Springer Book Archive

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