Toward Real Time Fractal Image Compression Using Graphics Hardware

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3804)


In this paper, we present a parallel fractal image compression using the programmable graphics hardware. The main problem of fractal compression is the very high computing time needed to encode images. Our implementation exploits SIMD architecture and inherent parallelism of recently graphic boards to speed-up baseline approach of fractal encoding. The results we present are achieved on cheap and widely available graphics boards.


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  1. 1.
    Barnsley, M.F., Sloan, A.: Chaotic compression. Computer Graphics World (1987)Google Scholar
  2. 2.
    GPGPU. (Website),
  3. 3.
    Yuval, F.: Fractal Image Compression - Theory and Application. Springer, Heidelberg (1994)zbMATHGoogle Scholar
  4. 4.
    Beaumont, J.M.: Image data compression using fractal techniques. British Telecom Technol. Journal 9, 93–109 (1991)Google Scholar
  5. 5.
    Jacobs, E.W., Fisher, Y., Boss, R.D.: Image compression: A study of the iterated transform method. Signal Processing 29, 251–263 (1992)zbMATHCrossRefGoogle Scholar
  6. 6.
    Yuval, F.: Fractal image compression. Fractals: Complex Geometry, Patterns, and Scaling in Nature and Society 2, 347–361 (1994)zbMATHCrossRefGoogle Scholar
  7. 7.
    Saupe, D.: Accelerating fractal image compression by multi-dimensional nearest neighbor search. In: Storer, J.A., Cohn, M. (eds.) Proceedings DCC 1995 Data Compression Conference. IEEE Computer Society Press, Los Alamitos (1995)Google Scholar
  8. 8.
    Palazzari, P., Coli, M., Lulli, G.: Massively parallel processing approach to fractal image compression with near-optimal coefficient quantization. J. Syst. Archit. 45, 765–779 (1999)CrossRefGoogle Scholar
  9. 9.
    Venkatasubramanian, S.: The graphics card as a stream computer. In: SIGMOD-DIMACS Workshop on Management and Processing of Data Streams (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  1. 1.ISISLab – Dipartimento di Informatica ed Appl. “R.M. Capocelli”Università degli Studi di SalernoBaronissiItaly

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