Dynamic Topology Networks for Colour Image Compression

  • Ezequiel López-Rubio
  • José Muñoz-Pérez
  • José Antonio Gómez-Ruiz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2085)


The Self-Organizing Dynamic Graph (SODG) is a novel unsupervised neural network that overcomes some of the limitations of the Kohonen’s Self-Organizing Feature Map (SOFM) by using a dynamic topology among neurons. In this paper an application of the SODG to colour image compression is studied. A Huffman coding and the Lempel-Ziv algorithm are applied to the output of the SODG to provide considerable improvements in compression rates with respect to standard competitive learning. Furthermore, this system is shown to give mean squared errors of the reconstructed images similar to those of competitive learning. Experimental results are presented to illustrate the performance of this system.


Weight Vector Image Compression Compression Rate Competitive Learning Huffman Code 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Gray, R.M.: Vector Quantization. IEEE ASSP Magazine 1 (1980) 4–29.CrossRefGoogle Scholar
  2. 2.
    Ahalt, S.C., Krishnamurphy, A.K., Chen, P. and Melton, D.E.: Competitive Learning Algorithms for Vector Quantization. Neural Networks 3 (1990) 277–290.CrossRefGoogle Scholar
  3. 3.
    Yair, E., Zeger, K. and Gersho, A.: Competitive Learning and Soft Competition for Vector Quantizer Design. IEEE Trans. Signal Processing 40 (1992),No. 2, 294–308.CrossRefGoogle Scholar
  4. 4.
    Ueda, N. and Nakano, R.: A New Competitive Learning Approach Based on an Equidistortion Principle for Designing Optimal Vector Quantizers. Neural Networks 7 (1994), No. 8, 1211–1227.CrossRefGoogle Scholar
  5. 5.
    Linde, Y., Buzo, A. and Gray, R.M.: An Algorithm for Vector Quantizer Design. IEEE Trans. On Communications 28 (1980), No. 1, 84–95.CrossRefGoogle Scholar
  6. 6.
    Dony, R.D. and Haykin, S.: Neural Networks Approaches to Image Compression. Proceedings of the IEEE 83 (1995), No. 2, 288–303.CrossRefGoogle Scholar
  7. 7.
    Cramer, C.: Neural Networks for Image and Video Compression: A Review. European Journal of Operational Research 108 (1998), 266–282.zbMATHCrossRefGoogle Scholar
  8. 8.
    Kohonen, T.: The Self-Organizing Map. Proceedings of the IEEE 78 (1990), 1464–1480.CrossRefGoogle Scholar
  9. 9.
    Corridoni, J.M., Del Bimbo, A., and Landi, L.: 3D Object classification using multi-object Kohonen networks. Pattern Recognition 29 (1996), 919–935.CrossRefGoogle Scholar
  10. 10.
    Pham, D.T. and Bayro-Corrochano, E.J.: Self-organizing neural-network-based pattern clustering method with fuzzy outputs. Pattern Recognition 27 (1994), 1103–1110.CrossRefGoogle Scholar
  11. 11.
    Subba Reddy, N.V. and Nagabhushan, P.: A three-dimensional neural network model for unconstrained handwritten numeral recognition: a new approach. Pattern Recognition 31 (1998), 511–516.CrossRefGoogle Scholar
  12. 12.
    Wang, S.S. and Lin., W.G.: A new self-organizing neural model for invariant pattern recognition. Pattern Recognition 29 (1996), 677–687.CrossRefGoogle Scholar
  13. 13.
    Von der Malsburg, C.: Network self-organization. In Zornetzer, S.F., Davis J.L. and Lau C. (eds.): An Introduction to Neural and Electronic Networks. Academic Press, Inc. San Diego, CA (1990), 421–432.Google Scholar
  14. 14.
    López-Rubio, E., Muñoz-Pérez, J. and Gómez-Ruiz, J.A.: Self-Organizing Dynamic Graphs. Proceedings of the International Conference on Neural Networks and Applications 2001 (NNA’01), 24–28. N. Mastorakis (Ed.), World Scientific and Engineering Society Press.Google Scholar
  15. 15.
    Comstock, D. and Gobson, J.: Hamming coding of DCT compressed images over noisy channels. IEEE Transactions on Communications 32 (1984), 856–861.CrossRefGoogle Scholar
  16. 16.
    Wyner, A.D. and Ziv, J.: The sliding-window Lempel-Ziv algorithm is asymptotically optimal. Proceedings of the IEEE 82 (1994), 872–877.CrossRefGoogle Scholar
  17. 17.
    Wyner, A.D. and Wyner, A.J.: Improved redundancy of a version of the Lempel-Ziv algorithm. IEEE Transactions on Information Theory, 35 (1995), 723–731.CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Ezequiel López-Rubio
    • 1
  • José Muñoz-Pérez
    • 1
  • José Antonio Gómez-Ruiz
    • 1
  1. 1.Computer Science DepartmentUniversity of MálagaMálagaSpain

Personalised recommendations