Image Compression with Artificial Neural Networks

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 189)

Abstract

In this paper, we make an experimental study of some techniques of image compression based on artificial neural networks, particularly algorithm based on back-propagation gradient error [5]. We also present a new hybrid method based on the use of a multilayer perceptron which combines hierarchical and adaptative schemes. The idea is to compute in a parallel way, the back propagation algorithm on an adaptative neural network that uses sub-neural networks with a hierarchical structure to classify the image blocks in entry according to their activity. The results come from the Yann Le Cun database [7], and show that the proposed hybrid method gives good results in some cases.

Keywords

neural networks image compression and coding back-propagation 

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References

  1. 1.
    Benamrane, N., Benhamed Daho, Z., Shen, J.: Compression des images medicales par reseaux de neurones. USTO, Traitement Du Signal 6, 631–638 (1998)Google Scholar
  2. 2.
    Benbenisti, et al.: New simple three-layer neural network for image compression. Opt. Eng. 36, 1814–1817 (2000)CrossRefGoogle Scholar
  3. 3.
    Carrato, S.: Neural networks for image compression. In: Neural Networks: Adv. and Appli., 2nd edn., vol. 2, pp. 177–198. Gelende Pub. North-Holland, Amsterdam (1992)Google Scholar
  4. 4.
    de Bodt, E., Cottrell, M., Verleysen, M.: Using the Kohonen algorithm for quick initialisation of simple competitive learning algorithm. In: European Symposium on Artificial Neural Networks (2001)Google Scholar
  5. 5.
    Jiang, J.: Images compression with neural networks, A Survey. Signal Processing: Image Communication (1998)Google Scholar
  6. 6.
    Karayiannis, N.B., Pai, P.I.: Fuzzy vector quantization algorithm and their application in image compression. IEEE Trans. Image Process. 4(9), 1193–1202 (2008)CrossRefGoogle Scholar
  7. 7.
    Le-Cun, Y.: A competitive learning method for asymmetric threshold network. In: COGNITIVA 1985, Paris, June 4-7 (1985)Google Scholar
  8. 8.
    Mallat, S.G.: A Wavelet Tour of Signal Processing, pp. 145–150. Academic Press (1999)Google Scholar
  9. 9.
    Mallat, S.G.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11(7), 674–693 (1989)MATHCrossRefGoogle Scholar
  10. 10.
    Namphol, A., Chin, S., Arozullah, M.: Image compression with a hierarchical neural network. IEEE Trans. Aerospace Electronic Systems 32(1), 326–337 (1996)CrossRefGoogle Scholar
  11. 11.
    Rabbani, M., Jones, P.W.: Digital Image Compression Techniques. Tutorial Texts. SPIE Optical Engineering Press (1991)Google Scholar
  12. 12.
    Ramel, M.J.Y., Agen, F., Michot, J.: Fractal compression, Jacquin methods, triangular subdivisions and Delaunay. EPUT, Depts.-Info (2004)Google Scholar
  13. 13.
    Zhang, L., et al.: Generating and conding of fractal graphs by neural network and mathematical morphology methods. IEEE Trans. Neural Networks 7(2), 400–407 (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of Yaounde IYaoundeCameroon

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