Spline Recurrent Neural Networks for Quad-Tree Video Coding

  • Lorenzo Topi
  • Raffaele Parisi
  • Aurelio Uncini
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2486)


In this paper a novel connectionist approach for video compression is presented. The basic idea is to extend to the temporal dimension the architecture used for the compression of still images. A multilayer perceptron (MLP) with infinite impulse response (IIR) synapses, embedded in a new quad-tree framework for video segmentation, is employed to take into account the video temporal dynamics. In order to reduce the computational burden and to improve the generalization performance, a flexible spline-based activation function, suitable for signal processing applications, has been used. Preliminary experimental results show that the proposed approach represents a viable alternative with respect to existing standards for high-quality video compression.


video compression flexible activation function spline neural networks recurrent neural networks 


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  1. [1]
    S. Y. Kung, J. N. Hwang, ”Neural Networks for Intelligent Multimedia Processing”, Proceedings of IEEE, Vol. 86, No. 6, pp. 1244–1272, June 1998.CrossRefGoogle Scholar
  2. [2]
    V.K. Goyal, ”Theoretical Foundations of Transform Coding”, IEEE Signal Processing Magazine, Vol. 18, No. 5, pp. 9–21, Sept. 2001.CrossRefGoogle Scholar
  3. [3]
    S. Hay kin, ”Neural Networks (A comprehensive Foundation)”, 2nd Edition, Prentice-Hall, 1999.Google Scholar
  4. [4]
    J. Woods, T. Naveen, ”A Filter Based Bit Allocation Scheme”, IEEE Trans. on Image Processing, Vol. 1, pp. 436–440, July 1992.Google Scholar
  5. [5]
    R.D. Dony, S. Haykin, ”Neural Networks to Image Compression”, Proceedings of IEEE, Vol. 83, No. 2, pp. 228–303, February 1992.Google Scholar
  6. [6]
    Cottrel G. W., Munro P., Zipser D., ”Image Compression by back propagation: an example of extensional programming”, in SHARKEY, N. E. (Ed.): ”Advances in cognitive science”, Ablex, Norwood, Nj., 1988.Google Scholar
  7. [7]
    G.L. Sicuranza, G. Ramponi, S. Marsi, ”Artificial Neural Networks for Image Compression”, Electronics Letters, Vol. 6, pp. 477–479, 1990.CrossRefGoogle Scholar
  8. [8]
    Parodi G., Passaggio F., ”Size-Adaptive Neural Network For Image Compression”, Int. Conf. on Image Processing, ICIP’ 94, Austin, TX, USA, 1994.Google Scholar
  9. [9]
    Piazza F., Smerilli S., Uncini A., Griffo M., Zunino R., ”Fast Spline Neural Networks for Image Compression”, WIRN-96, Proc. Of the 8th Italian Workshop on Neural Nets, Vietri sul Mare, Salerno, Italy, 23–25 May 1996.Google Scholar
  10. [10]
    Stefano Guarnieri, Francesco Piazza and Aurelio Uncini, ”Multilayer Feedforward Networks with AdaptiveSpline Activation Function”, IEEE Trans. On Neural Network, Vol. 10, No. 3, pp. 672–683, May 1999.CrossRefGoogle Scholar
  11. [11]
    Lorenzo Vecci, Francesco Piazza and Aurelio Uncini, ”Learning and Approximation Capabilities of Adaptive Spline Activation Function Neural Networks”, Neural Networks, Vol. 11, No. 2, pp 259–270, March 1998.CrossRefGoogle Scholar
  12. [12]
    Paolo Campolucci, Aurelio Uncini, Francesco Piazza and Bhaskar D. Rao, ”On-Line Learning Algorithms for Locally Recurrent Neural Networks”, IEEE Trans, on Neural Network, Vol. 10, No. 2, pp. 253–271 March 1999.CrossRefGoogle Scholar
  13. [13]
    R. Parisi, E. D. Di Claudio, G. Orlandi and B. D. Rao, ”Fast adaptive digital equalization by recurrent neural networks”, IEEE Trans. on Signal Processing, Special number on Neural Network applications, Vol. 45, No. 11, November 1997.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Lorenzo Topi
    • 1
  • Raffaele Parisi
    • 1
  • Aurelio Uncini
    • 1
  1. 1.INFOCOM dept.University of Rome ”La Sapienza”RomeItaly

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