Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
S. Y. Kung, J. N. Hwang, ”Neural Networks for Intelligent Multimedia Processing”, Proceedings of IEEE, Vol. 86, No. 6, pp. 1244–1272, June 1998.
V.K. Goyal, ”Theoretical Foundations of Transform Coding”, IEEE Signal Processing Magazine, Vol. 18, No. 5, pp. 9–21, Sept. 2001.
S. Hay kin, ”Neural Networks (A comprehensive Foundation)”, 2nd Edition, Prentice-Hall, 1999.
J. Woods, T. Naveen, ”A Filter Based Bit Allocation Scheme”, IEEE Trans. on Image Processing, Vol. 1, pp. 436–440, July 1992.
R.D. Dony, S. Haykin, ”Neural Networks to Image Compression”, Proceedings of IEEE, Vol. 83, No. 2, pp. 228–303, February 1992.
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.
G.L. Sicuranza, G. Ramponi, S. Marsi, ”Artificial Neural Networks for Image Compression”, Electronics Letters, Vol. 6, pp. 477–479, 1990.
Parodi G., Passaggio F., ”Size-Adaptive Neural Network For Image Compression”, Int. Conf. on Image Processing, ICIP’ 94, Austin, TX, USA, 1994.
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.
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.
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.
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.
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.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Topi, L., Parisi, R., Uncini, A. (2002). Spline Recurrent Neural Networks for Quad-Tree Video Coding. In: Marinaro, M., Tagliaferri, R. (eds) Neural Nets. WIRN 2002. Lecture Notes in Computer Science, vol 2486. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45808-5_9
Download citation
DOI: https://doi.org/10.1007/3-540-45808-5_9
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44265-3
Online ISBN: 978-3-540-45808-1
eBook Packages: Springer Book Archive