### Multi Library Wavelet Neural Network for Lossless Image Compression

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#### Abstract

Apart from the existing technology on image compression represented by series of JPEG, MPEG and H.26x standards, new technology such as Wavelet Neural Networks (WNN) and genetic algorithms are being developed to explore the future of image coding. Successful applications of WNN in the case of function approximation become well established. This paper presents a direct solution method based on Multi Library WNN for color image compression and coding which consists to transform an RGB image into Luminance-Chrominance space and then segment the luminance in a set of m blocks n by n pixels. These blocks should be transferred row by row (1D input vector) to the input of our wavelet network. Every input vector will be considered as unknown functional mapping and then it will be approximated by the network. *Copyright © 2017 Praise Worthy Prize - All rights reserved.*

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S. Mallat. A wavelet tour of signal processing. Academic Press, 1998.

Q. Zang, Wavelet Network in Nonparametric Estimation. IEEE Trans. Neural Networks, 8(2):227-236, 1997

Q. Zang and A. Benveniste, Wavelet networks. IEEE Trans. Neural Networks, vol. 3, pp. 889-898, 1992.

H. Bourlard, Y. Kamp, Autoassociation by multilayer perceptrons and singular values decomposition, Biol. Cybernet. 59 291-294, 1988.

A. Averbuch, D. Lazar, M. Israeli, Image compression using wavelet transform and multiresolution decomposition, IEEE Trans. Image Process. 5 (1) (January 1996) 4-15.

G. Basil, J. Jiang, Experiments on neural network implementation of LBG vector quantization, Research Report, Department of Computer Studies, Loughborough University, 1997.

G. Candotti, S. Carrato et al., Pyramidal multiresolution source coding for progressive sequences, IEEE Trans. Consumer Electronics 40 (4) 789-795, November 1994.

S. Carrato, Neural networks for image compression, Neural Networks: Adv. and Appl. 2 ed., Gelenbe Pub, North-Holland, Amsterdam, pp. 177-198, 1992.

O.T.C. Chen et al., Image compression using self-organisation networks, IEEE Trans. Circuits Systems For Video Technol. 4 (5) 480-489, 1994.

M.F. Barnsley, L.P. Hurd, Fractal Image Compression, AK Peters Ltd, ISBN: 1-56881-000-8, 1993.

Y. Oussar. Réseaux d’ondelettes et réseaux de neurones pour la modélisation statique et dynamique de processus, Thèse de doctorat, Université Pierre et Marie Curie, juillet 1998.

C. Foucher and G. Vaucher. Compression d’images et réseaux de neurones, revue Valgo n°01-02, 17-19, Ardèche, 2001.

Q. Zhang,”Using Wavelet Network in Nonparametric Estimation”, IEEE Trans. Neural Network, Vol. 8, pp.227-236, 1997.

M.A Alimi, The Beta System: Toward a Change in Our Use of Neuro-Fuzzy Systems, International Journal of Management, Invited Paper, no. June, pp. 15-19, 2000.

C. Ben Amar, W. Bellil and A. Alimi. “Beta Function and its Derivatives: A New Wavelet Family,” Transactions on Systems, Signals & Devices Volume 1, Number 3. 2005-2006

J. Jiang. Image compressing with neural networks – A survey, Signal processing: Image communication, ELSEVIER, vol. 14, n°9, 1999, p. 737-760.

W. Bellil, M. Othmani and C. Ben Amar. “Initialization by Selection for Multi library Wavelet Neural Network Training” 3rd international workshop on Artificial Neural Networks and Intelligent Information Processing ANNIIP 07 in conjunction with International Conference on Informatics in Control, Automation & Robotics ICINCO 07, Anger France. INSTICC PRESS, ISBN: 978-972-8865-86-3, 2007, pp 30-37.

K. K. Gupta, R. Gupta, Wavelet Based Speckle Filtering of the SAR Images, (2006) International Review on Computers and Software (IRECOS), 1 (3), pp. 224-232.

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