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Image Processing Compression and Reconstruction by Using New Approach Artificial Neural Network
K.Siva Nagi Reddy, B.R.Vikram, L. Koteswara Rao, B.Sudheer Reddy
Pages - 68 - 85     |    Revised - 15-03-2012     |    Published - 16-04-2012
Volume - 6   Issue - 2    |    Publication Date - April 2012  Table of Contents
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KEYWORDS
Artificial Neural Network, Multilayer Perception, Radial Basis Functions
ABSTRACT
In this paper a neural network based image compression method is presented. Neural networks offer the potential for providing a novel solution to the problem of data compression by its ability to generate an internal data representation. This network, which is an application of back propagation network, accepts a large amount of image data, compresses it for storage or transmission, and subsequently restores it when desired. A new approach for reducing training time by reconstructing representative vectors has also been proposed. Performance of the network has been evaluated using some standard real world images. It is shown that the development architecture and training algorithm provide high compression ratio and low distortion while maintaining the ability to generalize and is very robust as well.
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 Scribd 
5 SlideShare 
6 PdfSR 
1 R. P. Lippmann, “An introduction to computing with neural network”, IEEE ASSP mag., pp. 36-54, 1987.
2 M.M. Polycarpou, P. A. Ioannou, “Learning and Convergence Analysis of Neural Type Structured Networks”, IEEE Transactions on Neural Network, Vol 2, Jan 1992, pp.39-50.
3 K. R Rao, P. Yip, Discrete Cosine Transform Algorithms, Advantages, Applications, Academic Press, 1990
4 Rao, P.V. Madhusudana, S.Nachiketh,S.S.Keerthi, K. “image compression using artificial neural network”.EEE, ICMLC 2010, PP: 121-124.
5 Dutta, D.P.; Choudhury, S.D.; Hussain, M.A.; Majumder, S.; ”Digital image compression using neural network” .IEEE, international Conference on Advances in Computing, Control, Telecommunication Technologies, 2009. ACT '09.
6 N.M.Rahim, T.Yahagi, “Image Compression by new sub-image bloc Classification techniques using Neural Networks”, IEICE Trans. On Fundamentals, Vol. E83-A, No.10, pp 2040-2043, 2000.
7 M. S. Rahim, "Image compression by new sub- image block Classification techniques using neural network. IEICE Trans. On Fundamentals of Electronics, Communications, and Computer Sciences, E83-A (10), (2000), pp. 2040- 2043.
8 D. Anthony, E. Hines, D. Taylor and J. Barham, “A study of data compression using neural networks and principal component analysis, “in Colloquium on Biomedical Applications of Digital Signal Processing,1989, pp. 1–5.
9 G. L. Sicuranzi, G. Ramponi, and S. Marsi, “Artificial neural network for image compression,” Electronics Letters, vol. 26, no. 7, pp. 477– 479, March 29 1990.
10 M.Egmont-Petersen, D.de.Ridder, Handels, “Image Processing with Neural Networks – a review”, Pattern Recognition 35(2002) 2279-2301 [11] M. H. Hassoun, Fundamentals of Artificial Neural Networks, MIT Press, Cambridge, MA, 1995.
11 Wasserman, P.D., 1989. Neural Computing: Theory and Practice. Coriolis Group, New York, USA, ISBN: 10: 0442207433, pp: 230.
12 Durai S.A. And E.A. Saro, 2006. Image compression with back-propagation neural network using cumulative distribution function. World Acad. Sci. Eng. Technol., 17: 60-64.
13 Xianghong, T. And L. Yang, 2008. An image compressing algorithm based on classified blocks with BP neural networks. Proceeding of the International Conference on Computer Science and Software Engineering, Dec. 12-14, IEEE Computer Society, and Wuhan, Hubei pp: 819-822.
14 Veisi, H. And M. Jamzad, 2009. A complexity-based approach in image compression using neural networks. Int. J. Sign. Process. 5: 82-92.
15 B. M. Wilamowski, Y. Chen, A. Malinowski, “Efficient algorithm for training neural networks with one hidden layer,” In Proc. IJCNN, vol.3, pp.1725-728, 1999.
16 T. Cong Chen, D. Jian Han, F. T. K. Au, L. G.Than, “Acceleration of Levenberg-Marquardt training of neural networks with variable decay rate”, IEEE Trans. on Neural Net., vol. 3, no. 6, pp. 1873 - 1878, 2003. World Academy of Science, Engineering and Technology 6 2005.
Associate Professor K.Siva Nagi Reddy
montessori engineering college - India
sivanag1979@gmail.com
Dr. B.R.Vikram
- India
Dr. L. Koteswara Rao
- India
Dr. B.Sudheer Reddy
- India