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Abstract
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Compression algorithms are methods that reduce the number of symbols used to represent source information, therefore reducing the amount of space needed to store the source information or the amount of time necessary to transmit it for a given channel capacity. This paper presents a neural network based technique and wavelet based compression. A three layered Back propagation Neural Network (BPNN) was designed for building image compression system. The Back propagation neural network algorithm (BP) was Used for training the designed BPNN.
Keywords: Compression, Back Propagation Network, Neural Network##plugins.themes.academic_pro.article.details##
How to Cite
Ashu Bansal, N. (2014). Back Propagation Neural Network Based Image Compression. International Journal of Emerging Trends in Science and Technology, 1(07). Retrieved from https://igmpublication.org/ijetst.in/index.php/ijetst/article/view/350
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2. Juha Vesanto, Johan Himberg, Esa Alhoniemi, Juha Parhankangas,“Self-organizing map in Matlab: the SOM Toolboxâ€, Proceeding of the MatLab DSP Conference pp. 35-39, Nov. 1999.
3. Martin T. Hagan, Howard B. Demuth, Mark Beale, “Neural Network Designâ€, China Machine Press, 2002.
4. Himavathi, Anitha, Muthuramalingam,“Feed forward Neural Network Implementation in FPGA Using Layer Multiplexing for Effective Resource Utilizationâ€, Neural Networks, IEEE Transactions-2007
5. Rafael Gadea, Franciso Ballester, Antonio MocholÃ, JoaquÃn Cerdá,“Artificial neural network implementation on a single FPGA of a pipelined on-line backpropagationâ€, ISSS Proceedings of the 13th international symposium on System synthesis, IEEE Computer Society 2000.
6. S. Areepongsa, N. Kaewkamnerd, Y. F. Syed, K. R. Rao, "Wavelet Based Compression for Image Retrieval Systems".