Open AccessDESIGN OF NEURAL NETWORK AS DATA FLOW MODEL FOR IMAGE COMPRESSION
Author(s) -
Daneshwari I. Hatti,
Savitri Raju,
Mahendra M. Dixit
Publication year - 2013
Publication title -
international journal of image processing and vision sciences
Language(s) - English
Resource type - Journals
ISSN - 2278-1110
DOI - 10.47893/ijipvs.2013.1037
Subject(s) - discrete cosine transform , computer science , image compression , artificial neural network , data compression , backpropagation , algorithm , bandwidth (computing) , matlab , artificial intelligence , computer engineering , image (mathematics) , image processing , computer network , operating system
In digital communication bandwidth is essential parameter to be considered. Transmission and storage of images requires lot of memory in order to use bandwidth efficiently neural network and Discrete cosine transform together are used in this paper to compress images. Artificial neural network gives fixed compression ratio for any images results in fixed usage of memory and bandwidth. In this paper multi-layer feedforward neural network has been employed to achieve image compression. The proposed technique divides the original image in to several blocks and applies Discrete Cosine Transform (DCT) to these blocks as a pre-process technique. Quality of image is noticed with change in training algorithms, convergence time to attain desired mean square error. Compression ratio and PSNR in dB is calculated by varying hidden neurons. The proposed work is designed using MATLAB 7.10. and synthesized by mapping on Vertex 5 in Xilinx ISE for understanding hardware complexity. Keywords - backpropagation, Discrete