Title: Vector quantisation-based neuro-wavelet model with cumulative distribution function for efficient image compression
Authors: Arun Vikas Singh; K. Srikanta Murthy
Addresses: Department of ECE, P.E.S. Institute of Technology, 100 feet ring road, BSK 3rd Stage, Bangalore – 560 097, Karnataka, India ' Department of CSE, P.E.S. School of Engineering, Hosur road, Bangalore – 560 100, Karnataka, India
Abstract: An efficient image compression technique is required for storage and transmission of raw images that need enormous amounts of disk space. The compression algorithms for different types of images take a longer time to converge when they are compressed using radial basis function neural network (RBFNN) along with wavelet. The convergence of the network as well as the compression ratio can be improved, by estimating cumulative distribution function (CDF) for the image and CDF is used to map the image pixels. The main contribution of this paper is in developing a compression technique that combines the specific features of wavelet transform, RBFNN and vector quantisation using CDF. The distinct way in which the low and high frequency components are handled in this paper, makes it an efficient technique for compression. It is been demonstrated that the RBFNN, along with wavelet, not only yields better peak signal to noise ratio at high compression ratio but also reduces computation time when the mapped image pixels are used in relation to the unmapped image pixels.
Keywords: image compression; radial basis function; RBF neural networks; RBFNN; cumulative distribution function; CDF; vector quantisation; neuro-wavelet models; wavelets; peak SNR; signal to noise ratio; PSNR.
International Journal of Computer Applications in Technology, 2013 Vol.48 No.2, pp.106 - 119
Published online: 24 Aug 2013 *Full-text access for editors Access for subscribers Purchase this article Comment on this article