Title: An unsupervised learning quantiser design for image compression in the wavelet domain using statistical modelling

Authors: P. Arockia Jansi Rani, V. Sadasivam

Addresses: Department of Computer Science and Engineering, Manonmaniam Sundaranar University, Tirunelveli 627012, Tamil Nadu, India. ' Department of Computer Science and Engineering, Manonmaniam Sundaranar University, Tirunelveli 627012, Tamil Nadu, India

Abstract: Statistical modelling methods are becoming indispensable in today|s large-scale image analysis. In this paper, a novel algorithm for modelling code vectors of the codebook making use of Savitzky-Golay polynomial in the wavelet domain is proposed. The wavelet-transformed coefficients are subject to Vector Quantisation followed by Huffman Encoder. In the Quantisation process, initially a codebook is designed using an unsupervised greedy method. If the spatial distribution of the code vectors in the codebook is modelled statistically, better-reconstructed image quality may be obtained. The experimental results show the real effectiveness of the proposed method in terms of both compression ratio and quality.

Keywords: greedy method; unsupervised learning; codebook; polynomial regression; vector quantisation; wavelets; image compression; modelling; code vectors; image analysis; compression ratio; image quality.

DOI: 10.1504/IJSISE.2011.039183

International Journal of Signal and Imaging Systems Engineering, 2011 Vol.4 No.1, pp.35 - 41

Published online: 13 Mar 2015 *

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