An unsupervised learning quantiser design for image compression in the wavelet domain using statistical modelling
by P. Arockia Jansi Rani, V. Sadasivam
International Journal of Signal and Imaging Systems Engineering (IJSISE), Vol. 4, No. 1, 2011

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.

Online publication date: Fri, 13-Mar-2015

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