FVQEOPT: Fast Vector Quantization Encoding with Orthogonal Polynomials Transform - Volume 5, Number 1 (Feb. 2013) - IJCTE
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General Information
Prof. Wael Badawy
Department of Computing and Information Systems Umm Al Qura University, Canada
I'm happy to take on the position of editor in chief of IJCTE. We encourage authors to submit papers concerning any branch of computer theory and engineering.
IJCTE 2013 Vol.5(1): 31-35 ISSN: 1793-8201
DOI: 10.7763/IJCTE.2013.V5.641

FVQEOPT: Fast Vector Quantization Encoding with Orthogonal Polynomials Transform

Krishnamoorthy R. and Punidha R.

Abstract—This paper deals with a design of new vector quantization algorithm for coding of color images in the Transform domain. In order to speed up the design issue, the feature of transform coding is combined with vector quantization. The transformed training set is obtained with the proposed integer Orthogonal Polynomials transform with reduced computational complexity. The proposed method then generates a single codebook for all the three color components, utilizing the inter-correlation property of the individual color plane as well as interactions among the color planes with the proposed transformation. In the codebook generation phase of the proposed vector quantization encoding, binary tree method is used to achieve considerable saving in codebook construction time. The vector encoding phase uses binary search and partial distortion elimination to further reduce the encoding time for finding closest codeword of an input vector. The experimental result shows that the proposed algorithm greatly reduces the encoding time. The proposed algorithm is also compared with existing standard LBG algorithm.

Index Terms—Vector quantization, orthogonal polynomials transform, binary tree method.

The authors are with Computer vision lab, Department of CSE, Anna University of Technology, Tiruchirappalli - 620 024, Tamil Nadu, India (e-mail: arkrish26@hotmail.com, br_punidha@yahoo.co.in).


Cite: Krishnamoorthy R. and Punidha R., "FVQEOPT: Fast Vector Quantization Encoding with Orthogonal Polynomials Transform," International Journal of Computer Theory and Engineering vol. 5, no. 1, pp. 31-35, 2013.

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