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Journal of Electronic Imaging

Novel hybrid classified vector quantization using discrete cosine transform for image compression
Author(s): Ali Hassan Nasser Al-Fayadh; Abir Jaafar Hussain; Paulo Lisboa; Dhiya Al-Jumeily
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Paper Abstract

We present a novel image compression technique using a classified vector Quantizer and singular value decomposition for the efficient representation of still images. The proposed method is called hybrid classified vector quantization. It involves a simple but efficient classifier-based gradient method in the spatial domain, which employs only one threshold to determine the class of the input image block, and uses three AC coefficients of discrete cosine transform coefficients to determine the orientation of the block without employing any threshold. The proposed technique is benchmarked with each of the standard vector quantizers generated using the k-means algorithm, standard classified vector quantizer schemes, and JPEG-2000. Simulation results indicate that the proposed approach alleviates edge degradation and can reconstruct good visual quality images with higher peak signal-to-noise ratio than the benchmarked techniques, or be competitive with them.

Paper Details

Date Published: 1 April 2009
PDF: 13 pages
J. Electron. Imaging. 18(2) 023003 doi: 10.1117/1.3116564
Published in: Journal of Electronic Imaging Volume 18, Issue 2
Show Author Affiliations
Ali Hassan Nasser Al-Fayadh, Liverpool John Moores Univ. (United Kingdom)
Abir Jaafar Hussain, Liverpool John Moores Univ. (United Kingdom)
Paulo Lisboa, Liverpool John Moores Univ. (United Kingdom)
Dhiya Al-Jumeily, Liverpool John Moores Univ. (United Kingdom)


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