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Novel hybrid classified vector quantization using discrete cosine transform for image compression

[+] Author Affiliations
Ali Al-Fayadh

Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, United Kingdom and Ahlia University, PO Box 10878, 1st Floor, Gosi Complex, Manama, Kingdom of Bahrain

Abir Jaafar Hussain

Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, United Kingdom and Ahlia University, PO Box 10878, 1st Floor, Gosi Complex, Manama, Kingdom of Bahrain

Paulo Lisboa

Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, United Kingdom and Ahlia University, PO Box 10878, 1st Floor, Gosi Complex, Manama, Kingdom of Bahrain

Dhiya Al-Jumeily

Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, United Kingdom and Ahlia University, PO Box 10878, 1st Floor, Gosi Complex, Manama, Kingdom of Bahrain

J. Electron. Imaging. 18(2), 023003 (April 21, 2009). doi:10.1117/1.3116564
History: Received April 12, 2008; Revised February 12, 2009; Accepted March 03, 2009; Published April 21, 2009
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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.

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© 2009 SPIE and IS&T

Citation

Ali Al-Fayadh ; Abir Jaafar Hussain ; Paulo Lisboa and Dhiya Al-Jumeily
"Novel hybrid classified vector quantization using discrete cosine transform for image compression", J. Electron. Imaging. 18(2), 023003 (April 21, 2009). ; http://dx.doi.org/10.1117/1.3116564


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