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A New Image Compression framework: DWT Optimization using LS-SVM regression under IWP-QPSO based hyper parameter optimization

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Author(s): S. Nagaraja Rao | M.N.Giri Prasad

Journal: International Journal of Computer Science and Information Security
ISSN 1947-5500

Volume: 9;
Issue: 7;
Start page: 52;
Date: 2011;
Original page

Keywords: Model integrating DWT | Least squares support machines (LS-SVM) | Honed Fast Haar wavelet transforms (HFHT) | QPSO | HFHT | FHT.

ABSTRACT
In this chapter, a hybrid model integrating DWT and least squares support machines (LSSVM) is proposed for Image coding. In this model, proposed Honed Fast Haar wavelet transform (HFHT) is used to decompose an original RGB Image with different scales. Then the LS-SVM regression is used to predict series of coefficients. The hyper coefficients for LS-SVM selected by using proposed QPSO technique called intensified worst particle based QPSO (IWP-QPSO). Two mathematical models discussed, one is to derive the HFHT that is computationally efficient when compared with traditional FHT, and the other is to derive IWP-QPSO that performed with minimum iterations when compared to traditional QPSO. The experimental results show that the hybrid model, based on LSSVM regression, HFHT and IWP-QPSO, outperforms the traditional Image coding standards like jpeg and jpeg2000 and, furthermore, the proposed hybrid model emerged as best in comparative study with jpeg2000 standard.
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