Abstract An image compression approach is proposed based on support vector domain description (SVDD) and adaptively weighted support vector machine (w-SVM). An intensity image is divided into some non-overlapped rectangular subblocks and transformed from spatial domain to frequency domain via discrete cosine transform (DCT). On each segmented subblock, its corresponding weight function model is obtained according to the distance from each DCT coefficient to the center of the smallest enclosing hypersphere in high dimension feature space. The established model is eventually embedded into the w-SVM based image compression scheme. Simulation results show that the proposed approach is superior in prediction performance and compression effect to general SVM-based image compression algorithms.
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2007, Vol. 20 
