Contrast Sensitive Epsilon-SVR and its application in image compression

This paper presents a practical and effective image compression system based on wavelet decomposition and contrast sensitive-SVR (support vector regression) for compressing still images. The kernel function in an SVR plays the central role of implicitly mapping the input vector (through an inner product) into a high-dimensional feature space. We study the different wavelet kernel for image compression application. Image quality is measured objectively, using peak signal-to-noise ratio, and subjectively, using perceived image quality. The effects of different wavelet kernels, image contents and compression ratios are assessed. A comparison with JPEG, SPIHT compression system is given. Our results provide a good reference to choose a suitable kernel for image compression application.

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