Image compression using wavelet support vector machines

Abstract

In this paper, we present a new image compression algorithm which combines Wavelet Support Vector Machines (WSVM) learning with the wave-let transform. Based on the characteristic of wavelet transform, Daubechies 9/7 wavelet has been used to transform the image and the wavelet coefficients are trained with WSVM using translation-invariant wavelet kernels. Compression is achieved by using WSVM learning to approximate wavelet coefficients with the predefined level of accuracy. A minimal number of coefficients (support vectors) are then encoded by an effective entropy coder based on run-length and arithmetic coding. Experimental results show that the proposed method gains better performance than that of existing compression algorithm. © Springer-Verlag Berlin Heidelberg 2007.