A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
The nature of statistical learning theory
The nature of statistical learning theory
Arithmetic coding for data compression
Communications of the ACM
Fundamentals of Artificial Neural Networks
Fundamentals of Artificial Neural Networks
SVM regression and its application to image compression
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Image compression by self-organized Kohonen map
IEEE Transactions on Neural Networks
Combining support vector machine learning with the discrete cosine transform in image compression
IEEE Transactions on Neural Networks
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In this paper, we present a new image compression algorithm which combines Wavelet Support Vector Machines (WSVM) learning with the wavelet 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.