The nature of statistical learning theory
The nature of statistical learning theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Fundamentals of Artificial Neural Networks
Fundamentals of Artificial Neural Networks
Image compression using multi-layer neural networks
ISCC '97 Proceedings of the 2nd IEEE Symposium on Computers and Communications (ISCC '97)
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
SVM regression and its application to image compression
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Combining support vector machine learning with the discrete cosine transform in image compression
IEEE Transactions on Neural Networks
Perceptual adaptive insensitivity for support vector machine image coding
IEEE Transactions on Neural Networks
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Unlike traditional neural networks that require predefined topology of the network, support vector regression (SVR) approach can model the data within the given level of accuracy with only a small subset of the training data, which are called support vectors (SVs). This property of sparsity has been exploited as the basis for image compression. In this paper, for still image compression, we propose a multi-scale support vector regression (MS-SVR) approach, which can model the images with steep variations and smooth variations very well resulting in good performance. We test our proposed MS-SVR based algorithm on some standard images. The experimental results verify that the proposed MS-SVR achieves better performance than standard SVR. And in a wide range of compression ratio, MS-SVR is very close to JPEG in terms of peak signal-to-noise ratio (PSNR) but exhibits better subjective quality. Furthermore, MS-SVR even outperforms JPEG on both PSNR and subjective quality when the compression ratio is higher enough, for example 25:1 for Lena image. Even when compared with JPEG-2000, the results show greatly similar trend as those in JPEG experiments, except that the compression ratio is a bit higher where our proposed MS-SVR will outperform JPEG-2000.