Perceptual Adaptive Insensitivity for Support Vector Machine Image Coding | |||
G. Gómez, G. Camps, J.
Gutiérrez,
and J. Malo, IEEE Trans. Neural Networks Vol. 16, 6, pp 1574-1581 (2005) |
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Abstract
Support Vector Machine (SVM) learning has been recently proposed for image compression in the frequency domain using a constant insensitivity zone by Robinson and Kecman [Rob&Kec03]. However, according to the statistical properties of natural images and the properties of human perception, a constant insensitivity makes sense in the spatial domain but it is certainly not a good option in a frequency domain. In fact, in their approach they made a fixed low-pass assumption: they neglected high-frequency coefficients in the SVM training. This paper proposes the use of
adaptive
insensitivity SVMs [Camps01] for image coding using an appropriate
distortion
criterion [Malo01] based on a simple visual cortex model. Training the
SVM by using an accurate perception model avoids any a priori
assumption
and improves the rate-distortion performance of the original
approach. |
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Keywords: Support Vector Machine, Adaptive Insensitivity, Image Coding, DCT, Perceptual Metric, Maximum Perceptual Error. References: 23 |
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