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)

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. 
 


Keywords:
Support Vector Machine, Adaptive Insensitivity, Image Coding, DCT, Perceptual Metric, Maximum Perceptual Error.

References: 23


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