Perceptual adaptive insensitivity for support vector machine image coding

Authors:
G. Gomez-Perez;G. Camps-Valls;J. Gutierrez;J. Malo
Affiliations:
Escola Tecnica Superior d'Enginyeria, Univ. of Valencia, Spain;-;-;-
Venue:
IEEE Transactions on Neural Networks
Year:
2005

Citing 0
Cited 4

Letters: Exploiting multi-scale support vector regression for image compression

Neurocomputing
On the Suitable Domain for SVM Training in Image Coding

The Journal of Machine Learning Research
Relevance vector machine with adaptive wavelet kernels for efficient image coding

Neurocomputing
Image Denoising with Kernels Based on Natural Image Relations

The Journal of Machine Learning Research

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Abstract

Support vector machine (SVM) learning has been recently proposed for image compression in the frequency domain using a constant ε-insensitivity zone by Robinson and Kecman. 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 as the number of discrete cosine transform (DCT) coefficients to be used in the training was limited. This paper extends the work of Robinson and Kecman by proposing the use of adaptive insensitivity SVMs for image coding using an appropriate distortion criterion , 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.