Perceptual Adaptive Insensitivity for Support Vector Machine Image Coding  

Oleh:

Gomez-Perez, G. ; Camps-Valls, G. ; Gutierrez, J. ; Malo, J.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 16 no. 6 (Nov. 2005), page 1574-1581.
Topik: images; perceptual; insensitivity; support vector; machine image coding

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

Support vector machine (SVM) learning has been recently proposed for image compression in the frequency domain using a constant e - 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 SVM s 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.

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