title: Perceptual adaptive insensitivity for support vector machine image coding.
abstract: Support vector machine (SVM) learning has been recently proposed for image compression in the frequency domain using a constant epsilon-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 [2] for image coding using an appropriate distortion criterion [3], [4] 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.
authors.affiliation: Department of Electrical Engineering, University of València, València, Spain. gabriel.gomez@uv.es
authors.forename: Gabriel
authors.initials: G
authors.lastname: Gómez-Pérez
authors.forename: Gustavo
authors.initials: G
authors.lastname: Camps-Valls
authors.forename: Juan
authors.initials: J
authors.lastname: Gutiérrez
authors.forename: Jesús
authors.initials: J
authors.lastname: Malo
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D044962:Data Compression / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D012815:Signal Processing, Computer-Assisted; D014796:Visual Perception