6533b7d8fe1ef96bd126b65f

RESEARCH PRODUCT

Perceptual Image Representations for Support Vector Machine Image Coding

Jesús MaloJuan Manuel GutiérrezGustavo Camps-vallsGabriel Gómez-pérez

subject

Computer sciencebusiness.industryComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONPattern recognitionImage processingPermissionImage (mathematics)Support vector machineAutomatic image annotationDigital image processingComputer visionArtificial intelligenceImage warpingbusinessFeature detection (computer vision)

description

Support-vector-machine image coding relies on the ability of SVMs for function approximation. The size and the profile of the e-insensitivity zone of the support vector regressor (SVR) at some specific image representation determines (a) the amount of selected support vectors (the compression ratio), and (b) the nature of the introduced error (the compression distortion). However, the selection of an appropriate image representation is a key issue for a meaningful design of the e-insensitivity profile. For example, in image-coding applications, taking human perception into account is of paramount relevance to obtain a good rate-distortion performance. However, depending on the accuracy of the considered perception model, certain image representations are not suitable for SVR training. In this Perceptual Image Representations for Support Vector Machine Image Coding 299 Copyright © 2007, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. chapter, we analyze the general procedure to take human vision models into account in SVR-based image coding. Specifically, we derive the condition for image representation selection and the associated e-insensitivity profiles.

yearjournalcountryeditionlanguage
2007-01-01
https://doi.org/10.4018/978-1-59904-042-4.ch013