Jean-Philippe Farrugia & Bernard Péroche
University of Lyon I
Department of Computer Science
LIRIS (Lyon Research Center for Images and Intelligent Information
Systems)
Bâtiment Nautibus (710)
8, boulevard Niels Bohr
69622 Villeurbanne Cedex
France
Stéphane Albin
Ecole Nationale Supérieure des Mines de Saint Etienne
158 Cours Fauriel
42023 Saint Etienne Cedex 2
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e-mail: jpfarrug@bat710.emse.fr |
Abstract
This paper presents two points: a
new simple color vision model and an adaptive way to compute an image
metric based on a vision model. Metrics are very useful in computer
graphics. Applications include perceptually-based rendering or image
comparison for photorealism. Usual vision model-based metrics make an
expensive use of memory and cpu resources, mainly for two reasons.
First, the vision model is a pipeline of non linear functions applying
on a multi-scale decomposition of the image. Second, the model is
computed for every single pixel of the picture. In this paper, we
designed a very simple mono-scale vision model taking into account many
perceptual issues like masking effects and adaptation. We also propose
an adaptive approach of distance computation : the image plane sample
scheme is designed to be denser when distance variation is greater.
This method is usable with any vision model and only uses two
parameters, making it very easy to configure. By combining it with our
simple vision model, it computes a difference map interactively for
512x512 pictures.