Paper
16 February 2007 Fully automatic perceptual modeling of near regular textures
G. Menegaz, A. Franceschetti, A. Mecocci
Author Affiliations +
Proceedings Volume 6492, Human Vision and Electronic Imaging XII; 64921B (2007) https://doi.org/10.1117/12.703121
Event: Electronic Imaging 2007, 2007, San Jose, CA, United States
Abstract
Near regular textures feature a relatively high degree of regularity. They can be conveniently modeled by the combination of a suitable set of textons and a placement rule. The main issues in this respect are the selection of the minimum set of textons bringing the variability of the basic patterns; the identification and positioning of the generating lattice; and the modelization of the variability in both the texton structure and the deviation from periodicity of the lattice capturing the naturalness of the considered texture. In this contribution, we provide a fully automatic solution to both the analysis and the synthesis issues leading to the generation of textures samples that are perceptually indistinguishable from the original ones. The definition of an ad-hoc periodicity index allows to predict the suitability of the model for a given texture. The model is validated through psychovisual experiments providing the conditions for subjective equivalence among the original and synthetic textures, while allowing to determine the minimum number of textons to be used to meet such a requirement for a given texture class. This is of prime importance in model-based coding applications, as is the one we foresee, as it allows to minimize the amount of information to be transmitted to the receiver.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
G. Menegaz, A. Franceschetti, and A. Mecocci "Fully automatic perceptual modeling of near regular textures", Proc. SPIE 6492, Human Vision and Electronic Imaging XII, 64921B (16 February 2007); https://doi.org/10.1117/12.703121
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Cited by 2 scholarly publications.
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KEYWORDS
Statistical analysis

Model-based design

Nickel

Electronic imaging

Image compression

Receivers

Statistical modeling

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