Share Email Print

Proceedings Paper

Statistically and perceptually motivated nonlinear image representation
Author(s): Siwei Lyu; Eero P. Simoncelli
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

We describe an invertible nonlinear image transformation that is well-matched to the statistical properties of photographic images, as well as the perceptual sensitivity of the human visual system. Images are first decomposed using a multi-scale oriented linear transformation. In this domain, we develop a Markov random field model based on the dependencies within local clusters of transform coefficients associated with basis functions at nearby positions, orientations and scales. In this model, division of each coefficient by a particular linear combination of the amplitudes of others in the cluster produces a new nonlinear representation with marginally Gaussian statistics. We develop a reliable and efficient iterative procedure for inverting the divisive transformation. Finally, we probe the statistical and perceptual advantages of this image representation, examining robustness to added noise, rate-distortion behavior, and artifact-free local contrast enhancement.

Paper Details

Date Published: 7 February 2007
PDF: 15 pages
Proc. SPIE 6492, Human Vision and Electronic Imaging XII, 649207 (7 February 2007); doi: 10.1117/12.720848
Show Author Affiliations
Siwei Lyu, New York Univ. (United States)
Eero P. Simoncelli, New York Univ. (United States)

Published in SPIE Proceedings Vol. 6492:
Human Vision and Electronic Imaging XII
Bernice E. Rogowitz; Thrasyvoulos N. Pappas; Scott J. Daly, Editor(s)

© SPIE. Terms of Use
Back to Top