Statistically and perceptually motivated nonlinear image representation

Siwei Lyu; Eero P. Simoncelli

doi:10.1117/12.720848

Statistically and perceptually motivated nonlinear image representation

Siwei Lyu, Eero P. Simoncelli

Neural Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English (US)
Title of host publication	Proceedings of SPIE-IS and T Electronic Imaging - Human Vision and Electronic Imaging XII
DOIs	https://doi.org/10.1117/12.720848
State	Published - 2007
Event	Human Vision and Electronic Imaging XII - San Jose, CA, United States Duration: Jan 29 2007 → Feb 1 2007

Publication series

Name	Proceedings of SPIE - The International Society for Optical Engineering
Volume	6492
ISSN (Print)	0277-786X

Other

Other	Human Vision and Electronic Imaging XII
Country/Territory	United States
City	San Jose, CA
Period	1/29/07 → 2/1/07

Keywords

Contrast enhancement
Divisive normalization
Independent components
Markov random field
Optimal representation

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Condensed Matter Physics
Computer Science Applications
Applied Mathematics
Electrical and Electronic Engineering

Access to Document

10.1117/12.720848

Cite this

Statistically and perceptually motivated nonlinear image representation. / Lyu, Siwei; Simoncelli, Eero P.
Proceedings of SPIE-IS and T Electronic Imaging - Human Vision and Electronic Imaging XII. 2007. 649207 (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 6492).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Lyu, S & Simoncelli, EP 2007, Statistically and perceptually motivated nonlinear image representation. in Proceedings of SPIE-IS and T Electronic Imaging - Human Vision and Electronic Imaging XII., 649207, Proceedings of SPIE - The International Society for Optical Engineering, vol. 6492, Human Vision and Electronic Imaging XII, San Jose, CA, United States, 1/29/07. https://doi.org/10.1117/12.720848

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AB - 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.

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KW - Markov random field

KW - Optimal representation

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Statistically and perceptually motivated nonlinear image representation

Abstract

Publication series

Other

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this