Statistically and perceptually motivated nonlinear image representation

Siwei Lyu; Eero P. Simoncelli

doi:10.1117/12.720848

7 February 2007 Statistically and perceptually motivated nonlinear image representation

Siwei Lyu, Eero P. Simoncelli

Proceedings Volume 6492, Human Vision and Electronic Imaging XII; 649207 (2007) https://doi.org/10.1117/12.720848
Event: Electronic Imaging 2007, 2007, San Jose, CA, United States

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.

Citation Download Citation

Siwei Lyu and Eero P. Simoncelli "Statistically and perceptually motivated nonlinear image representation", Proc. SPIE 6492, Human Vision and Electronic Imaging XII, 649207 (7 February 2007); https://doi.org/10.1117/12.720848

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