Statistically and Perceptually Motivated Nonlinear Image Representation
Published in:
Proc. SPIE, Conf. on Human Vision and Electronic Imaging XII,
vol. 6492, San Jose, CA, Jan 2007. © SPIE
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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