Statistically and perceptually motivated nonlinear image representation

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish (US)
Title of host publicationProceedings of SPIE-IS and T Electronic Imaging - Human Vision and Electronic Imaging XII
DOIs
StatePublished - 2007
EventHuman Vision and Electronic Imaging XII - San Jose, CA, United States
Duration: Jan 29 2007Feb 1 2007

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume6492
ISSN (Print)0277-786X

Other

OtherHuman Vision and Electronic Imaging XII
CountryUnited States
CitySan Jose, CA
Period1/29/072/1/07

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

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