A perceptual framework for contrast processing of high dynamic range images

  • Authors:
  • Rafal Mantiuk;Karol Myszkowski;Hans-Peter Seidel

  • Affiliations:
  • MPI Informatik, Saarbrücken, Germany;MPI Informatik, Saarbrücken, Germany;MPI Informatik, Saarbrücken, Germany

  • Venue:
  • ACM Transactions on Applied Perception (TAP)
  • Year:
  • 2006

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Abstract

Image processing often involves an image transformation into a domain that is better correlated with visual perception, such as the wavelet domain, image pyramids, multiscale-contrast representations, contrast in retinex algorithms, and chroma, lightness, and colorfulness predictors in color-appearance models. Many of these transformations are not ideally suited for image processing that significantly modifies an image. For example, the modification of a single band in a multiscale model leads to an unrealistic image with severe halo artifacts. Inspired by gradient domain methods, we derive a framework that imposes constraints on the entire set of contrasts in an image for a full range of spatial frequencies. This way, even severe image modifications do not reverse the polarity of contrast. The strengths of the framework are demonstrated by aggressive contrast enhancement and a visually appealing tone mapping, which does not introduce artifacts. In addition, we perceptually linearize contrast magnitudes using a custom transducer function. The transducer function has been derived especially for the purpose of HDR images, based on the contrast-discrimination measurements for high-contrast stimuli.