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Image-Difference Measure Optimized Gamut Mapping

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Even though there is still room for improvement, recent perceptual image-difference measures show a prediction performance that makes them interesting to be used as objective functions for optimizing image processing algorithms. In this paper, we use a color enhanced modification of the Structural Similarity (SSIM) index for optimizing gamut mapping. An iterative algorithm is proposed that minimizes this measure for a given reference image subject to in-gamut images. Since distortions within remote image regions contribute independently to the measure a descent direction can be specified locally. The step-length is chosen to be a fraction of the just-noticeable-distance ensuring a decrease of the measure. Results show that the proposed approach preserves contrast and structural information of reference images. Some artifacts suggest modifications of the employed image-difference measure.
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Document Type: Research Article

Publication date: January 1, 2012

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  • CIC is the premier annual technical gathering for scientists, technologists, and engineers working in the areas of color science and systems, and their application to color imaging. Participants represent disciplines ranging from psychophysics, optical physics, image processing, color science to graphic arts, systems engineering, and hardware and software development. While a broad mix of professional interests is the hallmark of these conferences, the focus is color. CICs traditionally offer two days of short courses followed by three days of technical sessions that include three keynotes, an evening lecture, a vibrant interactive (poster) papers session, and workshops. An endearing symbol of the meeting is the Cactus Award, given each year to the author(s) of the best interactive paper; there are also Best Paper and Best Student Paper awards.

    Please note: for Purposes of its Digital Library content, IS&T defines Open Access as papers that will be downloadable in their entirety for free in perpetuity. Copyright restrictions on papers vary; see individual paper for details.

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