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An adaptive Bilateral Filter for Predicting Color Image Difference

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Color image difference metrics are of great importance in the field of color image reproduction. In this study, we introduce an adaptive bilateral filter for predicting color image difference. This filter is simple, employing two Gaussian smoothing filters in different domains, which avoids the loss of edge information when smoothing the image. However, the challenge is to select appropriate parameters to result in a better performance when applying for color image deference prediction. We propose a method to optimize the parameters, which are designed to be adaptive to the corresponding viewing conditions, and the quantity and homogeneity of information contained in an image. We have conducted psychophysical experiments to evaluate the performance of our approach. The experimental sample images are reproduced with variations in six image attributes: Lightness, Chroma, Hue, Compression, Noise, and Sharpness. The Pearson's correlation value between the predicted difference and the z-score of visual judgments was employed to evaluate the performance and compare it with that of s-CIELAB and iCAM.
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Document Type: Research Article

Publication date: January 1, 2009

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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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