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Rank Order and Image Difference Metrics

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There are a number of ways to reproduce an image, for an example gamut mapping, halftoning and compression. To find the best reproduction among a number of variants of the same reproduction algorithm, a psychophysical experiment can be carried out. Image difference metrics have been introduced to eliminate these experiments. To do this the metrics must reflect the perceived image difference. One way to evaluate the overall performance of image differnece metrics is to compute the correlation coefficient between perceived and predicted image difference. This does not always reflect the true performance of the metric, therefore we propose to use the ranking based on the predicted image difference for each scene as a data set for the rank order method. This results in a z-score similar to the overall perceived image difference, the correlation coefficient between metric z-score and perceived z-score reflects the overall performance of the image difference metrics.
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Document Type: Research Article

Publication date: January 1, 2008

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  • Started in 2002 and merged with the Color and Imaging Conference (CIC) in 2014, CGIV covered a wide range of topics related to colour and visual information, including color science, computational color, color in computer graphics, color reproduction, volor vision/psychophysics, color image quality, color image processing, and multispectral color science. Drawing papers from researchers, scientists, and engineers worldwide, DGIV offered attendees a unique experience to share with colleagues in industry and academic, and on national and international standards committees. Held every year in Europe, DGIV papers were more academic in their focus and had high student participation rates.

    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 papers for details.

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