A New Metric for Grey-Scale Image Comparison |
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Authors: | Wilson Dale L. Baddeley Adrian J. Owens Robyn A. |
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Affiliation: | (1) Department of Engineering Science, The University of Oxford, UK;(2) Department of Mathematics, The University of Western Australia, Australia;(3) Department of Computer Science, The University of Western Australia, Australia |
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Abstract: | Error measures can be used to numerically assess the differences between two images. Much work has been done on binary error measures, but little on objective metrics for grey-scale images. In our discussion here we introduce a new grey-scale measure, g, aiming to improve upon the most common grey-scale error measure, the root-mean-square error. Our new measure is an extension of the authors' recently developed binary error measure, b, not only in structure, but also having both a theoretical and intuitive basis. We consider the similarities between b and g when tested in practice on binary images, and present results comparing g to the root-mean-squared error and the Sobolev norm for various binary and grey-scale images. There are no previous examples where the last of these measures, the Sobolev norm, has been implemented for this purpose. |
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Keywords: | grey-scale image comparison error measures /content/l7443g0545335422/xxlarge916.gif" alt=" Delta" align=" BASELINE" BORDER=" 0" > metrics root-mean-squared error Sobolev norm Hausdorff metric Myopic topology image distortion visual perception |
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