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Image-Difference Prediction: From Grayscale to Color

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5 Author(s)
Lissner, I. ; Inst. of Printing Sci. & Technol., Tech. Univ. Darmstadt, Darmstadt, Germany ; Preiss, J. ; Urban, P. ; Lichtenauer, M.S.
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Existing image-difference measures show excellent accuracy in predicting distortions, such as lossy compression, noise, and blur. Their performance on certain other distortions could be improved; one example of this is gamut mapping. This is partly because they either do not interpret chromatic information correctly or they ignore it entirely. We present an image-difference framework that comprises image normalization, feature extraction, and feature combination. Based on this framework, we create image-difference measures by selecting specific implementations for each of the steps. Particular emphasis is placed on using color information to improve the assessment of gamut-mapped images. Our best image-difference measure shows significantly higher prediction accuracy on a gamut-mapping dataset than all other evaluated measures.

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Image Processing, IEEE Transactions on  (Volume:22 ,  Issue: 2 )