IMAGE QUALITY ASSESSMENT FROM ERROR VISIBILITY TO STRUCTRAL SIMILARITY
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 imagedifference measure shows significantly higher prediction accuracy on a gamut mapping dataset than all other evaluated measures.