title: Image-difference prediction: from grayscale to color.
abstract: 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.
authors.affiliation: Institute of Printing Science and Technology, Technische Universität Darmstadt, Darmstadt 64289, Germany. lissner@idd.tu-darmstadt.de
authors.forename: Ingmar
authors.initials: I
authors.lastname: Lissner
authors.forename: Jens
authors.initials: J
authors.lastname: Preiss
authors.forename: Philipp
authors.initials: P
authors.lastname: Urban
authors.forename: Matthias Scheller
authors.initials: MS
authors.lastname: Lichtenauer
authors.forename: Peter
authors.initials: P
authors.lastname: Zolliker