Matthias Scheller Lichtenauer

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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(More)
We discuss a few selected hypotheses on how the visual system judges differences of color images. We then derive five image-difference features from these hypotheses and address their relation to the visual processing. Three models are proposed to combine these features for the prediction of perceived image differences. The parameters of the(More)
We present a corpus of experimental data from psychome-tric studies on gamut mapping and demonstrate its use to develop image similarity measures. We investigate whether similarity measures based on luminance (SSIM) can be improved when features based on chroma and hue are added. Image similarity measures can be applied to automatically select a good image(More)
Rendering materials on displays becomes ubiquitous in industrial design, architecture, and visualization. Yet the experience of the material from other modes of perception is missing in that representation. This forces observers to rely on visual cues only while judging material properties. In the present study, we compare judgments of rough and glossy(More)
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