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Journal of Electronic Imaging • Open Access

Transformation-aware perceptual image metric
Author(s): Petr Kellnhofer; Tobias Ritschel; Karol Myszkowski; Hans-Peter Seidel

Paper Abstract

Predicting human visual perception has several applications such as compression, rendering, editing, and retargeting. Current approaches, however, ignore the fact that the human visual system compensates for geometric transformations, e.g., we see that an image and a rotated copy are identical. Instead, they will report a large, false-positive difference. At the same time, if the transformations become too strong or too spatially incoherent, comparing two images gets increasingly difficult. Between these two extrema, we propose a system to quantify the effect of transformations, not only on the perception of image differences but also on saliency and motion parallax. To this end, we first fit local homographies to a given optical flow field, and then convert this field into a field of elementary transformations, such as translation, rotation, scaling, and perspective. We conduct a perceptual experiment quantifying the increase of difficulty when compensating for elementary transformations. Transformation entropy is proposed as a measure of complexity in a flow field. This representation is then used for applications, such as comparison of nonaligned images, where transformations cause threshold elevation, detection of salient transformations, and a model of perceived motion parallax. Applications of our approach are a perceptual level-of-detail for real-time rendering and viewpoint selection based on perceived motion parallax.

Paper Details

Date Published: 21 September 2016
PDF: 16 pages
J. Electron. Imaging. 25(5) 053014 doi: 10.1117/1.JEI.25.5.053014
Published in: Journal of Electronic Imaging Volume 25, Issue 5
Show Author Affiliations
Petr Kellnhofer, Max-Planck-Institut für Informatik (Germany)
Tobias Ritschel, Max-Planck-Institut für Informatik (Germany)
Univ. des Saarlandes (Germany)
Univ. College London (United Kingdom)
Karol Myszkowski, Max-Planck-Institut für Informatik (Germany)
Hans-Peter Seidel, Max-Planck-Institut für Informatik (Germany)


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