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Abstract

The diversity of display technologies and introduction of high dynamic range
imagery introduces the necessity of comparing images of radically different
dynamic ranges. Current quality assessment metrics are not suitable for this
task, as they assume that both reference and test images have the same dynamic
range. Image fidelity measures employed by a majority of current metrics, based
on the difference of pixel intensity or contrast values between test and
reference images, result in meaningless predictions if this assumption does not
hold. We present a novel image quality metric capable of operating on an image
pair where both images have arbitrary dynamic ranges. Our metric utilizes a
model of the human visual system, and its central idea is a new definition of
visible distortion based on the detection and classification of visible changes
in the image structure. Our metric is carefully calibrated and its performance
is validated through perceptual experiments. We demonstrate possible
applications of our metric to the evaluation of direct and inverse tone mapping
operators as well as the analysis of the image appearance on displays with
various characteristics.