Dynamic range independent image quality assessment

By T. Aydın, Rafał K. Mantiuk, Karol Myszkowski, Hans-Peter Seidel. ACM Transactions on Graphics, 2008

303 citations recorded by the source corpus.

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.

Subfields: Atomic and Molecular Physics, and Optics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/dbde9da5619d17276569f8c992c1fdbe1074a803

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In Atomic and Molecular Physics, and Optics

In Computer Vision and Pattern Recognition

Citations in the corpus

Objective Quality Assessment of Tone-Mapped Images (2013, methodology): “The only exception is the method proposed in [14], which creates probability maps to distinguish between visible (suprathreshold) and invisible (subthreshold) degradations.”
Enhancing underwater images and videos by fusion (2012, methodology): “The filtered version I ωhc is obtained by employing a small 5 × 5 ( 1 16 [1, 4, 6, 4, 1]) separable binomial kernel with the high frequency cut-off value ωhc = π/2.”
An Underwater Image Enhancement Benchmark Dataset and Beyond (2019, methodology): “For a real-world underwater image where the ground truth image was unavailable, non-reference image quality evaluation metrics, such as image entropy, visible edges [57], and dynamic range independent image quality assessment [58], were utilized.”
HDR-VDP-2: a calibrated visual metric for visibility and quality predictions in all luminance conditions (2011, methodology): “The underlying visual model of the HDR-VDP-2 can be used in combination with higher level visual metrics, such as dynamic range independent metrics [Aydin et al. 2008; Aydin et al. 2010].”