Ye, N;
Perez-Ortiz, M;
Mantiuk, RK;
(2018)
Trained Perceptual Transform for Quality Assessment of High Dynamic Range Images and Video.
In: Nikou, Christophoros and Plataniotis, Kostas, (eds.)
Proceedings of the 25th IEEE International Conference on Image Processing (ICIP 2018).
(pp. pp. 1718-1722).
IEEE: Athens, Greece.
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Abstract
In this paper, we propose a trained perceptually transform for quality assessment of high dynamic range (HDR) images and video. The transform is used to convert absolute luminance values found in HDR images into perceptually uniform units, which can be used with any standard-dynamic-range metric. The new transform is derived by fitting the parameters of a previously proposed perceptual encoding function to 4 different HDR subjective quality assessment datasets using Bayesian optimization. The new transform combined with a simple peak signal-to-noise ratio measure achieves better prediction performance in cross-dataset validation than existing transforms. We provide Matlab code for our metric 1.
| Type: | Proceedings paper |
|---|---|
| Title: | Trained Perceptual Transform for Quality Assessment of High Dynamic Range Images and Video |
| Event: | 25th IEEE International Conference on Image Processing (ICIP 2018), 7-10 October 2018, Athens, Greece |
| Location: | Athens, GREECE |
| Dates: | 07 October 2018 - 10 October 2018 |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&ar... |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Image quality assessment, high dynamic range, perceptually uniform encoding |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10069042 |
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