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From Pairwise Comparisons and Rating to a Unified Quality Scale

Pérez-Ortiz, M; Mikhailiuk, A; Zerman, E; Hulusic, V; Valenzise, G; Mantiuk, RK; (2020) From Pairwise Comparisons and Rating to a Unified Quality Scale. IEEE Transactions on Image Processing , 29 pp. 1139-1151. 10.1109/TIP.2019.2936103. Green open access

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Abstract

The goal of psychometric scaling is the quantification of perceptual experiences, understanding the relationship between an external stimulus, the internal representation and the response. In this paper, we propose a probabilistic framework to fuse the outcome of different psychophysical experimental protocols, namely rating and pairwise comparisons experiments. Such a method can be used for merging existing datasets of subjective nature and for experiments in which both measurements are collected. We analyze and compare the outcomes of both types of experimental protocols in terms of time and accuracy in a set of simulations and experiments with benchmark and real-world image quality assessment datasets, showing the necessity of scaling and the advantages of each protocol and mixing. Although most of our examples focus on image quality assessment, our findings generalize to any other subjective quality-of-experience task.

Type: Article
Title: From Pairwise Comparisons and Rating to a Unified Quality Scale
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/TIP.2019.2936103
Publisher version: https://doi.org/10.1109/TIP.2019.2936103
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: Psychometric scaling, pairwise comparisons, rating, image and video quality assessment, dataset fusion
UCL classification: UCL
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.ucl.ac.uk/id/eprint/10081517
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