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Can eye movements be quantitatively applied to image quality studies?

Published: 23 October 2004 Publication History

Abstract

The aim of the study is to find out whether subjective image quality evaluations can be quantified by eye movement tracking. We want to map objective or physically measurable image quality to subjective evaluations and eye movement data. Results show that eye movement parameters consistently change according to the instructions given to the user, and according to physical image quality. These results indicate that eye movement tracking could be used to differentiate image quality evaluation strategies that the users have. Results also show that eye movements would help mapping between technological and subjective image quality. We also propose to extend the widely used image quality process model, the Image Quality Circle. We suggest adding the objective measurements of a viewer (e.g. eye tracking) in parallel with customer perceptions as an option to gather information of customer perceptions of image quality.

References

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Babcock, J. S., Pelz, J. B. & Fairchild, M. D. (2003). Eye Tracking Observers During Rank Order, Paired Comparison, and Graphical Rating Tasks. PICS 2003, Rochester, NY, May 13, 2003, Vol. 6.
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Engeldrum, Peter G. (2000). Psychometric Scaling: A Toolkit for Imaging Systems Development. Winchester, USA: Imcotek Press.
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Duchowski, A. (2003). Eye Tracking Methodology: Theory and Practice. London: Springer.
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Freiser, H. & Biederman (1963). Experiments on image quality in relation to the modulation transfer function and graininess of photographs. Photographic Science and Engineering, 7, 28--34.
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Molnar, F. (1981). About the role of visual exploration in aesthetics. Advances in Instrinsic Motivation and Aesthetics. New York: Plenum Press.
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Näsänen, R., Ojanpää, H. & Kojo, I. (2001). Effect of stimulus contrast on performance and eye movements in visual search. Vision Research 41, 1817--1824.
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Parkhurst, D. Law, K. & Niebur, E. (2002). Modeling the role of salience in the allocation of overt visual attention. Vision Research, 42, 107--123.
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Radach, R., Lemmer, S., Vorstius, C., Heller, D. & Radach, K. (2003). Eye Movements in the Processing of Print Advertisements. The Minds Eye: Cognitive and Applied Aspects of Eye Movement Research. Amsterdam: Elsevier Science BV.
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  • (2022)Titles and Semantic Violations Affect Eye Movements When Viewing Contemporary PaintingsFrontiers in Human Neuroscience10.3389/fnhum.2022.80833016Online publication date: 4-Mar-2022
  • (2020)Individual difference on reading ability tested by eye-tracking: from perspective of genderInteractive Technology and Smart Education10.1108/ITSE-12-2019-008217:3(267-283)Online publication date: 4-Mar-2020
  • (2020)How Quickly Can We Predict Users’ Ratings on Aesthetic Evaluations of Websites? Employing Machine Learning on Eye-Tracking DataResponsible Design, Implementation and Use of Information and Communication Technology10.1007/978-3-030-45002-1_37(429-440)Online publication date: 1-Apr-2020
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cover image ACM Other conferences
NordiCHI '04: Proceedings of the third Nordic conference on Human-computer interaction
October 2004
472 pages
ISBN:1581138571
DOI:10.1145/1028014
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 October 2004

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Author Tags

  1. eye movements
  2. image quality
  3. psychometric scaling

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NordiCHI04
NordiCHI04: NordiCHI 2004
October 23 - 27, 2004
Tampere, Finland

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Overall Acceptance Rate 379 of 1,572 submissions, 24%

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Cited By

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  • (2022)Titles and Semantic Violations Affect Eye Movements When Viewing Contemporary PaintingsFrontiers in Human Neuroscience10.3389/fnhum.2022.80833016Online publication date: 4-Mar-2022
  • (2020)Individual difference on reading ability tested by eye-tracking: from perspective of genderInteractive Technology and Smart Education10.1108/ITSE-12-2019-008217:3(267-283)Online publication date: 4-Mar-2020
  • (2020)How Quickly Can We Predict Users’ Ratings on Aesthetic Evaluations of Websites? Employing Machine Learning on Eye-Tracking DataResponsible Design, Implementation and Use of Information and Communication Technology10.1007/978-3-030-45002-1_37(429-440)Online publication date: 1-Apr-2020
  • (2019)Building pipelines for educational data using AI and multimodal analytics: A “grey‐box” approachBritish Journal of Educational Technology10.1111/bjet.1285450:6(3004-3031)Online publication date: 21-Jul-2019
  • (2019)Multimodal data as a means to understand the learning experienceInternational Journal of Information Management10.1016/j.ijinfomgt.2019.02.00348(108-119)Online publication date: Oct-2019
  • (2016)Online Learners’ Reading Ability Detection Based on Eye-Tracking SensorsSensors10.3390/s1609145716:9(1457)Online publication date: 10-Sep-2016
  • (2016)ErgocrowdActes de la 28ième conference francophone sur l'Interaction Homme-Machine10.1145/3004107.3004141(291-298)Online publication date: 25-Oct-2016
  • (2015)An efficient contents-adaptive backlight control method for mobile devicesMobile Devices and Multimedia: Enabling Technologies, Algorithms, and Applications 201510.1117/12.2087385(94110U)Online publication date: 11-Mar-2015
  • (2014)How Passive Image Viewers Became Active Multimedia UsersVisual Signal Quality Assessment10.1007/978-3-319-10368-6_2(31-72)Online publication date: 16-Oct-2014
  • (2013)ReferencesManaging Emotion in Design Innovation10.1201/b15398-8(181-190)Online publication date: Aug-2013
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