Rafał Mantiuk *
Rafał Mantiuk
Professor of Graphics and Displays
Department of Computer Science and Technology
The Computer Laboratory
Rainbow Research Group
University of Cambridge

Office address
University of Cambridge
Computer Laboratory
William Gates Building
15 JJ Thomson Avenue
Cambridge CB3 0FD
United Kingdom
office: +44 1223 763831
rafal [dot] mantiuk [at] cl [dot] cam [dot] ac [dot] uk

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Reserach interest

Applied visual perception; high dynamic range imaging; display algorithms; machine learning for image synthesis; tone-mapping; video coding for new display technologies; image and video quality metrics; visibility metrics; virtual reality and low-level perception; computational photography; computational displays; novel display technologies; colour; perception in computer graphics; novel image and video representations (beyond 2D); psychophysics; modeling visual perception with machine learning.


Professor/Reader of Graphics and Displays, University of Cambridge, Computer Laboratory, UK (from 2018)
Senior Lecturer, University of Cambridge, Computer Laboratory, UK (2015-2018)
Lecturer/Senior Lecturer, Bangor University, School of Computer Science, UK (2009-2015)
Postdoc Fellow, University of British Columbia, Canada (2008-2009)
Postdoc, Max-Planck-Institut for Computer Science, Germany (2007-2008)
Internship, Sharp Laboratories of America, Camas WA, USA (2006)
PhD (summa cum laude, Computer Science), Max-Planck-Institut for Computer Science, Germany (2006)
Msc (Computer Science), Technical University of Szczecin, Poland (2003)
Google Scholar LinkedIn profile Mendeley profile

Recent projects

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stelaCSF - A Unified Model of Contrast Sensitivity as the Function of Spatio-Temporal Frequency, Eccentricity, Luminance and Area

A contrast sensitivity function of the human visual system has been modeled as the function of spatial and temporal frequency, eccentricity, luminance, and area (stelaCSF). The same model explains the data from 11 contrast sensitivity datasets from the literature and enables applications in foveated rendering, flicker visibility, and other areas.

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Dark Stereo: Improving Depth Perception Under Low Luminance

Low display brightness, which is often desirable in VR and stereo applications, decreases the precision of binocular depth cues. We measure and model this effect and then show how it can be compensated with a simple, real-time, image-space technique to produce preferable and more 3D rendering for low-luminance VR headsets.

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Comparison of Single-image HDR Reconstruction Methods — The Caveats of Quality Assessment

Existing protocols for evaluating single-image HDR reconstruction methods are unreliable because of large color and tone differences. We demonstrate that the accuracy of metrics can be improved by correcting for camera-response-curve inversion errors. Still, the metrics can detect only substantial differences, and conducting a controlled experiment is preferable.

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Recent papers

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Awards and grants

My contribution to the organization of research networks and conferences can be found here.