Rafał Mantiuk *
Photograph
Rafał Mantiuk
Reader in 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
Room
SS22
Phone
office: +44 1223 763831
E-mail
rafal [dot] mantiuk [at] cl [dot] cam [dot] ac [dot] uk

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If you are contacting me about internship, PhD studentship, or a PostDoc position, please check the "Jobs" section first.


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 metric; 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.


Biography

Senior Lecturer, University of Cambridge, Computer Laboratory, UK (from 2015)
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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DiCE: Dichoptic Contrast Enhancement for VR and Stereo Displays

The perceived contrast in VR/AR or stereoscopic displays is enhanced by showing a slightly modified image to each eye. The effect takes advantage of the binocular fusion mechanism, which shows bias toward the eye that can see a higher contrast.

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From pairwise comparisons and rating to a unified quality scale

The method for scaling together the results of rating and pairwise comparison experiments into a unified quality scal and the meaning units of Just Objectionable Differences. The method can be used to combine together existing datasets or to design experiments in which both protocols are combined.

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Single-frame Regularization for Temporally Stable CNNs

We make generative CNNs produce temporarily coherent video by adding regularization terms to the loss function. The regularization facilitates learning geometric transformations, which should affect the output frame in the same way as the input frame.

See more projects.


Recent papers

See all papers.


Awards and grants

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