Rafał Mantiuk *
Photograph
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
Room
SS22
Phone
office: +44 1223 763831
E-mail
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.

Biography

Professor of Graphics and Displays, University of Cambridge, Computer Laboratory, UK (from 2023)
Professor/Reader of Graphics and Displays, University of Cambridge, Computer Laboratory, UK (2018-2023)
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 ORCID

Recent projects

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X2HDR: HDR Image Generation in a Perceptually Uniform Space

Existing pretrained diffusion models can be easily adapted to HDR generation without retraining from scratch. We show that perceptually uniform colour encodings, such as PU21, can make the distribution of HDR colour values close to that of the SDR images used to train diffusion models. This lets us easily adapt existing diffusion models (FLUX) to text-to-HDR, SDR-to-HDR, and RAW-to-HDR tasks without adding any additional model complexity.

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AROMA: Augmented Reality Optimized Tone Mapping

Optical see-through AR displays suffer severe contrast loss from ambient light. AROMA preserves the source image's perceived color, brightness, and contrast using a global tone curve that minimizes contrast distortion, adaptive local detail restoration, and saturation correction. Perceived quality matches a 45–59% luminance increase, enabling substantial display power savings.

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Adapting Quality Metrics to Tone Mapping

A simple addition of a display model and a perceptual encoding (PU21) can adapt many existing quality metrics to the task of evaluating the quality of tone-mapped images (with respect to the HDR reference). We show that such adapted metrics, including our modified ColorVideoVDP, outperform dedicated tone-mapping metrics.

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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.

Last modified: Sat 24 Oct 12:34:24 BST 2026