Rafał Mantiuk

Professor of Graphics and Displays

Department of Computer Science and Technology

The Computer Laboratory

Rainbow Research Group

University of Cambridge

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)

Recent projects

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.

Quantifying reality of ultra-realistic 3-D displays

We built an ultrarealistic display system capable of reproducing 3D scenes with such fidelity that they are indistinguishable from their real counterparts. Then, we used the display to measure how reducing resolution and contrast affects perceived realism.

Streaming of rendered content with adaptive frame rate and resolution

To improve the perceived quality of streamed content while reducing rendering costs, we exploit the spatiotemporal limits of the human visual system and adaptively adjust both frame rate and resolution based on scene content and motion. Those are controlled with a neural network, trained on a dataset of game content.

See more projects.

Recent papers

• Adapting Quality Metrics to Tone Mapping
  Kenneth Chen, Dongyeon Kim, Yuta Asano, Alexandre Chapiro, Qi Sun, Rafał Mantiuk.
  In: Proc. of SIGGRAPH 2026, 2026
  (doi) (project page) (dataset) (PDF)
• Quantifying reality of ultra-realistic 3-D displays - the effect of resolution and contrast
  Joseph Gerard March, Dounia Hammou, Simon J. Watt, Rafał K. Mantiuk.
  In: Proc. of SIGGRAPH 2026, 2026
  (doi) (project page) (dataset) (PDF)
• Streaming of rendered content with adaptive frame rate and resolution
  Yaru Liu, Joseph March, Rafał K. Mantiuk.
  In: Proc. of SIGGRAPH 2026, 2026
  (doi) (project page) (dataset) (PDF)
• Evaluating quality metrics through the lenses of psychophysical measurements of low-level vision
  Dounia Hammou, Yancheng Cai, Pavan Madhusudanarao, Christos G. Bampis, Zhi Li and Rafał K. Mantiuk.
  In: International Conference on Quality of Multimedia Experience (QoMEX 2026), 2026
  (project page) (code)
• A perceptual uniformity error metric for standard and high dynamic range colour spaces
  Maryam Azimi, Minjung Kim, Graham D. Finlayson, Rafał K. Mantiuk.
  In: Human Vision and Electronic Imaging, 2026
  (PDF) (code)

See all papers.

Awards and grants

• ColorVideoVDP-ML was awarded second place in the full reference category of ICME Generalizable HDR & SDR Video Quality Measurement Grand Challenge 2025
• HDR-VDP-2 paper received the Test-of-Time Award from SIGGRAPH (2023) for "a significant and lasting impact on computer graphics and interactive techniques over at least a decade"
• HDR-VDP-3 won the 1st place in the HDR Video Quality Measurement Grand Challenge (2023) in the full-reference category
• Color Imaging Conference 2022 (CIC30) Best Paper Award for the paper Suprathreshold contrast matching between different luminance levels
• London Imaging Meeting 2022 Best Paper Award for the paper A Comparative Study on the Loss Functions for Image Enhancement Networks
• ACM SIGGRAPH European Conference on Visual Media Production (CVMP 2022) Best Paper Award for the paper Distilling Style from Image Pairs for Global Forward and Inverse Tone Mapping
• Color Imaging Conference (CIC28) Best Paper Award for the paper Practical color contrast sensitivity functions for luminance levels up to 10000 cd/m²
• Electronic Imaging / Human Vision and Electronic Imaging 2020 Best Paper Award for the paper Predicting visible flicker in temporally changing images
• The IEEE Virtual Reality 2019 Best Journal Paper - for the paper Temporal Resolution Multiplexing: Exploiting the limitations of spatio-temporal vision for more efficient VR rendering
• Electronic Imaging / Human Vision and Electronic Imaging 2019 Best Paper Award for the paper A visual model for predicting chromatic banding artifacts
• ERC Consolidator Grant (2017) - EyeCode: Perceptual encoding of high fidelity light fields
• MSCA Innovative Training Network (2018) - RealVision: Hyper-realistic Visual Experience
• EPSRC research grant (2017) - A spatio-chromatic colour appearance model for retargeting high-dynamic-range image appearance across viewing conditions
• HPC Wales Research and Innovation grant (2013/14) - Video retargeting for delivery to mobile and future display technologies
• Royal Society Research Grant (2013) - Limiting factors of perceptual image fidelity
• EPSRC grant EP/I006575/1 (2011) - Quantifying image quality in computer graphics
• Heinz Billing Award 2006

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