- 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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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)
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.
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.
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.
See more projects.
- The effect of display capabilities on the gloss consistency between real and virtual objects
Bin Chen, Akshay Jindal, Michal Piovarči, Chao Wang, Hans-Peter Seidel, Piotr Didyk, Karol Myszkowski, Ana Serrano and Rafał K. Mantiuk.
In: Proc. of SIGGRAPH Asia 2023, 2023
(doi) (project page) (PDF)
- Neural partitioning pyramids for denoising Monte Carlo renderings
Martin Balint, Krzysztof Wolski, Karol Myszkowski, Hans-Peter Seidel and Rafal Mantiuk.
In: Proc. of SIGGRAPH 2023, 2023
(doi) (project page) (PDF)
- Robust estimation of exposure ratios in multi-exposure image stacks
Param Hanji and Rafal K. Mantiuk.
In: IEEE Transactions on Computational Imaging, 9, article no. 721-731, 2023
(doi) (PDF) (code)
- Comparison of metrics for predicting image and video quality at varying viewing distances
Dounia Hammou, Lukas Krasula, Christos G. Bampis, Zhi Li, Rafał K. Mantiuk.
In: IEEE MMSP 2023, 2023
- HDR-VDP-3: A multi-metric for predicting image differences, quality and contrast distortions in high dynamic range and regular content
Rafal K. Mantiuk, Dounia Hammou, Param Hanji.
In: arXiv pre-print arXiv:2304.13625, 2023
(project page) (PDF)
See all papers.
Awards and grants
- 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.