- 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
- 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 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/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)
Recent projects
We reproduce gloss on our ultra-realistic HDR 3D display so that it appears identical to the gloss of real objects seen side by side. Our observation is that the dynamic range, absolute luminance and tone-curve are the factors that influence gloss perception the most.
Spatiotemporal denoising for path tracing relies on a trained multi-scale decomposition (pyramid), which can better preserve details and avoid artifacts of previous methods.
Inaccurate information on exposure times result in banding artifacts when merging a stack of multiple exposures into an HDR image. We show how the exposure times can be robustly estimated from an exposure stack while accounting for camera noise and pixel misalignment.
See more projects.
Recent papers
- Impact of focus cue presentation on perceived realism of 3-D scene structure: implications for scene perception and for display technology
Joseph March, Anantha Krishnan, Rafał K. Mantiuk and Simon J. Watt.
In: Journal of Vision, 24, article no. 13, 2024
(doi) (project page) - Perceptual Assessment and Optimization of HDR Image Rendering
Peibei Cao, Rafał K. Mantiuk, Kede Ma.
In: Proc. Computer Vision and Pattern Recognition (CVPR), 2024 - Spatiotemporal contrast sensitivity functions: predictions for the critical flicker frequency
Ali Bozorgian, Maliha Ashraf and Rafał K. Mantiuk.
In: Human Vision and Electronic Imaging, 2024
(doi) (PDF) (code) - Image quality assessment across viewing distances: A comparison study of CSF-based and rescaling-based metrics
Dounia Hammou, Lukas Krasula, Christos G. Bampis, Zhi Li and Rafał K. Mantiuk.
In: Human Vision and Electronic Imaging, 2024
(PDF) - Color calibration methods for OLED displays
Maliha Ashraf, Alejandro Sztrajman, Dounia Hammou and Rafał K. Mantiuk.
In: Color Imaging XXIX: Displaying, Processing, Hardcopy, and Applications, 2024
(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.