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/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

AR-DAVID

AR-DAVID is a video quality dataset that captures how distortions due to display technologies (e.g, waveguide non-uniformity) are going to be seen on an optical see-through AR display. We found a simple blending of the environment and display light cannot predict the visibility of distortions.

elaTCSF: A Temporal Contrast Sensitivity Function for Flicker Detection and Modeling Variable Refresh Rate Flicker

elaTCSF is a temporal contrast sensitivity model that can predict the visibility of a flicker, such as a flicker found in variable-refresh-rate OLED and LCD displays.

ColorVideoVDP

ColorVideoVDP is a differentiable image and video quality metric that models human color and spatiotemporal vision. It is targeted and calibrated to assess image distortions due to AR/VR display technologies and video streaming, and it can handle both SDR and HDR content.

See more projects.

Recent papers

• Do computer vision foundation models learn the low-level characteristics of the human visual system?
  Yancheng Cai, Fei Yin, Dounia Hammou, Rafał K. Mantiuk.
  In: Proc. Computer Vision and Pattern Recognition (CVPR), 2025
  [CVPR Highlight paper - top 14%]
  (project page) (PDF) (code)
• Training Neural Networks on RAW and HDR Images for Restoration Tasks
  Andrew Yanzhe Ke, Lei Luo, Xiaoyu Xiang, Yuchen Fan, Rakesh Ranjan, Alexandre Chapiro, Rafał K. Mantiuk.
  In: New Trends in Image Restoration and Enhancement (CVPR workshop), 2025
  (arxiv) (PDF)
• G-SemTMO: Tone Mapping with a Trainable Semantic Graph
  Abhishek Goswami, Erwan Bernard, Aru Ranjan Singh, Wolf Hauser, Frederic Dufaux, and Rafal Mantiuk.
  In: IEEE Access, 2024
  (doi) (PDF)
• Stereoscopic Depth Perception Through Foliage
  Robert Kerschner, Rakesh John Amala Arokia Nathan, Rafał K. Mantiuk, and Oliver Bimber.
  In: Scientific Reports, 14(23056), 2024
  (doi) (project page) (PDF)
• Modelling contrast matching across luminance levels
  Maliha Ashraf and Rafał K. Mantiuk.
  In: Color Imaging Conference (CIC32), 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.