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

stelaCSF - A Unified Model of Contrast Sensitivity as the Function of Spatio-Temporal Frequency, Eccentricity, Luminance and Area

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.

Dark Stereo: Improving Depth Perception Under Low Luminance

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.

Comparison of Single-image HDR Reconstruction Methods — The Caveats of Quality Assessment

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.

Recent papers

• 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
  (project page) (PDF)
• Modeling contrast sensitivity of discs
  Maliha Ashraf, Rafał K. Mantiuk and Alexandre Chapiro.
  In: Human Vision and Electronic Imaging, 2023
  (PDF)
• Critical Flicker Frequency (CFF) at high luminance levels
  Alexandre Chapiro, Nathan Matsuda, Maliha Ashraf, and Rafał K. Mantiuk.
  In: Human Vision and Electronic Imaging, 2023
  (PDF)
• Impact of correct and simulated focus cues on perceived realism
  Josepth March, Anantha Krishnan, Simon J. Watt, Marek Wernikowski, Hongyun Gao, Ali Özgür Yöntem and Rafał K. Mantiuk.
  In: Proc. of SIGGRAPH Asia 2022, article no. 22, 2022
  (doi) (project page) (PDF)
• Suprathreshold contrast matching between different luminance levels
  Maliha Ashraf, Rafał K. Mantiuk, Jasna Martinovic and Sophie Wuerger.
  In: Color Imaging Conference (CIC30), 2022
  [Best Paper Award]
  (doi) (PDF)

See all papers.

Awards and grants

• HDR-VDP-3 won the 1st place in the HDR Video Quality Measurement Grand Challenge (2023) in the full-reference category
• Color Imaging Conference (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) - 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.