This is a brief overview of the topics I am interested in.
Display algorithms: tone, color and enhancement
Digital photographs look quite realistic on a computer screen, although they are not as bright, they have lower contrast and different colors than the the real-world scenes. The visual system can tolerate a significant mismatch between the reproduced scened and the original. The understanding of these apects of the visual system let us create better algorithms for tone and color reproduction, and image enhancement.
Relevant publications
- Real-time noise-aware tone-mapping and its use in luminance retargeting
Gabriel Eilertsen, Rafał K. Mantiuk and Jonas Unger.
In: Proc. of IEEE International Conference on Image Processing (ICIP'16), pp. 894-898, 2016
(doi) (PDF) - Simulating and compensating changes in appearance between day and night vision
Robert Wanat and Rafał K. Mantiuk.
In: ACM Transactions on Graphics (Proc. of SIGGRAPH 2014), 33(4), article no. 147, 2014
(doi) (project page) (PDF) - Color Correction for Tone Mapping
Radosław Mantiuk, Rafał Mantiuk, Anna Tomaszewska, Wolfgang Heidrich.
In: Computer Graphics Forum (Proc. of EUROGRAPHICS'09), 28(2), pp. 193-202, 2009
(doi) (project page) (PDF) - Display Adaptive Tone Mapping
Rafał Mantiuk, Scott Daly, Louis Kerofsky.
In: ACM Transactions on Graphics (Proc. of SIGGRAPH'08), 27(3), article no. 68, 2008
(doi) (project page) (PDF) (errata)
Image quality metrics and visual models
Visual difference metrics can predict whether differences between two images are visible to the human observer or not. Such metrics are used for testing either visibility of information (whether we can see important visual information) or visibility of noise (to make sure we do not see any distortions in images, e.g. due to lossy compression). We introduce a High Dynamic Range Visible Difference Predictor, which can work within the complete range of luminance the human eye can see and therefore, unlike the other visible difference metrics, can be used for the full range of lighting conditions that can be met in real-word situations.
Relevant publications
- Towards a quality metric for dense light fields
Vamsi K. Adhikarla, Marek Vinkler, Denis Sumin, Rafal K. Mantiuk, Karol Myszkowski, Hans-Peter Seidel, Piotr Didyk.
In: Proc. Computer Vision and Pattern Recognition (CVPR), pp. 3720-3729, 2017
(doi) (project page) (PDF) - HDR-VDP-2.2: A Calibrated Method for Objective Quality Prediction of High Dynamic Range and Standard Images
Manish Narwaria, Rafal K. Mantiuk, Mattheiu Perreira Da Silva and Patrick Le Callet.
In: Journal of Electronic Imaging, 24(1), 2015
(doi) (PDF) - HDR-VDP-2: A calibrated visual metric for visibility and
quality predictions in all luminance conditions
Rafał Mantiuk, Kil Joong Kim, Allan G. Rempel and Wolfgang Heidrich.
In: ACM Transactions on Graphics (Proc. of SIGGRAPH'11), 30(4), article no. 40, 2011
(doi) (project page) (PDF) - Dynamic Range Independent Image Quality Assessment
Tunç O. Aydin, Rafał Mantiuk, Karol Myszkowski, Hans-Peter Seidel.
In: ACM Transactions on Graphics (Proc. of SIGGRAPH'08), 27(3), article no. 69, 2008
(doi) (project page) (PDF)
High Dynamic Range Image and Video Compression
Commonly used image and video formats encode visual information using 8-bit per color channel. Such representation, although suitable for typical LCD or CRT monitors, is unsufficient for next generation of high-dynamic range displays. To overcome the limitations of the existing image encoding formats, we extended existing formats to efficiently encode all visual information that the human eye can see: luminance levels from star light to sun light and complete color gamut.
Relevant publications
- JPEG XT: A Compression Standard for HDR and WCG Images [Standards in a Nutshell]
A. Artusi, R. K. Mantiuk, T. Richter, P. Korshunov, P. Hanhart, T. Ebrahimi, M. Agostinelli.
In: IEEE Signal Processing Magazine, 33(2), pp. 118-124, 2016
(doi) - Backward Compatible High Dynamic Range MPEG Video Compression
Rafał Mantiuk, Alexander Efremov, Karol Myszkowski, Hans-Peter Seidel.
In: ACM Transactions on Graphics (Proc. of SIGGRAPH'06), 25(3), pp. 713-723, 2006
(project page) (PDF) - High Dynamic Range Image and Video Compression - Fidelity Matching Human Visual
Performance
Rafał Mantiuk, Grzegorz Krawczyk, Karol Myszkowski and Hans-Peter Seidel.
In: Proc. of IEEE International Conference on Image Processing (ICIP'07), pp. 9-12, 2007
(PDF) - Perception-motivated High Dynamic Range Video Encoding
Rafał Mantiuk, Grzegorz Krawczyk, Karol Myszkowski, Hans-Peter Seidel.
In: ACM Transactions on Graphics (Proc. of SIGGRAPH'04), 23(3), pp. 733-741, 2004
(project page) (PDF)