FLIP: A Difference Evaluator for Alternating Images
(2020) In Proceedings of the ACM in Computer Graphics and Interactive Techniques 3(2). p.1-23- Abstract
- Image quality measures are becoming increasingly important in the field of computer graphics. For example, there is currently a major focus on generating photorealistic images in real time by combining path tracing with denoising, for which such quality assessment is integral. We present FLIP, which is a difference evaluator with a particular focus on the differences between rendered images and corresponding ground truths. Our algorithm produces a map that approximates the difference perceived by humans when alternating between two images. FLIP is a combination of modified existing building blocks, and the net result is surprisingly powerful. We have compared our work against a wide range of existing image difference algorithms and we have... (More)
- Image quality measures are becoming increasingly important in the field of computer graphics. For example, there is currently a major focus on generating photorealistic images in real time by combining path tracing with denoising, for which such quality assessment is integral. We present FLIP, which is a difference evaluator with a particular focus on the differences between rendered images and corresponding ground truths. Our algorithm produces a map that approximates the difference perceived by humans when alternating between two images. FLIP is a combination of modified existing building blocks, and the net result is surprisingly powerful. We have compared our work against a wide range of existing image difference algorithms and we have visually inspected over a thousand image pairs that were either retrieved from image databases or generated in-house. We also present results of a user study which indicate that our method performs substantially better, on average, than the other algorithms. To facilitate the use of FLIP, we provide source code in C++, MATLAB, NumPy/SciPy, and PyTorch. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/397ced35-879a-43a6-a225-ce31b0e984a2
- author
- Andersson, Pontus LU ; Akenine-Möller, Tomas LU ; Nilsson, Jim ; Åström, Kalle LU ; Oskarsson, Magnus LU and Fairchild, Mark
- organization
- publishing date
- 2020-08
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Proceedings of the ACM in Computer Graphics and Interactive Techniques
- volume
- 3
- issue
- 2
- article number
- 15
- pages
- 23 pages
- publisher
- Association for Computing Machinery (ACM)
- external identifiers
-
- scopus:85105482622
- ISSN
- 2577-6193
- DOI
- 10.1145/3406183
- project
- Evaluating and Improving Rendered Visual Experiences
- WASP: Wallenberg AI, Autonomous Systems and Software Program at Lund University
- language
- English
- LU publication?
- yes
- id
- 397ced35-879a-43a6-a225-ce31b0e984a2
- alternative location
- https://research.nvidia.com/sites/default/files/node/3260/FLIP_Paper.pdf
- date added to LUP
- 2020-08-31 11:48:42
- date last changed
- 2024-03-05 03:30:31
@article{397ced35-879a-43a6-a225-ce31b0e984a2, abstract = {{Image quality measures are becoming increasingly important in the field of computer graphics. For example, there is currently a major focus on generating photorealistic images in real time by combining path tracing with denoising, for which such quality assessment is integral. We present FLIP, which is a difference evaluator with a particular focus on the differences between rendered images and corresponding ground truths. Our algorithm produces a map that approximates the difference perceived by humans when alternating between two images. FLIP is a combination of modified existing building blocks, and the net result is surprisingly powerful. We have compared our work against a wide range of existing image difference algorithms and we have visually inspected over a thousand image pairs that were either retrieved from image databases or generated in-house. We also present results of a user study which indicate that our method performs substantially better, on average, than the other algorithms. To facilitate the use of FLIP, we provide source code in C++, MATLAB, NumPy/SciPy, and PyTorch.}}, author = {{Andersson, Pontus and Akenine-Möller, Tomas and Nilsson, Jim and Åström, Kalle and Oskarsson, Magnus and Fairchild, Mark}}, issn = {{2577-6193}}, language = {{eng}}, number = {{2}}, pages = {{1--23}}, publisher = {{Association for Computing Machinery (ACM)}}, series = {{Proceedings of the ACM in Computer Graphics and Interactive Techniques}}, title = {{FLIP: A Difference Evaluator for Alternating Images}}, url = {{http://dx.doi.org/10.1145/3406183}}, doi = {{10.1145/3406183}}, volume = {{3}}, year = {{2020}}, }