TY - JOUR
T1 - FLIP: A Difference Evaluator for Alternating Images
AU - Andersson, Pontus
AU - Akenine-Möller, Tomas
AU - Nilsson, Jim
AU - Åström, Kalle
AU - Oskarsson, Magnus
AU - Fairchild, Mark
PY - 2020/8
Y1 - 2020/8
N2 - 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.
AB - 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.
UR - https://research.nvidia.com/publication/2020-07_FLIP
U2 - 10.1145/3406183
DO - 10.1145/3406183
M3 - Article
SN - 2577-6193
VL - 3
SP - 1
EP - 23
JO - Proceedings of the ACM in Computer Graphics and Interactive Techniques
JF - Proceedings of the ACM in Computer Graphics and Interactive Techniques
IS - 2
M1 - 15
ER -