ꟻLIP: A Difference Evaluator for Alternating Images

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 ꟻLIP, 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. ꟻLIP 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 ꟻLIP, we provide source code in C++, MATLAB, NumPy/SciPy, and PyTorch.

Please check out our gallery of images and error maps.
NOTE: The image and error map gallery is currently unavailable. We are working on getting it back up as soon as possible. See supplemental material below for a subset of the images in the gallery.

Hindsight: in the paper we advocated for the weighted median, computed from the weighted histogram, but this is not ideal. If a single number is to be used, then we recommend using the mean ꟻLIP value instead.

How to write an ꟻ: 
Unicode: fileformat.info
LaTeX: \usepackage{mathtools} \usepackage{xspace} \newcommand{\FLIP}{\protect\reflectbox{F}LIP\xspace}

Magnus Oskarsson (Lund University)
Kalle Åström (Lund University)
Mark D. Fairchild (Rochester Institute of Technology)
Publication Date: 
Monday, July 13, 2020
Research Area: