{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/flip-a-difference-evaluator-for-alternating","title":"FLIP: A Difference Evaluator for Alternating Images","arxiv_id":null,"date":"2020-08-26","proceeding":"Proceedings of the ACM on Computer Graphics and Interactive Techniques 2020 8","authors":["Pontus Andersson","Jim Nilsson","Tomas Akenine-Möller","Magnus Oskarsson","Kalle Åström","Mark D. Fairchild"],"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.","url_abs":"https://dl.acm.org/doi/10.1145/3406183","url_pdf":"https://research.nvidia.com/sites/default/files/node/3260/FLIP_Paper.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"flip-a-difference-evaluator-for-alternating","repo_url":"https://github.com/nvlabs/flip","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"denoising","task_name":"Denoising"}],"methods":[{"method_slug":"flip","method_name":"FLIP"}],"datasets_introduced":[],"methods_introduced":[{"slug":"flip","name":"FLIP","full_name":"FLIP"}],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}