{"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/pytorch-image-quality-metrics-for-image","title":"PyTorch Image Quality: Metrics for Image Quality Assessment","arxiv_id":"2208.14818","date":"2022-08-31","proceeding":null,"authors":["Sergey Kastryulin","Jamil Zakirov","Denis Prokopenko","Dmitry V. Dylov"],"abstract":"Image Quality Assessment (IQA) metrics are widely used to quantitatively estimate the extent of image degradation following some forming, restoring, transforming, or enhancing algorithms. 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