Image distortion classification and detection is an im-portant task in many applications. For example when com-pressing images, if we know the exact location of the distortion, then it is possible to re-compress images by adjusting the local compression level dynamically. In this paper, we address the problem of detecting the distortion region and classifying the distortion type of a given image. We show that our model significantly outperforms the state-of-The-Art distortion classifier, and report accurate detection results for the first time. We expect that such results prove the use-fulness of our approach in many potential applications such as image compression or distortion restoration.
N.Ahn and K.-A. Sohn were supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education [NRF-2016R1D1A1B03933875], and B.Kang by [NRF-2016R1A6A3A11932796].