{"title":"Image Distortion Detection Using Convolutional Neural Network","authors":"Namhyuk Ahn, Byungkon Kang, Kyung-ah Sohn","doi":"10.1109/ACPR.2017.95","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":426561,"journal":{"name":"2017 4th IAPR Asian Conference on Pattern Recognition (ACPR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 4th IAPR Asian Conference on Pattern Recognition (ACPR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ACPR.2017.95","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 11
Abstract
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.