A. Chetouani, Azeddine Beghdadi, A. Bouzerdoum, Mohamed Deriche
{"title":"A new scheme for no reference image quality assessment","authors":"A. Chetouani, Azeddine Beghdadi, A. Bouzerdoum, Mohamed Deriche","doi":"10.1109/IPTA.2012.6469534","DOIUrl":null,"url":null,"abstract":"In this paper, we propose to overcome one of the limitations of No Reference (NR) Image Quality Metrics (IQMs). Indeed, this kind of metrics is generally distortion-based and can be used only for a specific degradation such as ringing, blur or blocking. We propose to detect and identify the type of the degradation contained in the image before quantifying its quality. The degradation type is here identified using a Linear Discriminant Analysis (LDA) classifier. Then, the NR-IQM is selected according to the degradation type. We focus our work on the more common artefacts and degradations: blocking, ringing, blur and noise. The efficiency of the proposed method is evaluated in terms of correct classification across the considered degradations and artefacts.","PeriodicalId":267290,"journal":{"name":"2012 3rd International Conference on Image Processing Theory, Tools and Applications (IPTA)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 3rd International Conference on Image Processing Theory, Tools and Applications (IPTA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IPTA.2012.6469534","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
In this paper, we propose to overcome one of the limitations of No Reference (NR) Image Quality Metrics (IQMs). Indeed, this kind of metrics is generally distortion-based and can be used only for a specific degradation such as ringing, blur or blocking. We propose to detect and identify the type of the degradation contained in the image before quantifying its quality. The degradation type is here identified using a Linear Discriminant Analysis (LDA) classifier. Then, the NR-IQM is selected according to the degradation type. We focus our work on the more common artefacts and degradations: blocking, ringing, blur and noise. The efficiency of the proposed method is evaluated in terms of correct classification across the considered degradations and artefacts.