dc.contributor.author | Chetouani, Aladine | |
dc.contributor.author | Pedersen, Marius | |
dc.date.accessioned | 2020-02-04T08:49:03Z | |
dc.date.available | 2020-02-04T08:49:03Z | |
dc.date.created | 2019-09-18T08:17:48Z | |
dc.date.issued | 2019 | |
dc.identifier.isbn | 978-1-5386-6249-6 | |
dc.identifier.uri | http://hdl.handle.net/11250/2639455 | |
dc.description.abstract | A plethora of image quality metrics have been proposed in the literature. These metrics aims to estimate the perceptual image quality automatically. One important key aspect that the perceived quality is dependent on is the viewing distance from the observer to the image. In this study, we propose to consider this information by estimating the quality of a given image without a reference image for different viewing distances. For that, a Convolutional Neural Network (CNN) model was used in this study. Relevant patches are first selected from the image and they are then used as inputs to the CNN. The selection is here based on saliency information. The used CNN is composed of two outputs that correspond to the predicted subjective scores for two viewing distances (50 cm and 100 cm). Our method was evaluated using the Colourlab Image Database: Image Quality (CID:IQ) that provides subjective scores at two different viewing distances. The obtained results show the efficiency of our method. | nb_NO |
dc.language.iso | eng | nb_NO |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | nb_NO |
dc.relation.ispartof | 2019 IEEE International Conference on Image Processing | |
dc.title | On the Use of a Convolutional Neural Network to Predict Perceptual Quality of Images without Reference for Different Viewing Distances | nb_NO |
dc.type | Chapter | nb_NO |
dc.description.version | acceptedVersion | nb_NO |
dc.source.pagenumber | 1009-1013 | nb_NO |
dc.identifier.doi | 10.1109/ICIP.2019.8803056 | |
dc.identifier.cristin | 1725970 | |
dc.description.localcode | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | nb_NO |
cristin.unitcode | 194,63,10,0 | |
cristin.unitname | Institutt for datateknologi og informatikk | |
cristin.ispublished | true | |
cristin.fulltext | postprint | |
cristin.qualitycode | 1 | |