| dc.contributor.author | Pedersen, Marius | |
| dc.contributor.author | Cherepkova, Olga | |
| dc.date.accessioned | 2020-09-14T08:17:42Z | |
| dc.date.available | 2020-09-14T08:17:42Z | |
| dc.date.created | 2020-06-29T20:15:20Z | |
| dc.date.issued | 2020 | |
| dc.identifier.citation | International journal of imaging and robotics. 2020, 20 (3), 39-71. | en_US |
| dc.identifier.issn | 2231-525X | |
| dc.identifier.uri | https://hdl.handle.net/11250/2677556 | |
| dc.description.abstract | In this work we investigate the influence of image content on image quality assessment. We describe image content through a set of features, such as busyness, colorfulness, lightness, sharpness and entropy, investigating the influence of each feature separately and
their combination on the performance of image quality metrics. Based on the found dependencies we developed prediction models based on SVM and Random Forest machine learning techniques, which predict correctly and wrongly estimated images (compared to observer score) by an image quality metric. | en_US |
| dc.language.iso | eng | en_US |
| dc.publisher | Centre for Environment, Social and Economic Research Publications | en_US |
| dc.title | Content-based image quality assessment | en_US |
| dc.type | Peer reviewed | en_US |
| dc.type | Journal article | en_US |
| dc.description.version | acceptedVersion | en_US |
| dc.source.pagenumber | 39-71 | en_US |
| dc.source.volume | 20 | en_US |
| dc.source.journal | International journal of imaging and robotics | en_US |
| dc.source.issue | 3 | en_US |
| dc.identifier.cristin | 1817683 | |
| dc.description.localcode | This article will not be available due to copyright restrictions (c) 2020 by Centre for Environment, Social and Economic Research Publications | en_US |
| cristin.ispublished | true | |
| cristin.fulltext | postprint | |
| cristin.qualitycode | 1 | |
