{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T13:13:22Z","timestamp":1666012402266},"reference-count":0,"publisher":"IOS Press","license":[{"start":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T00:00:00Z","timestamp":1665964800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,10,17]]},"abstract":"<jats:p>Image quality assessment (IQA) algorithms are critical for determining the quality of high-resolution photographs. This work proposes a hybrid NR IQA approach that uses deep transfer learning to enhance classic NR IQA with deep learning characteristics. Firstly, we simulate a pseudo reference image (PRI) from the input image. Then, we used a pre-trained inception-v3 deep feature extractor to generate the feature maps from the input distorted image and PRI. The distance between the feature maps of the input distorted image and PRI are measured using the local structural similarity (LSS) method. A nonlinear mapping function is used to calculate the final quality scores. When compared to previous work, the proposed method has a promising performance.<\/jats:p>","DOI":"10.3233\/faia220345","type":"book-chapter","created":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T12:30:16Z","timestamp":1666009816000},"source":"Crossref","is-referenced-by-count":0,"title":["Referenceless Image Quality Assessment Utilizing Deep Transfer-Learned Features"],"prefix":"10.3233","author":[{"given":"Basma","family":"Ahmed","sequence":"first","affiliation":[{"name":"Faculty of Computers and Information, South Valley University, Qena, Egypt"}]},{"given":"Osama A.","family":"Omer","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, Aswan University, Aswan, Egypt"}]},{"given":"Amal","family":"Rashed","sequence":"additional","affiliation":[{"name":"Faculty of Computers and Information, South Valley University, Qena, Egypt"}]},{"given":"Domenec","family":"Puig","sequence":"additional","affiliation":[{"name":"Computer Engineering and Mathematics Department, University Rovira i Virgili, Tarragona, Spain"}]},{"given":"Mohamed","family":"Abdel-Nasser","sequence":"additional","affiliation":[{"name":"Computer Engineering and Mathematics Department, University Rovira i Virgili, Tarragona, Spain"},{"name":"Electrical Engineering Department, Aswan University, Aswan, Egypt"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Artificial Intelligence Research and Development"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA220345","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T12:30:22Z","timestamp":1666009822000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA220345"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,17]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia220345","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,17]]}}}