{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T06:06:56Z","timestamp":1709273216950},"reference-count":0,"publisher":"IOS Press","license":[{"start":{"date-parts":[[2020,11,9]],"date-time":"2020-11-09T00:00:00Z","timestamp":1604880000000},"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":[[2020,11,9]]},"abstract":"<jats:p>Sample Adaptive Offset (SAO) in High Efficient Video Coding (HEVC) is a new technic to improve the quality of videos. It categories the pixels and choices the best way by adding some offsets to the reconstructed video. So, it causes a dramatically increased computational complexity. According to the dependency of sample adaptive offset and visual saliency map, an improved SAO method is proposed in order to minimize the coding time of SAO by skipping some RD cost calculation. Experimental result shows that the proposed method reduces 27.02% SAO encoding time with negligible performance loss.<\/jats:p>","DOI":"10.3233\/faia200710","type":"book-chapter","created":{"date-parts":[[2020,11,10]],"date-time":"2020-11-10T17:36:02Z","timestamp":1605029762000},"source":"Crossref","is-referenced-by-count":1,"title":["The Improved Algorithm of Sample Adaptive Offset Based on Visual Saliency"],"prefix":"10.3233","author":[{"given":"Nana","family":"Shan","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, Taishan University"}]},{"given":"Wei","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Electronics and Information, Northwestern Polytechnical University"}]},{"given":"Zhemin","family":"Duan","sequence":"additional","affiliation":[{"name":"Department of Electronics and Information, Northwestern Polytechnical University"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Fuzzy Systems and Data Mining VI"],"original-title":[],"link":[{"URL":"http:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA200710","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,11,10]],"date-time":"2020-11-10T17:36:02Z","timestamp":1605029762000},"score":1,"resource":{"primary":{"URL":"http:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA200710"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia200710","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,9]]}}}