{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,20]],"date-time":"2025-12-20T22:11:07Z","timestamp":1766268667234,"version":"3.41.2"},"reference-count":34,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,7,8]],"date-time":"2021-07-08T00:00:00Z","timestamp":1625702400000},"content-version":"vor","delay-in-days":188,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>With the rapid advancement in many multimedia applications, such as video gaming, computer vision applications, and video streaming and surveillance, video quality remains an open challenge. Despite the existence of the standardized video quality as well as high definition (HD) and ultrahigh definition (UHD), enhancing the quality for the video compression standard will improve the video streaming resolution and satisfy end user\u2019s quality of service (QoS). Versatile video coding (VVC) is the latest video coding standard that achieves significant coding efficiency. VVC will help spread high\u2010quality video services and emerging applications, such as high dynamic range (HDR), high frame rate (HFR), and omnidirectional 360\u2010degree multimedia compared to its predecessor high efficiency video coding (HEVC). Given its valuable results, the emerging field of deep learning is attracting the attention of scientists and prompts them to solve many contributions. In this study, we investigate the deep learning efficiency to the new VVC standard in order to improve video quality. However, in this work, we propose a wide\u2010activated squeeze\u2010and\u2010excitation deep convolutional neural network (WSE\u2010DCNN) technique\u2010based video quality enhancement for VVC. Thus, the VVC conventional in\u2010loop filtering will be replaced by the suggested WSE\u2010DCNN technique that is expected to eliminate the compression artifacts in order to improve visual quality. Numerical results demonstrate the efficacy of the proposed model achieving approximately \u22122.85%, \u22128.89%, and \u221210.05% BD\u2010rate reduction of the luma (<jats:italic>Y<\/jats:italic>) and both chroma (<jats:italic>U<\/jats:italic>, <jats:italic>V<\/jats:italic>) components, respectively, under random access profile.<\/jats:p>","DOI":"10.1155\/2021\/9912839","type":"journal-article","created":{"date-parts":[[2021,7,8]],"date-time":"2021-07-08T17:50:14Z","timestamp":1625766614000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["VVC In\u2010Loop Filtering Based on Deep Convolutional Neural Network"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0657-6900","authenticated-orcid":false,"given":"Soulef","family":"Bouaafia","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5205-4914","authenticated-orcid":false,"given":"Seifeddine","family":"Messaoud","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1790-3033","authenticated-orcid":false,"given":"Randa","family":"Khemiri","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3875-4153","authenticated-orcid":false,"given":"Fatma Elzahra","family":"Sayadi","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,7,8]]},"reference":[{"key":"e_1_2_8_1_2","unstructured":"BrossB. 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