VVC In-Loop Filtering Based on Deep Convolutional Neural Network.
| Autor: | Bouaafia S; University of Monastir, Laboratory of Electronics and Microelectronics, Faculty of Sciences of Monastir, Monastir, Tunisia., Messaoud S; University of Monastir, Laboratory of Electronics and Microelectronics, Faculty of Sciences of Monastir, Monastir, Tunisia., Khemiri R; University of Monastir, Laboratory of Electronics and Microelectronics, Faculty of Sciences of Monastir, Monastir, Tunisia.; University of Gabes, Higher Institute of Computer Science and Multimedia of Gabes, Gabes, Tunisia., Sayadi FE; University of Sousse, National Engineering School of Sousse, Sousse, Tunisia. |
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| Jazyk: | angličtina |
| Zdroj: | Computational intelligence and neuroscience [Comput Intell Neurosci] 2021 Jul 07; Vol. 2021, pp. 9912839. Date of Electronic Publication: 2021 Jul 07 (Print Publication: 2021). |
| DOI: | 10.1155/2021/9912839 |
| Abstrakt: | 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's quality of service (QoS). Versatile video coding (VVC) is the latest video coding standard that achieves significant coding efficiency. VVC will help spread high-quality video services and emerging applications, such as high dynamic range (HDR), high frame rate (HFR), and omnidirectional 360-degree 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-activated squeeze-and-excitation deep convolutional neural network (WSE-DCNN) technique-based video quality enhancement for VVC. Thus, the VVC conventional in-loop filtering will be replaced by the suggested WSE-DCNN 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 -2.85%, -8.89%, and -10.05% BD-rate reduction of the luma ( Y ) and both chroma ( U , V ) components, respectively, under random access profile. Competing Interests: The authors declare that they have no conflicts of interest. (Copyright © 2021 Soulef Bouaafia et al.) |
| Databáze: | MEDLINE |
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