Deep learning-based video quality enhancement for the new versatile video coding.

Autor: Bouaafia S; Laboratory of Electronics and Microelectronics, Faculty of Sciences of Monastir, University of Monastir, Monastir, Tunisia., Khemiri R; Laboratory of Electronics and Microelectronics, Faculty of Sciences of Monastir, University of Monastir, Monastir, Tunisia.; Higher Institute of Computer Science and Multimedia of Gabes, University of Gabes, Gabes, Tunisia., Messaoud S; Laboratory of Electronics and Microelectronics, Faculty of Sciences of Monastir, University of Monastir, Monastir, Tunisia., Ben Ahmed O; XLIM-CNRS, Bât SP2MI, University of Poitiers, 11 Bd Marie et Pierre Curie, 86962 Chasseneuil Cedex, France., Sayadi FE; Laboratory of Networked Objects Control and Communication Systems, National Engineering School of Sousse, University of Sousse, Sousse, Tunisia.
Jazyk: angličtina
Zdroj: Neural computing & applications [Neural Comput Appl] 2022; Vol. 34 (17), pp. 14135-14149. Date of Electronic Publication: 2021 Sep 08.
DOI: 10.1007/s00521-021-06491-9
Abstrakt: Multimedia IoT (M-IoT) is an emerging type of Internet of things (IoT) relaying multimedia data (images, videos, audio and speech, etc.). The rapid growth of M-IoT devices enables the creation of a massive volume of multimedia data with different characteristics and requirements. With the development of artificial intelligence (AI), AI-based multimedia IoT systems have been recently designed and deployed for various video-based services for contemporary daily life, like video surveillance with high definition (HD) and ultra-high definition (UHD) and mobile multimedia streaming. These new services need higher video quality in order to meet the quality of experience (QoE) required by the users. Versatile video coding (VVC) is the new video coding standard that achieves significant coding efficiency over its predecessor high-efficiency video coding (HEVC). Moreover, VVC can achieve up to 30% BD rate savings compared to HEVC. Inspired by the rapid advancements in deep learning, we propose in this paper a wide-activated squeeze-and-excitation deep convolutional neural network (WSE-DCNN) technique-based video quality enhancement for VVC. Therefore, we replace the conventional in-loop filtering in VVC by the proposed WSE-DCNN model that eliminates the compression artifacts in order to improve visual quality and hence increase the end user QoE. The obtained results prove that the proposed in-loop filtering technique achieves - 2.85 %, - 8.89 %, and - 10.05 % BD rate reduction for luma and both chroma components under random access configuration. Compared to the traditional CNN-based filtering approaches, the proposed WSE-DCNN-based in-loop filtering framework achieves efficient performance in terms of RD cost.
Competing Interests: Conflict of interestThe authors declare that they have no conflict of interest.
(© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021.)
Databáze: MEDLINE
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