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Machine Learning for Multimedia Communications

Thomos, N; Maugey, T; Toni, L; (2022) Machine Learning for Multimedia Communications. Sensors , 22 (3) , Article 819. 10.3390/s22030819. Green open access

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Machine learning is revolutionizing the way multimedia information is processed and transmitted to users. After intensive and powerful training, some impressive efficiency/accuracy improvements have been made all over the transmission pipeline. For example, the high model capacity of the learning-based architectures enables us to accurately model the image and video behavior such that tremendous compression gains can be achieved. Similarly, error concealment, streaming strategy or even user perception modeling have widely benefited from the recent learningoriented developments. However, learning-based algorithms often imply drastic changes to the way data are represented or consumed, meaning that the overall pipeline can be affected even though a subpart of it is optimized. In this paper, we review the recent major advances that have been proposed all across the transmission chain, and we discuss their potential impact and the research challenges that they raise.

Type: Article
Title: Machine Learning for Multimedia Communications
Open access status: An open access version is available from UCL Discovery
DOI: 10.3390/s22030819
Publisher version: https://doi.org/10.3390/s22030819
Language: English
Additional information: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Keywords: multimedia communications; machine learning; video coding; image coding; error concealment; video streaming; QoE assessment; content consumption; channel coding; caching
UCL classification: UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng
UCL > Provost and Vice Provost Offices > UCL BEAMS
URI: https://discovery.ucl.ac.uk/id/eprint/10142844
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