Thomos, Nikolaos and Maugey, Thomas and Toni, Laura (2022) Machine Learning for Multimedia Communications. Sensors, 22 (3). p. 819. DOI https://doi.org/10.3390/s22030819
Thomos, Nikolaos and Maugey, Thomas and Toni, Laura (2022) Machine Learning for Multimedia Communications. Sensors, 22 (3). p. 819. DOI https://doi.org/10.3390/s22030819
Thomos, Nikolaos and Maugey, Thomas and Toni, Laura (2022) Machine Learning for Multimedia Communications. Sensors, 22 (3). p. 819. DOI https://doi.org/10.3390/s22030819
Abstract
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 learning-oriented 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.
Item Type: | Article |
---|---|
Uncontrolled Keywords: | multimedia communications; machine learning; video coding; image coding; error concealment; video streaming; QoE assessment; content consumption; channel coding; caching |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 31 Jan 2022 10:45 |
Last Modified: | 30 Oct 2024 16:36 |
URI: | http://repository.essex.ac.uk/id/eprint/32158 |
Available files
Filename: sensors-1536339-english done.pdf
Licence: Creative Commons: Attribution 3.0