Research Repository

Machine Learning for Multimedia Communications

Thomos, Nikolaos and Maugey, Thomas and Toni, Laura (2022) 'Machine Learning for Multimedia Communications.' Sensors, 22 (3). p. 819. ISSN 1424-8220

sensors-1536339-english done.pdf - Published Version
Available under License Creative Commons Attribution.

Download (2MB) | Preview


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: Elements
Depositing User: Elements
Date Deposited: 31 Jan 2022 10:45
Last Modified: 03 Mar 2022 06:39

Actions (login required)

View Item View Item