Machine Learning for Multimedia Communications
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-ba...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-01-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/3/819 |
| _version_ | 1827658889401729024 |
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| author | Nikolaos Thomos Thomas Maugey Laura Toni |
| author_facet | Nikolaos Thomos Thomas Maugey Laura Toni |
| author_sort | Nikolaos Thomos |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T23:10:56Z |
| format | Article |
| id | doaj.art-9c30f489ac3c443c9517c9e73e84bd4a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T23:10:56Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-9c30f489ac3c443c9517c9e73e84bd4a2023-11-23T17:45:55ZengMDPI AGSensors1424-82202022-01-0122381910.3390/s22030819Machine Learning for Multimedia CommunicationsNikolaos Thomos0Thomas Maugey1Laura Toni2School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UKInria, 35042 Rennes, FranceDepartment of Electrical & Electrical Engineering, University College London (UCL), London WC1E 6AE, UKMachine 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.https://www.mdpi.com/1424-8220/22/3/819multimedia communicationsmachine learningvideo codingimage codingerror concealmentvideo streaming |
| spellingShingle | Nikolaos Thomos Thomas Maugey Laura Toni Machine Learning for Multimedia Communications Sensors multimedia communications machine learning video coding image coding error concealment video streaming |
| title | Machine Learning for Multimedia Communications |
| title_full | Machine Learning for Multimedia Communications |
| title_fullStr | Machine Learning for Multimedia Communications |
| title_full_unstemmed | Machine Learning for Multimedia Communications |
| title_short | Machine Learning for Multimedia Communications |
| title_sort | machine learning for multimedia communications |
| topic | multimedia communications machine learning video coding image coding error concealment video streaming |
| url | https://www.mdpi.com/1424-8220/22/3/819 |
| work_keys_str_mv | AT nikolaosthomos machinelearningformultimediacommunications AT thomasmaugey machinelearningformultimediacommunications AT lauratoni machinelearningformultimediacommunications |