TY - GEN
T1 - Research Proposal
T2 - 13th ACM Multimedia Systems Conference, MMSys 2022
AU - Vijayakumar, Sowmya
AU - Corcoran, Peter
AU - Flynn, Ronan
AU - Murray, Niall
N1 - Publisher Copyright:
© 2022 ACM.
PY - 2022/6/14
Y1 - 2022/6/14
N2 - This paper contains the research proposal of Sowmya Vijayakumar that was presented at the MMSys 2022 doctorial symposium. Multimedia applications can now be found across many application domains including but not limited to entertainment, communication, health, business, and education. It is becoming more and more important to understand the factors that influence user perceptual quality, and hence monitoring user quality of experience (QoE) for improving multimedia interaction and services is essential. In this PhD work, we propose advanced machine learning techniques to predict QoE from physiological signals for immersive multimedia experiences. The aim of this doctoral study is to investigate the utility of physiological responses for QoE assessment for different multimedia technologies. Here, the research questions and solutions proposed to address this challenge are presented. A multimodal QoE prediction model is being developed that integrates several physiological measurements to improve QoE prediction performance.
AB - This paper contains the research proposal of Sowmya Vijayakumar that was presented at the MMSys 2022 doctorial symposium. Multimedia applications can now be found across many application domains including but not limited to entertainment, communication, health, business, and education. It is becoming more and more important to understand the factors that influence user perceptual quality, and hence monitoring user quality of experience (QoE) for improving multimedia interaction and services is essential. In this PhD work, we propose advanced machine learning techniques to predict QoE from physiological signals for immersive multimedia experiences. The aim of this doctoral study is to investigate the utility of physiological responses for QoE assessment for different multimedia technologies. Here, the research questions and solutions proposed to address this challenge are presented. A multimodal QoE prediction model is being developed that integrates several physiological measurements to improve QoE prediction performance.
KW - Deep learning
KW - Immersive multimedia systems
KW - Machine learning
KW - Physiological signals
KW - Quality of experience
UR - http://www.scopus.com/inward/record.url?scp=85137143007&partnerID=8YFLogxK
U2 - 10.1145/3524273.3533935
DO - 10.1145/3524273.3533935
M3 - Conference contribution
AN - SCOPUS:85137143007
T3 - MMSys 2022 - Proceedings of the 13th ACM Multimedia Systems Conference
SP - 403
EP - 407
BT - MMSys 2022 - Proceedings of the 13th ACM Multimedia Systems Conference
PB - Association for Computing Machinery, Inc
Y2 - 14 June 2022 through 17 June 2022
ER -