About the Project
for other streaming technologies alone are not sufficient . Recently many works have focused on developing QoE models targeting HAS based applications . Also, the recently published ITU-T Recommendation series P.1203 proposes a parametric bitstream-based model for the quality assessment of progressive download and adaptive audiovisual streaming services over reliable transport.
Machine learning has been proposed in the recent years to develop models for video quality assessment, with promising results .
The main goal of this project is to develop new QoE models for HAS based on machine learning, including the most recent video presentation formats (e.g. UHD, HDR, 360 degrees video, light field imaging). The models will consider the information available at the different points of the transmission chain, e.g., both “full reference” and “no reference” models will be considered, the latter in the case when the original video sequence is not available, as for models estimating the quality at the user terminal .
The project can include a few months of internship in industry (a major broadcasting or Over The Top (OTT) service provider), either in the UK or abroad, in line with the collaborations in place and being established.
 N. Barman and M. G. Martini, “QoE Modeling for HTTP Adaptive Video Streaming - A Survey and Open Challenges”, IEEE Access, vol. 7, pp. 30831-30859, 2019.
 N. Barman, E. Jammeh, S. A. Ghorashi and M. Martini, “No-reference Video Quality Estimation Based on Machine Learning for Passive Gaming Video Streaming Applications”, IEEE Access, 2019.
 A. Ahmad, L. Atzori, and M. G. Martini, “Qualia: A Multilayer Solution for QoE Passive Monitoring at the User Terminal,” in IEEE International Conference on Communications (ICC), (Paris, France), May 2017.
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Doctor of Engineering (EngD): Evaluation of compact and low-cost sensing for rapid for biomedical and consumer healthcare using processing and machine learning techniques (STMicroelectronics (R&D) Ltd and University of Strathclyde)