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Steven Schmidt
- © Q&U
Research Field
- Quality of Experience (QoE) for Cloud Gaming Services
- Engagement in Virtual Reality
Research Topics
- Identification and quantification of perceptual quality dimensions for gaming QoE
- Prediction of gaming QoE based on encoding and network parameters
- Classification of game content
- Crowdsourcing for gaming evaluation
Biography
Steven Schmidt received his M.Sc. degree in Electrical Engineering at the TU Berlin with a major in Communication Systems. Since 2016 he is employed as a research assistant at the Quality and Usability Lab where he is working towards a PhD in the field of Quality of Experience in Mobile Gaming.
Projects
ITU-T SG12 Activities:
- ITU-T Rec. G.1032 - Influence Factors on Gaming Quality of Experience (2017)
- ITU-T Rec. P.809 - Subjective Evaluation Methods for Gaming Quality (2018)
- ITU-T Rec. G.1072 - Opinion Model Predicting Gaming QoE for Cloud Gaming Services (2020)
Address
Quality and Usability Lab
Technische Universität Berlin
Ernst-Reuter-Platz 7
D-10587 Berlin, Germany
Tel: +49 151 12044969
Publications
| Zitatschlüssel | barman2018c |
|---|---|
| Autor | Barman, Nabajeet and Schmidt, Steven and Zadtootaghaj, Saman and Martini, Maria G. and Möller, Sebastian |
| Buchtitel | Proceedings of the 23rd Packet Video Workshop |
| Seiten | 1–6 |
| Jahr | 2018 |
| ISBN | 978-1-4503-5773-9 |
| DOI | 10.1145/3210424.3210434 |
| Ort | Amsterdam, Netherlands |
| Adresse | New York, NY, USA |
| Monat | jun |
| Notiz | electronic |
| Verlag | ACM |
| Serie | PV '18 |
| Wie herausgegeben | full |
| Zusammenfassung | Video Quality assessment is imperative to estimate and hence manage the Quality of Experience (QoE) in video streaming applications to the end-user. Recent years have seen a tremendous advancement in the field of objective video quality assessment (VQA) metrics, with the development of models that can predict the quality of the videos streamed over the Internet. However, no work so far has attempted to study the performance of such quality assessment metrics on gaming videos, which are artificial and synthetic and have different streaming requirements than traditionally streamed videos. Towards this end, we present in this paper a study of the performance of objective quality assessment metrics for gaming videos considering passive streaming applications. Objective quality assessment considering eight widely used VQA metrics is performed on a dataset of 24 reference videos and 576 compressed sequences obtained by encoding them at 24 different resolution-bitrate pairs. We present an evaluation of the performance behavior of the VQA metrics. Our results indicate that VMAF predicts subjective video quality ratings the best, while NIQE turns out to be a promising alternative as a no-reference metric in some scenarios. |