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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 | zadtootaghaj2020a |
|---|---|
| Autor | Zadtootaghaj, Saman and Schmidt, Steven and Sabet, Saeed Shafiee and Möller, Sebastian and Griwodz, Carsten |
| Buchtitel | Proceedings of the 11th ACM Multimedia Systems Conference |
| Seiten | 213–224 |
| Jahr | 2020 |
| ISBN | 9781450368452 |
| DOI | 10.1145/3339825.3391872 |
| Ort | Istanbul, Turkey |
| Adresse | New York, NY, USA |
| Monat | jun |
| Verlag | Association for Computing Machinery |
| Serie | MMSys ’20 |
| Wie herausgegeben | Fullpaper |
| Zusammenfassung | The gaming industry is one of the largest digital markets for decades and is steady developing as evident by new emerging gaming services such as gaming video streaming, online gaming, and cloud gaming. While the market is rapidly growing, the quality of these services depends strongly on network characteristics as well as resource management. With the advancement of encoding technologies such as hardware accelerated engines, fast encoding is possible for delay sensitive applications such as cloud gaming. Therefore, already existing video quality models do not offer a good performance for cloud gaming applications. Thus, in this paper, we provide a gaming video quality dataset that considers hardware accelerated engines for video compression using the H.264 standard. In addition, we investigate the performance of signal-based and parametric video quality models on the new gaming video dataset. Finally, we build two novel parametric-based models, a planning and a monitoring model, for gaming quality estimation. Both models are based on perceptual video quality dimensions and can be used to optimize the resource allocation of gaming video streaming services. |