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TU Berlin

Inhalt des Dokuments

Steven Schmidt

Q&U
Lupe

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

Quality Estimation Models for Gaming Video Streaming Services Using Perceptual Video Quality Dimensions
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.
Link zur Publikation Link zur Originalpublikation Download Bibtex Eintrag

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