direkt zum Inhalt springen

direkt zum Hauptnavigationsmenü

Sie sind hier

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

Delay Sensitivity Classification of Cloud Gaming Content
Zitatschlüssel sabet2020b
Autor Sabet, Saeed Shafiee and Schmidt, Steven and Zadtootaghaj, Saman and Griwodz, Carsten and Möller, Sebastian
Buchtitel Proceedings of the 12th ACM International Workshop on Immersive Mixed and Virtual Environment Systems
Seiten 25–30
Jahr 2020
ISBN 9781450379472
DOI 10.1145/3386293.3397116
Ort Istanbul, Turkey
Adresse New York, NY, USA
Monat jun
Verlag Association for Computing Machinery
Serie MMVE ’20
Wie herausgegeben Fullpaper
Zusammenfassung Cloud Gaming is an emerging service that catches growing interest in the research community as well as industry. Cloud Gaming require a highly reliable and low latency network to achieve a satisfying Quality of Experience (QoE) for its users. Using a cloud gaming service with high latency would harm the interaction of the user with the game, leading to a decrease in playing performance and, thus players frustrations. However, the negative effect of delay on gaming QoE depends strongly on the game content. At a certain level of delay, a slow-paced card game is typically not as delay sensitive as a shooting game. For optimal resource allocation and quality estimation, it is highly important for cloud providers, game developers, and network planners to consider the impact of the game content. This paper contributes to a better understanding of the delay impact on QoE for cloud gaming applications by identifying game characteristics influencing the delay perception of the users. In addition, an expert evaluation methodology to quantify these characteristics as well as a delay sensitivity classification based on a decision tree are presented. The results indicated an excellent level of agreement, which demonstrates the reliability of the proposed method. Additionally, the decision tree reached an accuracy of 90% on determining the delay sensitivity classes which were derived from a large dataset of subjective input quality ratings during a series of experiments.
Link zur Publikation Link zur Originalpublikation Download Bibtex Eintrag

Zusatzinformationen / Extras

Direktzugang

Schnellnavigation zur Seite über Nummerneingabe