TU Berlin

Quality and Usability LabSteven Schmidt

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Steven Schmidt

Q&U
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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

A Classification of Video Games based on Game Characteristics linked to Video Coding Complexity
Citation key zadtootaghaj2018a
Author Zadtootaghaj, Saman and Schmidt, Steven and Barman, Nabajeet and Möller, Sebastian and Martini, Maria G.
Title of Book 16th Annual Workshop on Network and Systems Support for Games (NetGames)
Pages 1–6
Year 2018
ISSN 2156-8146
DOI 10.1109/NetGames.2018.8463434
Address Piscataway, NJ
Month jun
Note electronic
Publisher IEEE
How Published full
Abstract Applications used for video streaming of gaming content have seen tremendous growth over the recent years as evident with the increasing popularity of services such as Twitch.tv and YouTubeGaming. Gaming video streaming encoding needs to be performed in real-time and thus has a strict set of encoding constraints. Therefore, many traditional encoding optimization methods such as multiple-pass encoding are not suitable for live gaming video streaming applications. The video quality of streaming services is highly content dependent. While this holds true also for conventional contents, there exist many characteristics of games that do not vary much over time. Therefore, such game-specific information can be exploited to optimize the encoding process. In this paper, we present a classification of games using characteristics such as the type of camera movement, texture details, and static areas of a scene. Using a database of gaming videos from different genres and complexity, we obtain clusters corresponding to the calculated quality values (VMAF). The derived gaming characteristics are then mapped to the quality classes to obtain a decision tree based game classification. We illustrate how the classification can be used for encoding bitrate selection and quality prediction.
Link to publication Link to original publication Download Bibtex entry

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