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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
| Citation key | barman2018d |
|---|---|
| Author | Barman, Nabajeet and Zadtootaghaj, Saman and Schmidt, Steven and Martini, Maria G. and Möller, Sebastian |
| Pages | e2054 |
| Year | 2018 |
| DOI | 10.1002/nem.2054 |
| Address | Piscataway, NJ |
| Journal | International Journal of Network Management |
| Month | may |
| Note | electronic |
| Publisher | Hoboken, New Jersey |
| How Published | full |
| Abstract | Summary Passive gaming video-streaming applications have recently gained much attention as evident with the rising popularity of many Over The Top (OTT) providers such as Twitch.tv and YouTube Gaming. For the continued success of such services, it is imperative that the user Quality of Experience (QoE) remains high, which is usually assessed using subjective and objective video quality assessment methods. Recent years have seen 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. A study on the performance of objective VQA on gaming videos, which are artificial and synthetic and have different streaming requirements than traditionally streamed videos, is still missing. Towards this end, we present in this paper an objective and subjective quality assessment study on gaming videos considering passive streaming applications. Subjective ratings are obtained for 90 stimuli generated by encoding six different video games in multiple resolution-bitrate pairs. Objective quality performance evaluation considering eight widely used VQA metrics is performed using the subjective test results and on a data set of 24 reference videos and 576 compressed sequences obtained by encoding them in 24 resolution-bitrate pairs. Our results indicate that Video Multimethod Assessment Fusion (VMAF) predicts subjective video quality ratings the best, while Naturalness Image Quality Evaluator (NIQE) turns out to be a promising alternative as a no-reference metric in some scenarios. |