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Saman Zadtootaghaj
- © Copyright??
Research Field
- Assessing and predicting QoE of Gaming Applications
Research Topics
- Video quality assessment of computer generated content
- Cloud Gaming Quality of Experience
- Deep learning-based quality assessment of image/video content
- Image/video quality enhancement
Current Project:
Past Project:
Biography
Saman Zadtootaghaj is a researcher at the Quality and Usability Lab at Technische Universitat Berlin working on modeling the gaming quality of experience under the supervision of Prof. Dr.-Ing. Sebastian Moller. His main interest is subjective and objective quality assessment of Computer-Generated content. He received his bachelor degree from IASBS and master degree in information technology from University of Tehran.
He worked as a researcher at Telekom Innovation Laboratories of Deutsche Telekom AG from 2016 to 2018 as part of European project called QoE-Net. He is currently the chair of Computer-Generated Imagery group at Video Quality Expert Group.
Roles:
Chair of Computer-Generated Imagery (CGI) at VQEG
Local coordinator of HCID track of EIT master program
Visiting Researcher:
MMSPG lab, EPFL (2017)
LST group, DFKI (2019)
Teaching experience:
Advance Projects at Quality and Usability Lab (Deep Learning for Video Quality Assessment and Enhancement) SS2020
Usability engineering exercise SS2017/SS2018/SS2019/SS2020
Quality and Usability Seminar (Applied statistic) WS 2019-2020
Quality and Usability Seminar (Gamification) SS2018
Teacher assistant: Multiagent (University of Tehran 2014), computer networks (IASBS 2011).
Talks:
VQEG meetings at Nokia, Madrid, March 2018
VQEG meetings at Google (remote), USA, November 2018
VQEG Meetings at Deutsche Telekom, Germany, March 2019
VQEG meetings at Tencent, China, October 2019
VQEG meeting, Online Meeting, March 2020
Involvement in Standardization Activities:
Active in the following work items:
ITU-T P.BBQCG: Parametric bitstream-based Quality Assessment of Cloud Gaming Services
ITU-T G.CMVTQS: Computational model used as a QoE/QoS monitor to assess videotelephony services
ITU-T G.OMMOG: Opinion Model for Mobile Online Gaming applications
Contributed to the following recommendations:
ITU-T G.1032: Influence factors on gaming quality of experience
ITU-T P.809: Subjective evaluation methods for gaming quality
ITU-T G.1072: Opinion model predicting gaming quality of experience for cloud gaming services
Reviewed papers for TCSVT, Quality and User Experience journal, Journal of Electronic Imaging, QoMEX 2017-2019, ICC 2019 and ICME 2020, PQS workshop 2016
Tools for Quality Prediction of Gaming Content:
NDNetGaming: Deep Learning based Quality metric for Gaming Content
GamingPara: Gaming Parametric based Video Quality Models
Implementation of ITU-T Recommendation G.1072
Datasets:
GamingVideoSet: https://kingston.box.com/v/GamingVideoSET
Cloud Gaming Video Dataset: https://github.com/stootaghaj/CGVDS
Image Gaming Quality Dataset: https://github.com/stootaghaj/GISET
Find me on ResearchGate, LinkedIn, Scholar, GitHub.
Address
Quality and Usability Lab
Deutsche Telekom Laboratories
TU Berlin
Ernst-Reuter-Platz 7
D-10587 Berlin, Germany
Email: saman.zadtootaghaj@qu.tu-berlin.de
Tel: +49 30 8353 58394
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. |