Inhalt des Dokuments
Saman Zadtootaghaj
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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:
| Zitatschlüssel | barakabitze2019a |
|---|---|
| Autor | Barakabitze, Alcardo Alex and Barman, Nabajeet and Ahmad, Arslan and Zadtootaghaj, Saman and Sun, Lingfen and Martini, Maria G and Atzori, Luigi |
| Seiten | 1–1 |
| Jahr | 2019 |
| ISSN | 2373-745X |
| DOI | 10.1109/COMST.2019.2958784 |
| Journal | IEEE Communications Surveys Tutorials |
| Monat | dec |
| Notiz | electronic |
| Verlag | IEEE |
| Wie herausgegeben | Fullpaper, Poster |
| Zusammenfassung | The highly demanding Over-The-Top (OTT) multimedia applications pose increased challenges to Internet Service Providers (ISPs) for assuring a reasonable Quality of Experience (QoE) to their customers due to lack of flexibility, agility and scalability in traditional networks. The future networks are shifting towards the cloudification of the network resources via Software Defined Networks (SDN) and Network Function Virtualization (NFV). This will equip ISPs with cutting-edge technologies to provide service customization during service delivery and offer QoE which meets customers’ needs via intelligent QoE control and management approaches. Towards this end, we provide in this paper a tutorial and a comprehensive survey of QoE management solutions in current and future networks. We start with a high-level description of QoE management for multimedia services, which integrates QoE modelling, monitoring, and optimization. This followed by a discussion of HTTP Adaptive Streaming (HAS) solutions as the dominant technique for streaming videos over the best-effort Internet. We then summarize the key elements in SDN/NFV along with an overview of ongoing research projects, standardization activities and use cases related to SDN, NFV, and other emerging applications. We provide a survey of the state-of-the-art of QoE management techniques categorized into three different groups: a) QoE-aware/driven strategies using SDN and/or NFV; b) QoE-aware/driven approaches for adaptive streaming over emerging architectures such as multi-access edge computing, cloud/fog computing, and information-centric networking; and c) extended QoE management approaches in new domains such as immersive augmented and virtual reality, mulsemedia and video gaming applications. Based on the review, we present a list of identified future QoE management challenges regarding emerging multimedia applications, network management and orchestration, network slicing and collaborative service management in softwarized networks. Finally, we provide a discussion on future research directions with a focus on emerging research areas in QoE management, such as QoE-oriented business models, QoE-based big data strategies, and scalability issues in QoE optimization. |
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