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- Assessing and predicting QoE of Gaming Applications
- 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
Adaptive Edge/Cloud Compute and Network Continuum over a Heterogeneous Sparse Edge Infrastructure to Support Nextgen Applications (ACCORDION) 
Methods and Models for assessing and predicting the QoE linked to Mobile Gaming (QoE-NET/MSCA-ITN Network) 
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.
Chair of Computer-Generated Imagery (CGI) at VQEG
Local coordinator of HCID track of EIT master program
MMSPG lab, EPFL (2017)
LST group, DFKI (2019)
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).
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 
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 .
Quality and Usability Lab
Deutsche Telekom Laboratories
D-10587 Berlin, Germany
Email: firstname.lastname@example.org 
Tel: +49 30 8353 58394