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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:

Adaptive Edge/Cloud Compute and Network Continuum over a Heterogeneous Sparse Edge Infrastructure to Support Nextgen Applications (ACCORDION)

Past Project:

Methods and Models for assessing and predicting the QoE linked to Mobile Gaming (QoE-NET/MSCA-ITN Network)

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:

Know your Game: A bottom-up Approach for Gaming Research
Zitatschlüssel	mowlaei2018a
Autor	Mowlaei, Sajad and Schmidt, Steven and Zadtootaghaj, Saman and Möller, Sebastian
Buchtitel	2018 Tenth International Conference on Quality of Multimedia Experience (QoMEX)
Seiten	1–3
Jahr	2018
ISSN	2472-7814
DOI	10.1109/QoMEX.2018.8463423
Adresse	Piscataway, NJ
Monat	may
Notiz	electronic
Verlag	IEEE
Wie herausgegeben	full
Zusammenfassung	Recent advancements of network architecture such as 5G networks, promise cloud services with strict network constrains a bright future. Cloud gaming as an interactive service has strict end-to-end delay constraints. Therefore, many studies investigated the impact of network parameters such as delay or packet loss on gaming QoE. However, they mostly compared games or genres with each other and neglected the fact even two levels of the same game may have different sensitivity toward delay. In order to understand the game characteristics that cause this difference in delay sensitivity, a bottom-up approach by means of modifiable open source games can be of high value. In this paper we present a game designed to tackle this issue. The game allows to artificially change characteristics of the game, such as the pace and size of objects, and also simulate influences like delay, packet loss or a reduced frame rate. This allows the usage of the game also for crowdsourcing studies, where it is not possible to control the different network conditions of the participants, and to investigate the impact of spatial and temporal accuracy in respect to the sensitivity towards impairments.