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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:

Quality Enhancement of Gaming Content using Generative Adversarial Networks
Zitatschlüssel	avanaki2020a
Autor	Avanaki, Nasim Jamshidi and Zadtootaghaj, Saman and Barman, Nabajeet and Schmidt, Steven and Martini, Maria G. and Möller, Sebastian
Buchtitel	2020 Twelfth International Conference on Quality of Multimedia Experience (QoMEX)
Seiten	1–6
Jahr	2020
ISBN	978-1-7281-5965-2
DOI	10.1109/QoMEX48832.2020.9123074
Ort	Athlone, Ireland
Monat	may
Verlag	IEEE
Serie	QoMEX ’20
Wie herausgegeben	Fullpaper
Zusammenfassung	Recently, streaming of gameplay scenes has gained much attention, as evident with the rise of platforms such as Twitch.tv and Facebook Gaming. These streaming services have to deal with many challenges due to the low quality of source materials caused by client devices, network limitations such as bandwidth and packet loss, as well as low delay requirements. Spatial video artifact such as blockiness and blurriness as a result of as video compression or up-scaling algorithms can significantly impact the Quality of Experience of end-users of passive gaming video streaming applications. In this paper, we investigate solutions to enhance the video quality of compressed gaming content. Recently, several super-resolution enhancement techniques using Generative Adversarial Network (e.g., SRGAN) have been proposed, which are shown to work with high accuracy on non-gaming content. Towards this end, we improved the SRGAN by adding a modified loss function as well as changing the generator network such as layer levels and skip connections to improve the flow of information in the network, which is shown to improve the perceived quality significantly. In addition, we present a performance evaluation of improved SRGAN for the enhancement of frame quality caused by compression and rescaling artifacts for gaming content encoded in multiple resolution-bitrate pairs.