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TU Berlin

Inhalt des Dokuments

Saman Zadtootaghaj

Lupe

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:  
Tel:  +49 30 8353 58394

 

 

Publications:

A Comparative Quality Assessment Study for Gaming and Non-Gaming Videos
Zitatschlüssel barman2018a
Autor Barman, Nabajeet and Martini, Maria G. and Zadtootaghaj, Saman and Möller, Sebastian and Lee, Sanghoon
Buchtitel 2018 Tenth International Conference on Quality of Multimedia Experience (QoMEX)
Seiten 1–6
Jahr 2018
ISSN 2472-7814
DOI 10.1109/QoMEX.2018.8463403
Adresse Piscataway, NJ
Monat may
Notiz electronic
Verlag IEEE
Wie herausgegeben full
Zusammenfassung Recent years have seen a tremendous increase in video traffic with the rise of Over The Top (OTT) services. Along with traditional Video on demand (VoD) streaming services (e.g., Netflix, YouTube), live video services (e.g., Twitch. tv, YouTubeGaming, Facebook Live) have also resulted in a tremendous share of Internet traffic. Among the live streaming services, gaming video streaming has a major share, with Twitch.tv alone currently responsible for the fourth highest peak Internet traffic in the US. As a consequence of this, and due to the fact that gaming videos are artificial and synthetic, it is worth investigating the specificity of gaming videos in relation to compression and the consequent end user QoE. In this paper, we present an objective and subjective quality comparison study for regular videos and gaming videos, with 30 video sequences (15 per type), encoded using the state of the art encoder HEVC. We discuss the similarity and dissimilarity between the two video types and also discuss how these observations can be used to improve the end user QoE.
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