Haupt-Navigation

Inhalt des Dokuments

Saman Zadtootaghaj

: © Copyright??

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:

A Latency Compensation Technique Based on Game Characteristics to Mitigate the Influence of Delay on Cloud Gaming Quality of Experience
Zitatschlüssel	sabet2020c
Autor	Sabet, Saeed Shafiee and Schmidt, Steven and Zadtootaghaj, Saman and Naderi, Babak and Griwodz, Carsten and Möller, Sebastian
Buchtitel	Proceedings of the 11th ACM Multimedia Systems Conference
Seiten	15–25
Jahr	2020
ISBN	9781450368452
DOI	10.1145/3339825.3391855
Ort	Istanbul, Turkey
Adresse	New York, NY, USA
Monat	jun
Verlag	Association for Computing Machinery
Serie	MMSys ’20
Wie herausgegeben	Fullpaper
Zusammenfassung	Cloud Gaming (CG) is an immersive multimedia service that promises many benefits. In CG, the games are rendered in a cloud server, and the resulted scenes are streamed as a video sequence to the client. Using CG users are not forced to update their gaming hardware frequently, and available games can be played on any operating system or suitable device. However, cloud gaming requires a reliable and low-latency network, which makes it a very challenging service. Transmission latency strongly affects the playability of a cloud game and consequently reduces the users' Quality of Experience (QoE). In this paper, we propose a latency compensation technique using game adaptation that mitigates the influence of delay on QoE. This technique uses five game characteristics for the adaptation. These characteristics, in addition to an Aim-assistance technique, were implemented in four games for evaluation. A subjective study using 194 participants was conducted using a crowdsourcing approach. The results showed that the majority of the proposed adaptation techniques lead to significant improvements in the cloud gaming QoE.