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Saman Zadtootaghaj
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:
Past Project:
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:
Citation key | barman2018b |
---|---|
Author | Barman, Nabajeet and Schmidt, Steven and Zadtootaghaj, Saman and Martini, Maria G. and Möller, Sebastian |
Title of Book | 16th Annual Workshop on Network and Systems Support for Games (NetGames) |
Pages | 1–6 |
Year | 2018 |
ISSN | 2156-8146 |
DOI | 10.1109/NetGames.2018.8463362 |
Address | Piscataway, NJ |
Month | jun |
Note | electronic |
Publisher | IEEE |
How Published | full |
Abstract | This paper presents GamingVideoSET1, a dataset consisting of twenty-four uncompressed raw gaming videos of 30 seconds duration, 1080p resolution, and 30 fps for the research community working on gaming video quality assessment. Furthermore, the data set includes subjective quality assessment results for 90 video sequences obtained by encoding six different gaming videos using the H.264/MPEG-AVC codec standard in 15 different resolution-bitrate pairs (three resolution, five bitrates each). In addition to the reference videos, the dataset offers a total of 576 distorted videos in MP4 format, obtained by encoding the videos in 24 different resolution-bitrate pairs, and their objective quality assessment results (average and per-frame) using three video quality assessment metrics.1The database is available at https://kingston.box.com/v/GamingVideoSET |