No-Reference Video Quality Estimation Based on Machine Learning for Passive Gaming Video Streaming Applications
Open accessAbstract
Recent years have seen increasing growth and popularity of gaming services, both interactive and passive. While interactive gaming video streaming applications have received much attention, passive gaming video streaming, in-spite of its huge success and growth in recent years, has seen much less interest from the research community. For the continued growth of such services in the future, it is imperative that the end user gaming quality of experience (QoE) is estimated so that it can be controlled and maximized to ensure user acceptance. Previous quality assessment studies have shown not so satisfactory performance of existing No-reference (NR) video quality assessment (VQA) metrics. Also, due to the inherent nature and different requirements of gaming video streaming applications, as well as the fact that gaming videos are perceived differently from non-gaming content (as they are usually computer generated and contain artificial/synthetic content), there is a need for application-specific light-weight, no-reference gaming video quality prediction models. In this paper, we present two NR machine learning-based quality estimation models for gaming video streaming, NR-GVSQI, and NR-GVSQE, using NR features, such as bitrate, resolution, and temporal information. We evaluate their performance on different gaming video datasets and show that the proposed models outperform the current state-of-the-art no-reference metrics, while also reaching a prediction accuracy comparable to the best known full reference metric.
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892 · see citing works
Data Mining: Practical Machine Learning Tools and Techniques
Witten, I. H., Frank, E., & Hall, M. A. (2011) · Elsevier eBooks · doi:10.1016/c2009-0-19715-5 · Cited by 25,493 · see citing works
The WEKA data mining software: an update
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1656274.1656278 · Cited by 17,750 · see citing works
An introduction to variable and feature selection
GuyonIsabelle, & ElisseeffAndré (2003) · Journal of Machine Learning Research · doi:10.5555/944919.944968 · Cited by 7,843 · see citing works
Neural networks: A comprehensive foundation
Tadeusiewicz, R. (1995) · Control Engineering Practice · doi:10.1016/0967-0661(95)90080-2 · Cited by 7,217 · see citing works
Making a “Completely Blind” Image Quality Analyzer
Mittal, A., Soundararajan, R., & Bovik, A. C. (2013) · IEEE Signal Processing Letters · doi:10.1109/lsp.2012.2227726 · Cited by 6,766 · see citing works
An Introduction to Variable and Feature Selection.
Guyon, I., & Elisseeff, A. (2003) · Cited by 6,576 · see citing works
No-Reference Image Quality Assessment in the Spatial Domain
Mittal, A., Moorthy, A. K., & Bovik, A. C. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2214050 · Cited by 6,064 · see citing works
Gaussian Processes in Machine Learning
Rasmussen, C. E. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-28650-9_4 · Cited by 5,727 · see citing works
A survey on feature selection methods
Chandrashekar, G., & Sahin, F. (2014) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2013.11.024 · Cited by 5,189 · see citing works
Scope of validity of PSNR in image/video quality assessment
Huynh-Thu, Q., & Ghanbari, M. (2008) · Electronics Letters · doi:10.1049/el:20080522 · Cited by 2,959 · see citing works
Feature Selection for Knowledge Discovery and Data Mining
Liu, H., & Motoda, H. (1998) · Springer eBooks · doi:10.1007/978-1-4615-5689-3 · Cited by 2,216 · see citing works
Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment
Bosse, S., Maniry, D., Muller, K.-R., Wiegand, T., & Samek, W. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2760518 · Cited by 1,263 · see citing works
A Two-Step Framework for Constructing Blind Image Quality Indices
Moorthy, A. K., & Bovik, A. C. (2010) · IEEE Signal Processing Letters · doi:10.1109/lsp.2010.2043888 · Cited by 1,196 · see citing works
Correlation-based Feature Subset Selection for Machine Learning
Hall, M. A. (1998) · Cited by 959 · see citing works
End-to-End Blind Image Quality Assessment Using Deep Neural Networks
Ma, K., Liu, W., Zhang, K., Duanmu, Z., Wang, Z., & Zuo, W. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2774045 · Cited by 613 · see citing works
Developing a predictive model of quality of experience for internet video
Balachandran, A., Sekar, V., Akella, A., Seshan, S., Stoica, I. G., & Zhang, H. (2013) · ACM SIGCOMM Computer Communication Review · doi:10.1145/2534169.2486025 · Cited by 176 · see citing works
Quality of experience estimation for adaptive HTTP/TCP video streaming using H.264/AVC
Singh, K., Hadjadj‐Aoul, Y., & Rubino, G. (2012) · IEEE Consumer Communications and Networking Conference · doi:10.1109/ccnc.2012.6181070 · Cited by 132 · see citing works
End-to-End Blind Quality Assessment of Compressed Videos Using Deep Neural Networks
Liu, W., Duanmu, Z., & Wang, Z. (2018) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3240508.3240643 · Cited by 126 · see citing works
Predicting quality of experience in multimedia streaming
Menkovski, V., Oredope, A., Liotta, A., & Sánchez, A. C. (2009) · International Conference on Advances in Mobile Computing and Multimedia (MoMM) · doi:10.1145/1821748.1821766 · Cited by 91 · see citing works
The Relationship Among Video Quality, Screen Resolution, and Bit Rate
Cermak, G. W., Pinson, M., & Wolf, S. (2011) · IEEE Transactions on Broadcasting · doi:10.1109/tbc.2011.2121650 · Cited by 86 · see citing works
Quality-of-experience assessment and its application to video services in lte networks
Zheng, K., Zhang, X., Zheng, Q., et al. (2015) · IEEE Wireless Communications · doi:10.1109/mwc.2015.7054721 · Cited by 85 · see citing works
Image quality assessment using a neural network approach
Bouzerdoum, A., Havstad, A., & Beghdadi, A. (2005) · IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) · doi:10.1109/isspit.2004.1433751 · Cited by 74 · see citing works
GamingVideoSET: A Dataset for Gaming Video Streaming Applications
Barman, N., Zadtootaghaj, S., Schmidt, S., Martini, M. G., & Möller, S. (2018) · Annual Workshop on Network and Systems Support for Games (NetGames) · doi:10.1109/netgames.2018.8463362 · Cited by 71 · see citing works
Self-citationDeep Learning for Quality Assessment in Live Video Streaming
Vega, M. T., Mocanu, D. C., Famaey, J., et al. (2017) · IEEE Signal Processing Letters · doi:10.1109/lsp.2017.2691160 · Cited by 67 · see citing works
Low-Complexity Video Quality Assessment Using Temporal Quality Variations
Narwaria, M., Lin, W., & Liu, A. (2012) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2012.2190589 · Cited by 64 · see citing works
An Evaluation of Video Quality Assessment Metrics for Passive Gaming Video Streaming
Barman, N., Schmidt, S., Zadtootaghaj, S., Martini, M. G., & Möller, S. (2018) · doi:10.1145/3210424.3210434 · Cited by 60 · see citing works
Self-citationNR-GVQM: A No Reference Gaming Video Quality Metric
Zadtootaghaj, S., Barman, N., Schmidt, S., Martini, M. G., & Möller, S. (2018) · IEEE International Symposium on Multimedia (ISM) · doi:10.1109/ism.2018.00031 · Cited by 46 · see citing works
Self-citationRecent developments in visual quality monitoring by key performance indicators
Leszczuk, M., Hanusiak, M., Farias, M. C. Q., Wyckens, E., & Heston, G. (2014) · Multimedia Tools and Applications · doi:10.1007/s11042-014-2229-2 · Cited by 43 · see citing works
H.264/MPEG-AVC, H.265/MPEG-HEVC and VP9 codec comparison for live gaming video streaming
Barman, N., & Martini, M. G. (2017) · International Conference on Quality of Multimedia Experience (QoMEX) · doi:10.1109/qomex.2017.7965686 · Cited by 42 · see citing works
Self-citationA Comparative Quality Assessment Study for Gaming and Non-Gaming Videos
Barman, N., Martini, M. G., Zadtootaghaj, S., Möller, S., & Lee, S. (2018) · International Conference on Quality of Multimedia Experience (QoMEX) · doi:10.1109/qomex.2018.8463403 · Cited by 38 · see citing works
Self-citationKhan, A., Sun, L., & Ifeachor, E. (2010) · IET Communications · doi:10.1049/iet-com.2009.0649 · Cited by 34 · see citing works
Predictive no-reference assessment of video quality
Vega, M. T., Mocanu, D. C., Stavrou, S., et al. (2016) · Signal Processing Image Communication · doi:10.1016/j.image.2016.12.001 · Cited by 34 · see citing works
A Classification of Video Games based on Game Characteristics linked to Video Coding Complexity
Zadtootaghaj, S., Schmidt, S., Barman, N., et al. (2018) · Annual Workshop on Network and Systems Support for Games (NetGames) · doi:10.1109/netgames.2018.8463434 · Cited by 34 · see citing works
Self-citationA Novel No-Reference Video Quality Metric for Evaluating Temporal Jerkiness due to Frame Freezing
Xue, Y., Erkin, B., & Wang, Y. (2014) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2014.2368272 · Cited by 33 · see citing works
An artificial-neural-network-based QoE estimation model for Video streaming over wireless networks
Kang, Y., Chen, H., & Xie, L. (2013) · IEEE/CIC International Conference on Communications in China (ICCC) · doi:10.1109/iccchina.2013.6671126 · Cited by 27 · see citing works
A reduced complexity no-reference artificial neural network based video quality predictor
Shahid, M. U., Rossholm, A., & Lövström, B. (2011) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp.2011.6099931 · Cited by 25 · see citing works
Cognitive no-reference video quality assessment for mobile streaming services
Vega, M. T., Giordano, E., Mocanu, D. C., et al. (2015) · International Workshop on Quality of Multimedia Experience (QoMEX) · doi:10.1109/qomex.2015.7148128 · Cited by 18 · see citing works
Aligning subjective tests using a low cost common set
Pitrey, Y., Engelke, U., Barkowsky, M., et al. (2011) · HAL (Le Centre pour la Communication Scientifique Directe) · Cited by 18 · see citing works
Jiang, X., Meng, F., Xu, J., et al. (2008) · International Conference on Computer and Electrical Engineering · doi:10.1109/iccee.2008.158 · Cited by 16 · see citing works
Key indicators for monitoring of audiovisual quality
Leszczuk, M., Hanusiak, M., Blanco, I. D., et al. (2014) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu.2014.6830724 · Cited by 15 · see citing works
CNN-MR for No Reference Video Quality Assessment
Wang, C., Su, L., & Huang, Q. (2017) · International Conference on Information Science and Control Engineering · doi:10.1109/icisce.2017.56 · Cited by 11 · see citing works
Quality assessment for MPEG-2 video streams using a neural network model
Wang, C., Jiang, X., Meng, F., et al. (2011) · IEEE International Conference on Communication Technology (ICCT) · doi:10.1109/icct.2011.6158002 · Cited by 9 · see citing works
Estimating PSNR Without Reference for Real H.264/AVC Sequence Intra Frames
Slanina, M., & Říčný, V. (2008) · International Conference Radioelektronika · doi:10.1109/radioelek.2008.4542692 · Cited by 8 · see citing works
Packet layer model of HEVC wireless video quality assessment
Guo, J., Zheng, K., Hu, G., et al. (2016) · International Conference on Computer Science & Education (ICCSE) · doi:10.1109/iccse.2016.7581667 · Cited by 5 · see citing works
End to end video quality prediction for HEVC video streaming over packet networks
Anegekuh, L., Sun, L., & Ifeachor, E. (2013) · International Conference on Communications, Signal Processing, and their Applications (ICCSPA) · doi:10.1109/iccspa.2013.6487327 · Cited by 3 · see citing works
No-reference Quality of Experience estimation of H264/SVC stream
Chérif, W., Ksentini, A., & Négru, D. (2012) · IEEE Globecom Workshops (GC Wkshps) · doi:10.1109/glocomw.2012.6477778 · Cited by 2 · see citing works
An Artificial Neural Network approach for No-Reference High Definition Video quality assessment
Juayek, M., & Sotelo, R. (2016) · IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB) · doi:10.1109/bmsb.2016.7521900 · Cited by 2 · see citing works
A novel video quality measurement using ANN
Sunala, S. L., & Anurenjan, P. R. (2013) · doi:10.1109/aicera-icmicr.2013.6575925 · Cited by 1 · see citing works
Predict the MOS and the PSNR by the neural network
Khattabi, H. E., Aboutajdine, D., & Tamtaoui, A. (2012) · doi:10.1109/icmcs.2012.6320304 · Cited by 1 · see citing works
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Research works printed but not linked: 2
- B. Girod, ''What's wrong with mean-squared error?'' in Digital Images and Human Vision, 1993, pp. 207-220. [Online]. Available: http://dl.acm.org/citation.cfm?id=197765.197784
- S. Göring, R. R. R. Rao, and A. Raake, ''nofu-A lightweight no-reference pixel based video quality model for gaming content,'' in Proc. 11th Int. Conf. Qual. Multimedia Exper. (QoMEX), Jun. 2019, pp. 1-6. [Online]. Available: https://stg7.github.io/pdfs/2019/nofu_paper.pdf NABAJEET BARMAN received the B.Tech. degree in electronics engineering from the National Institute of Technology, Surat, India, in 2011, with a focus on wireless networks, the M.Sc. degree in information technology with specialization in communication engineering and media technology from the Universität Stuttgart, Germany, during which he worked at Bell Labs, Stuttgart, Germany, in 2014, as part of his intern- ship and master's thesis. He is currently pursuing the Ph.D. degree in the field of quality of experience of gaming video stream- ing applications with the Wireless Multimedia and Networking Research Group (WMN), Kingston University London, where he is currently a Research Associate, involved in QoE-aware video coding strategies as part of MSCA ITN QoE-Net. He is currently a Video Quality Expert Group (VQEG) Board member as a part of Computer Graphics Imagery (CGI) project and is also involved in ITU-T standardization activities. His research interests include wireless networking, multimedia communications, and machine learning.
Cited by
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QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey
Barakabitze, A. A., Barman, N., Ahmad, A., Zadtootaghaj, S., Sun, L., Martini, M. G., & Atzori, L. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2958784 · Cited by 239 · see citing works
Self-citation
- Authors
- Barman, N., Jammeh, E., Ghorashi, S. A., & Martini, M. G.
- Affiliations
- Kingston University London (GB)
- Shahid Beheshti University (IR)
- Venue
- IEEE Access
- Published
- 2019
- Volume
- 7
- Pages
- 74511-74527
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2948397451
- Cited by
- 71
- References
- 51
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 10 · 2023: 12 · 2022: 24 · 2021: 7 · 2020: 7 · 2019: 2
- FWCI
- 4.48
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- Research Repository (Kingston University London) (acceptedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- H2020 Marie Skłodowska-Curie Actions (643072)
- Kingston University
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Quality Assessment
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Analysis and Summarization
- Physical Sciences › Computer Science › Signal Processing › Video Coding and Compression Technologies
- Keywords
- no-reference video quality assessment, quality of experience, synthetic content, temporal features
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