Content-Based Video Quality Prediction for MPEG4 Video Streaming over Wireless Networks
Abstract
Abstract — There are many parameters that affect video quality but their combined effect is not well identified and understood when video is transmitted over mobile / wireless networks. In addition, video content has an impact on video quality under same network conditions. The main aim of this paper is the prediction of video quality combining the application and network level parameters for all content types. Firstly, video sequences are classified into groups representing different content types using cluster analysis. The classification of contents is based on the temporal (movement) and spatial (edges, brightness) feature extraction. Second, to study and analyze the behaviour of video quality for wide range variations of a set of selected parameters. Finally, to develop two learning models based on – (1) ANFIS to estimate the visual perceptual quality in terms of the Mean Opinion Score (MOS) and decodable frame rate (Q value) and (2) regression modeling to estimate the visual perceptual quality in terms of the MOS. We trained three ANFIS-based ANNs and regression based-models for the three distinct content types using a combination of network and application level parameters and tested the two models using unseen dataset. We confirmed that the video quality is more sensitive to network level compared to application level parameters. Preliminary results show that a good prediction accuracy was obtained from both models. However, the regression based model performed better in terms of the correlation coefficient and the root mean squared error. The work should help in the development of a reference-free video prediction model and Quality of Service (QoS) control methods for video over wireless/mobile networks. Index Terms — ANFIS, neural networks, Content clustering,
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Some works find it sufficient to correlate QoE to a single network QoS parameter, while others argue that multiple QoS parameters impact QoE and need to be considered in tandem.
“While some find it sufficient to correlate the QoE to a single network QoS parameter [145, 240, 383, 408], e.g.delay or throughput, others argue that multiple QoS parameters impact the QoE and need to be considered in tandem as features in a QoE/QoS correlation model [89,102,114,235,287,328,432].”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [235]
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Boutaba, R., Salahuddin, M. A., Limam, N., Ayoubi, S., Shahriar, N., Estrada-Solano, F., & Caicedo, O. M. (2018) · Journal of Internet Services and Applications · doi:10.1186/s13174-018-0087-2 · Cited by 1,036 · see citing works
- Authors
- Khan, A., Sun, L., & Ifeachor, E.
- Venue
- Journal of Multimedia
- Publisher
- Academy Publisher
- Published
- 2009-08-01
- Volume
- 4
- Issue
- 4
- ISSN
- 1796-2048 (ISSN-L)
- OpenAlex
- W2152152762
- Cited by
- 59
- References
- 20
- Citations by year
- 2021: 1 · 2020: 3 · 2019: 1 · 2018: 5 · 2017: 2 · 2016: 6 · 2015: 7 · 2014: 5 · 2013: 12 · 2012: 4
- FWCI
- 6.25
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Multimedia (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Quality Assessment
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Traffic and Congestion Control
- Physical Sciences › Computer Science › Signal Processing › Video Coding and Compression Technologies
- Keywords
- video quality prediction, wireless video transmission, video classification, content clustering, visual quality assessment, mean opinion score, adaptive neuro-fuzzy inference system, regression models, network parameters, quality of service, cellular networks
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