Abstract
In HTTP adaptive video streaming service, video clarity and fluency are the two most important Influencing Factors (IFs) affecting user’s Quality of Experience (QoE). In this paper, a Video Clarity-Fluency Network (VCFNet) is proposed to establish a QoE evaluation model, which focuses on characterizing the clarity-fluency characteristics of video. Firstly, the Harmonic-ResNeXt101 network is constructed by introducing the Harmonic Network into ResNeXt101 to capture the clarity information of video frames. The output of the Fully Connected (FC) layer of the Harmonic-ResNeXt101 network is fed into the Gated Recurrent Unit (GRU), which is used to perform short-term temporal modeling to capture the fluency information of video chunk. Then, the final output of GRU is extracted as the clarity-fluency features, which are concatenated with the statistical features of other IFs (including video quality level, re-buffering duration, re-buffering frequency, etc.) to form the feature parameter vector of IFs. Finally, a neural network composed of One-Dimensional Convolutional Neural Network (1D CNN) layer and two FC layers is designed to establish the mapping relationship model between the feature parameter vector of IFs and Mean Opinion Score (MOS) to predict user’s QoE. Experimental results on SQoE-III and SQoE-IV datasets demonstrate that the proposed VCFNet can effectively capture the clarity-fluency information of video, and the resulted QoE model can achieve the state-of-the-art performance compared with the existing QoE evaluation models.
Similar content being viewed by others
References
Agarla M, Celona L, Schettini R (2020) No-reference quality assessment of in-capture distorted videos. J Imag 6(8):74. https://doi.org/10.3390/jimaging6080074
Bampis CG, Bovik AC (2017) Learning to predict streaming video qoe: distortions, rebuffering and memory. arXiv:1703.00633
Bentaleb A, Begen AC, Zimmermann R (2016) Sdndash: improving qoe of http adaptive streaming using software defined networking. In: Proceedings of the 24th ACM international conference on Multimedia. https://doi.org/10.1145/2964284.2964332, pp 1296–1305
Chen P, Li L, Ma L, Wu J, Shi G (2020) Rirnet: recurrent-in-recurrent network for video quality assessment. In: Proceedings of the 28th ACM international conference on multimedia. https://doi.org/10.1145/3394171.3413717, p 834842
Chen P, Li L, Wu J, Zhang Y, Lin W (2021) Temporal reasoning guided qoe evaluation for mobile live video broadcasting. IEEE Trans Image Process 30:3279–3292. https://doi.org/10.1109/TIP.2021.3060255
Du L, Zhuo L, Li J, Zhang J, Li X, Zhang H (2020) Video quality of experience metric for dynamic adaptive streaming services using dash standard and deep spatial-temporal representation of video. Appl Sci 10(5):1793. https://doi.org/10.3390/app10051793
Duanmu Z, Liu W, Chen D, Li Z, Wang Z, Wang Y, Gao W (2019) A knowledge-driven quality-of-experience model for adaptive streaming videos. arXiv:1911.07944
Duanmu Z, Liu W, Li Z, Chen D, Wang Z, Wang Y, Gao W (2020) Assessing the quality-of-experience of adaptive bitrate video streaming. arXiv:2008.08804
Duanmu Z, Rehman A, Wang Z (2018) A quality-of-experience database for adaptive video streaming. IEEE Trans Broadcast 64(2):474–487. https://doi.org/10.1109/TBC.2018.2822870
Duanmu Z, Zeng K, Ma K, Rehman A, Wang Z (2016) A quality-of-experience index for streaming video. IEEE J Selected Topics Signal Process 11(1):154–166. https://doi.org/10.1109/JSTSP.2016.2608329
Hoßfeld T, Schatz R, Biersack E, Plissonneau L (2013) Internet video delivery in youtube: from traffic measurements to quality of experience. In: Data traffic monitoring and analysis. Springer, pp 264–301. https://doi.org/10.1007/978-3-642-36784-7_11
Jiang J, Sen S (2020) A new abstraction for internet qoe optimization. arXiv:2008.04128
Li D, Jiang T, Jiang M (2019) Quality assessment of in-the-wild videos. In: Proceedings of the 27th ACM international conference on multimedia. https://doi.org/10.1145/3343031.3351028, pp 2351–2359
Li M, Jianbin S, Hui L (2017) A determining method of frame rate and resolution to boost the video live qoe. In: 2017 2nd international conference on multimedia and image processing (ICMIP). https://doi.org/10.1109/ICMIP.2017.26. IEEE, pp 206–209
Li Z, Aaron A, Katsavounidis I, Moorthy A, Manohara M (2016) Toward a practical perceptual video quality metric. The Netflix Tech Blog 6:2
Liu X, Dobrian F, Milner H, Jiang J, Sekar V, Stoica I, Zhang H (2012) A case for a coordinated internet video control plane. In: Proceedings of the ACM SIGCOMM 2012 conference on applications, technologies, architectures, and protocols for computer communication. https://doi.org/10.1145/2342356.2342431, pp 359–370
Luan S, Chen C, Zhang B, Han J, Liu J (2018) Gabor convolutional networks. IEEE Trans Image Process 27(9):4357–4366. https://doi.org/10.1109/TIP.2018.2835143
Mok RKP, Chan EWW, Chang RKC (2011) Measuring the quality of experience of http video streaming. In: 12th IFIP/IEEE International symposium on integrated network management (IM 2011) and workshops. https://doi.org/10.1109/INM.2011.5990550. IEEE, pp 485–492
Qin Y, Hao S, Pattipati KR, Qian F, Sen S, Wang B, Yue C (2019) Quality-aware strategies for optimizing abr video streaming qoe and reducing data usage. In: Proceedings of the 10th ACM multimedia systems conference. https://doi.org/10.1145/3304109.3306231, pp 189–200
Rassool R (2017) Vmaf reproducibility: validating a perceptual practical video quality metric. In: 2017 IEEE international symposium on broadband multimedia systems and broadcasting (BMSB). https://doi.org/10.1109/BMSB.2017.7986143. IEEE, pp 1–2
Recommendation I (2017) 1203.3, “parametric bitstream-based quality assessment of progressive download and adaptive audiovisual streaming services over reliable transport-quality integration module”. International Telecommunication Union
Sector TS (2016) Series p: terminals and subjective and objective assessment methods, https://videoclarity.com/PDF/T-REC-P.913-201603.pdf. Accessed 30 June 2021
Tran HTT, Nguyen DV, Nguyen DD, Ngoc NP, Thang TC (2019) An lstm-based approach for overall quality prediction in http adaptive streaming. In: IEEE INFOCOM 2019-IEEE conference on computer communications workshops (INFOCOM WKSHPS). https://doi.org/10.1109/INFCOMW.2019.8845041. IEEE, pp 702–707
Ulicny M, Krylov VA, Dahyot R (2018) Harmonic networks: integrating spectral information into cnns. arXiv:1812.03205
Ulicny M, Krylov VA, Dahyot R (2019) Harmonic networks for image classification. In: BMVC, p 202
Ulicny M, Krylov VA, Dahyot R (2019) Harmonic networks with limited training samples. In: 2019 27th European signal processing conference (EUSIPCO). https://doi.org/10.23919/EUSIPCO.2019.8902831. IEEE, pp 1–5
Wang Y, Wang H, Shang J, Tuo H (2019) Resa: a real-time evaluation system for abr. In: 2019 IEEE International conference on multimedia and expo (ICME). https://doi.org/10.1109/ICME.2019.00317. IEEE, pp 1846–1851
Wang Z, Wang J, Wang F, Li C, Fei Z, Rahim T (2017) A video quality assessment method for voip applications based on user experience. Sensing and Imaging 18(1):12. https://doi.org/10.1007/s11220-017-0161-z
Watanabe K, Okamoto J, Kurita T (2007) Objective video quality assessment method for evaluating effects of freeze distortion in arbitrary video scenes. In: Image quality and system performance IV. https://doi.org/10.1117/12.703870, vol 6494. International Society for Optics and Photonics, p 64940P
Xue J, Zhang D-Q, Yu H, Chen CW (2014) Assessing quality of experience for adaptive http video streaming. In: 2014 IEEE International conference on multimedia and expo workshops (ICMEW). https://doi.org/10.1109/ICMEW.2014.6890604. IEEE, pp 1–6
Yan S, Guo Y, Chen Y, Xie F (2018) Predicting freezing of webrtc videos in wifi networks. In: International conference on ad hoc networks. https://doi.org/10.1007/978-3-030-05888-3_27. Springer, pp 292–301
Yin X, Jindal A, Sekar V, Sinopoli B (2015) A control-theoretic approach for dynamic adaptive video streaming over http. In: Proceedings of the 2015 ACM conference on special interest group on data communication. https://doi.org/10.1145/2785956.2787486, pp 325–338
Acknowledgements
This work was supported by the National Natural Science Foundation of China (No. 61531006, 61971016), Beijing Municipal Education Commission Cooperation Beijing Natural Science Foundation (No.KZ201910005007).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Du, L., Li, J., Zhuo, L. et al. VCFNet: video clarity-fluency network for quality of experience evaluation model of HTTP adaptive video streaming services. Multimed Tools Appl 81, 42907–42923 (2022). https://doi.org/10.1007/s11042-022-13483-x
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-022-13483-x