Quality of experience models for network video quality

doi:DOI: 10.19682/j.cnki.1005-8885.2019.1020

中国邮电高校学报(英文) ›› 2019, Vol. 26 ›› Issue (4): 80-88.doi: DOI: 10.19682/j.cnki.1005-8885.2019.1020

• Others • 上一篇

Quality of experience models for network video quality

Shi Zhiming, Huang Chengti

College of Engineering, Huaqiao University, Quanzhou 362021, China

Fujian Provincial Academic Engineering Research Centre in Industrial Intellectual Techniques and Systems, Huaqiao University, Quanzhou 362021, China

收稿日期:2018-10-18 修回日期:2019-07-25 出版日期:2019-08-31 发布日期:2019-10-29
通讯作者: Corresponding author: Shi Zhiming, E-mail: szmi_2007@126.com E-mail:szmi_2007@126.com
作者简介:Corresponding author: Shi Zhiming, E-mail: szmi_2007@126.com
基金资助:

Quality of experience models for network video quality

Shi Zhiming, Huang Chengti

College of Engineering, Huaqiao University, Quanzhou 362021, China

Fujian Provincial Academic Engineering Research Centre in Industrial Intellectual Techniques and Systems, Huaqiao University, Quanzhou 362021, China

Received:2018-10-18 Revised:2019-07-25 Online:2019-08-31 Published:2019-10-29
Contact: Corresponding author: Shi Zhiming, E-mail: szmi_2007@126.com E-mail:szmi_2007@126.com
About author:Corresponding author: Shi Zhiming, E-mail: szmi_2007@126.com
Supported by:

摘要/Abstract

摘要： Nowadays, the service of network video is increasing explosively. But the quality of experience (QoE) model of network video quality is not stable. The video quality may be impaired by many factors. This paper proposes QoE models for network video quality. It consists of two components: 1) the perceptual video quality model considering the impair factors related to video content as well as distortion caused by content and transmission. Next the model is built through a decision tree using a set of measured features form the network video. This proposed model can qualitatively give the grade of video quality and improve the accuracy of prediction. 2) Based on the above model, another model is proposed to give the concrete objective score of video quality. It also considers original impair factors and predicts the video quality using fuzzy decision tree. The two models have their own advantages. The first model has a good computational complexity; the second model is more precise. All the models are simulated by actual experiments. They can improve the accuracy of objective model. The detail results are shown.

关键词: perceptual video quality, QoE, objective model, fuzzy decision tree, accuracy

Abstract: Nowadays, the service of network video is increasing explosively. But the quality of experience (QoE) model of network video quality is not stable. The video quality may be impaired by many factors. This paper proposes QoE models for network video quality. It consists of two components: 1) the perceptual video quality model considering the impair factors related to video content as well as distortion caused by content and transmission. Next the model is built through a decision tree using a set of measured features form the network video. This proposed model can qualitatively give the grade of video quality and improve the accuracy of prediction. 2) Based on the above model, another model is proposed to give the concrete objective score of video quality. It also considers original impair factors and predicts the video quality using fuzzy decision tree. The two models have their own advantages. The first model has a good computational complexity; the second model is more precise. All the models are simulated by actual experiments. They can improve the accuracy of objective model. The detail results are shown.

Key words: perceptual video quality, QoE, objective model, fuzzy decision tree, accuracy

中图分类号:

TN919.85

Shi Zhiming, Huang Chengti. Quality of experience models for network video quality[J]. The Journal of China Universities of Posts and Telecommunications, 2019, 26(4): 80-88.

参考文献

References

1. Zhang W, Liu H T. Study of saliency in objective video quality assessment. IEEE Transactions on Image Processing, 2017, 26(3): 1275 -1288

2. Manasa K, Sumohana S C. An optical flow-based full reference video quality assessment algorithm. IEEE Transactions on Image Processing, 2016, 25(6): 2480 -2492

3. Li X L, Guo Q, et al. Spatiotemporal statistics for video quality assessment. IEEE Transactions on Image Processing, 2016, 25(7): 3329 -3342

4. Zhao T S, Liu Q, Chen C W. QoE in video transmission: a user experience-driven strategy. IEEE Communication Surveys and Tutorials, 2016, 19(1): 285 -302

5. Jacob S, Soren F, Jari K. No reference video quality assessment using codec analysis. IEEE Transactions on Circuits and Systems for Video Technology, 2015, 25(10): 1637 -1650

6. Hu S D, Jin L N, et al. Objective video quality assessment based on perceptually weighted mean squared error. IEEE Transactions on Circuits and Systems for Video Technology, 2017, 27 (9): 1844 -1855

7. Ali A N, Sang-Heon L, Javaan C. Quality index evaluation of videos based on fuzzy interface system. IET Image Processing, 2017, 11(5): 292 -300

8. Felix M M, Ke W, Zhang F, Roland B, David R B. On the optimal presentation duration for subjective video quality assessment. IEEE Transactions on Circuits and Systems for Video Technology, 2015, 26(11): 1977 -1987

9. Ruairi D F. Source separation approach to video quality prediction in computer networks. IEEE Communications Letters, 2016, 20(7): 1333 -1336

10. Chen Y J, Wu K S, Zhang Q. From QoS to QoE: a tutorial on video quality assessment. IEEE Communications Surveys and Tutorials, 2015, 17(2): 1126 -1165

11. Jenni R, Mikko N, et al. Did you notice it? -How can we predict the subjective detection of video quality changes from eye movements. IEEE Journal of Selected Topics in Signal Processing, 2017, 11(1): 37 -47

12. Qian L, Cheng Z X, et al. A QoE-driven encoder adaptation scheme for multi-user video streaming in wireless networks. IEEE Transactions on Broadcast, 2017, 63(1): 20 -31

13. Deepti G, Janice P, Alan C B. Learning a continuous-time streaming video QoE model. IEEE Transactions on Image Processing, 2018, 27(5): 2257 -2271

14. James N, Pablo S G, Jose M A C, et al. 5G-QoE: QoE modelling for Ultra-HD video streaming in 5G networks. IEEE Transactions on Broadcasting, 2018, 64(2): 621 -634

15. Christos G B, Li Z, Ioannis K, et al. Recurrent and dynamic models for predicting streaming video quality of experience. IEEE Transactions on Image Processing, 2018, 27(7): 3316 -3331

16. Mirghiasaldin S, Colin B, Peng X H. Model and performance of a no-reference quality assessment metric for video streaming. IEEE Transactions on Circuits and Systems for Video Technology, 2013, 23(12): 2034 -2043

17. Zhang F, David R B. A perception-based hybrid model for video quality assessment. IEEE Transactions on Circuits and Systems for Video Technology, 2016, 26(6): 1017 -1028

18. Maria T V, Decebal C M, Jeroen F, et al. Deep learning for quality assessment in live video streaming. IEEE Signal Processing Letters, 2017, 24(6): 736 -740

19. Zhu K F, Li C Q, Asari V, Saupe D. No-reference video quality assessment based on artifact measurement and statistical analysis. IEEE Transactions on Circuits and System for Video Technology, 2015, 25(4): 533 -546

20. Dem佼stenes Z R, Renata L R, Eduardo A C, Julia A, Graca B. Video quality assessment in video streaming services considering user preference for video content. IEEE Transactions on Consumer Electronics, 2014, 60(3): 436 -444

21. Ortiz-Jaramillo B, Nino-Castaneda J, Platisa L, Philips W. Content-aware objective video quality assessment. Journal of Electronic Imaging, 2016, 25(1): 013011 1 -16

22. Ricky K P M, Edmond W W C, Rocky K C C. Measuring the quality of experience of HTTP video streaming. 2011 12th IFIP/ IEEE International Symposium on Integrated Network Management and Workshops, May 23 -27, 2011, Dublin, Ireland, 2011: 485 -492

23. Taichi K, Kazuhisa Y, Keishiro W, et al. No reference video quality assessment model for video streaming services. 2010 18th International Packet Video Workshop, Dec 13 -14, 2010, HongKong, China, 2010: 158 -164

24. Zheng X, Yang B, Liu Y W, et al. Blockiness evaluation for reducing blocking artifacts in compressed images. 2009 Digest of Technical Papers International Conference on Consumer Electronics, Jan 10 -14, 2009, Las Vegas, NV, USA, 2009: 1 -2

25. Edson F D O, Maria E D L T, et al. Voltage THD analysis using knowledge discovery in databases with a decision tree classifier. IEEE Access, 2018(6): 1177 -1188

26. Mohammed A, Emad D, et al. Prediction of perceptual quality for mobile video using fuzzy inference systems. IEEE Transactions on Consumer Electronics, 2015, 61(4): 546 -554

27. Abdul H, Dai R, Benjamin B. A decision-tree-based perceptual video quality prediction model and its application in FEC for wireless multimedia communications, 2016, 18(4): 764 -774

Quality of experience models for network video quality

Quality of experience models for network video quality

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