Skip to main content

Advertisement

SpringerLink
Log in

Perceptual quality assessment of video using machine learning algorithm

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

User experience has become the most reliable and trustworthy source for service providers to assess system performance. To fulfill customer requirements, service providers require an efficient quality of experience (QoE) estimation model. QoE is a subjective metric that deals with user perception and can vary dramatically due to various factors such as emotions, the degree of annoyance, past experience, and aesthetics. Moreover, subjective QoE evaluation is expensive and time-consuming because of human participation. Therefore, a model is required to objectively measure QoE with reasonable accuracy. In the context of service and network providers fulfilling user requirements, with a reasonable quality of service (QoS), needs to be provided to the relevant services/applications. However, QoS parameters do not reflect subjective opinion of the user accurately. Therefore, it is necessary to compute the mapping or correlation between QoS and QoE. The mapping may help service providers to understand the behavior of the overall network on user experience and efficiently manage the network resources. In this paper, a new feature number of displayed frames impacted (NoDFI) along with a machine learning based model is presented to compute a correlation between QoS and QoE. A publically available dataset is used to represent the correlation between objective QoS parameters and subjective QoE metric. The result of experiments showed that proposed feature NoDFI proved to be a valuable addition when compared with previously proposed models.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Brunnström, K., Beker, S.A., de Moor, K., Dooms, A., Egger, S., Garcia, M.N., Hossfeld, T., Jumisko-Pyykkö, S., Keimel, C., Larabi, M.C., Lawlor, B.: Qualinet white paper on definitions of quality of experience. In: Proceedings of European Network on Quality of Experience in Multimedia Systems and Services COSTActionlC1003 (2013)

  2. Hiwasaki, Y.: SERIESG: transmission system and media, digital system and networks. IEEE Commun. Mag. 47(10), 110–116 (2009)

    Article  Google Scholar 

  3. Joskowicz, J., Sotelo, R., Arado, J.C.L.: Comparison of parametric models for video quality estimation: towards a general model. In: IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, pp. 1–7 (2012)

  4. Yang, F., Wan, S.: Bitstream-based quality assessment for networked video: a review. IEEE Commun. Mag. 50(11), 203–209 (2012)

    Article  Google Scholar 

  5. Anegekuh, L., Sun, L., Jammeh, E., Mkwawa, I.-H., Ifeachor, E.: Content-based video quality prediction for HEVC encoded videos streamed over packet networks. IEEE Trans. Multimed. 17(8), 1323–1334 (2015)

    Article  Google Scholar 

  6. Ting-Lan Lin, T.L., Kanumuri, S., Yuan Zhi, Y., Poole, D., Cosman, P.C., Reibman, A.R.: A versatile model for packet loss visibility and its application to packet prioritization. IEEE Trans. Image Process. 19(3), 722–735 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  7. Chikkerur, S., Chikkerur, S., Sundaram, V., Reisslein, M., Karam, L.J.: Objective video quality assessment methods: a classification, review, and performance comparison. IEEE Trans. Broadcast. 57(2), 165–182 (2011)

    Article  Google Scholar 

  8. Zhu, K., Hirakawa, K., Asari, V., Saupe, D.: A no-reference video quality assessment based on laplacian pyramids. In: 2013 20th IEEE International Conference on Image Processing (ICIP), pp. 49–53 (2013)

  9. Pinson, M.H., Wolf, S.: A new standardized method for objectively measuring video quality. IEEE Trans. Broadcast. 50(3), 312–313 (2004)

    Article  Google Scholar 

  10. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)

    Article  Google Scholar 

  11. Wang, Z., Lu, L., Bovik, A.C.: Video quality assessment based on structural distortion measurement. Signal Process. Image Commun. 19(2), 121–132 (2004)

    Article  Google Scholar 

  12. Bt, I., Itu-r, Q., Itu, T., Assembly, R.: Recommendation ITU-R BT. 500-11—methodology for the subjective assessment of the quality of television pictures Annex 1 description of assessment methods common features. In: Methodology, vol. 211(BT.500-11), pp. 1–48 (2002)

  13. Singh, K.D., Rubino, G.: No-reference quality of experience monitoring in DVB-H networks. In: 2010 Wireless Telecommunications Symposium (WTS), pp. 1–6 (2010)

  14. Ghanbari, M.: Temporal aspect of perceived quality in mobile video broadcasting. IEEE Trans. Broadcast. 54(3), 641–651 (2008)

    Article  Google Scholar 

  15. Feghali, R., Speranza, F., Wang, D., Vincent, A.: Video quality metric for bit rate control via joint adjustment of quantization and frame rate. IEEE Trans. Broadcast. 53(1), 441–446 (2007)

    Article  Google Scholar 

  16. Ong, E., Yang, X., Lin, W., Lu, Z., Yao, S.: Perceptual quality metric for compressed videos. In: Proceedings, IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005 (ICASSP’05) vol. 2, pp. 581–584 (2005)

  17. Loh, W.T., Bong, D.B.L.: A just noticeable difference-based video quality assessment method with low computational complexity. Sens. Imaging 19(1), 1–20 (2018)

    Article  Google Scholar 

  18. Ou, Y.-F., Ma, Z., Liu, T., Wang, Y.: Perceptual quality assessment of video considering both frame rate and quantization artifacts. IEEE Trans. Circuits Syst. Video Technol. 21(3), 286–298 (2011)

    Article  Google Scholar 

  19. Ou, Y.-F., Xue, Y., Wang, Y.: Q-STAR: a perceptual video quality model considering impact of spatial, temporal, and amplitude resolutions. IEEE Trans. Image Process. 23(6), 2473–86 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  20. McCarthy, J.D., Sasse, M.A., Miras, D.: Sharp or smooth? In: Proceedings of the 2004 Conference on Human Factors in Computing Systems—CHI’04, pp. 535–542 (2004)

  21. Korhonen, J., Reiter, U., You, J.: Subjective comparison of temporal and quality scalability. In: 2011 Third International Workshop on Quality of Multimedia Experience, pp. 161–166 (2011)

  22. Wu, H., Claypool, M., Kinicki, R.: ARMOR—a system for adjusting repair and media scaling for video streaming. J. Vis. Commun. Image Represent. 19(8), 489–499 (2008)

    Article  Google Scholar 

  23. Pitrey, Y., Barkowsky, M., Le Callet, P., Pepion, R.: Subjective quality assessment of MPEG-4 scalable video coding in a mobile scenario. In: 2010 2nd European Workshop on Visual Information Processing (EUVIP), pp. 86–91 (2010)

  24. Vakili, A., Grégoire, J.-C.: QoE management for video conferencing applications. Comput. Netw. 57(7), 1726–1738 (2013)

    Article  Google Scholar 

  25. Mu, M., Romaniak, P., Mauthe, A., Leszczuk, M., Janowski, L., Cerqueira, E.: Framework for the integrated video quality assessment. Multimed. Tools Appl. 61(3), 787–817 (2012)

    Article  Google Scholar 

  26. Bampis, C.G., Li, Z., Katsavounidis, I., Bovik, A.C.: Recurrent and dynamic models for predicting streaming video quality of experience. IEEE Trans. Image Process. 27(7), 3316–3331 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  27. Freitas, P.G., Akamine, W.Y.L., Farias, M.C.Q.: Using multiple spatio-temporal features to estimate video quality. Signal Process. Image Commun. 64(August 2017), 1–10 (2018)

    Article  Google Scholar 

  28. Immich, R., Cerqueira, E., Curado, M.: Adaptive video-aware FEC-based mechanism with unequal error protection scheme. In: Proceedings of the 28th Annual ACM Symposium on Applied Computing—SAC’13 (2013)

  29. Immich, R., Borges, P., Cerqueira, E., Curado, M.: Adaptive motion-aware FEC-based mechanism to ensure video transmission. In: 2014 IEEE Symposium on Computers and Communications (ISCC), pp. 1–6 (2014)

  30. Rosário, D., Cerqueira, E., Neto, A., Riker, A., Immich, R., Curado, M.: A QoE handover architecture for converged heterogeneous wireless networks. Wirel. Netw. 19(8), 2005–2020 (2013)

    Article  Google Scholar 

  31. Zhang, H., Hu, H., Gao, G., Wen, Y., Guan, K.: DeepQoE: a unified framework for learning to predict video QoE. In: 2018 IEEE International Conference on Multimedia and Expo, pp. 1–6 (2018)

  32. Dai, Q., Lehnert, R.: Impact of packet loss on the perceived video quality. In: 2010 2nd International Conference on Evolving Internet, pp. 206–209 (2010)

  33. Lin, T.-L., Cosman, P.C., Reibman, A.R.: Perceptual impact of burthy versus isolated packet losses in H.264 compressed video. In: Proceedings of International Conference on Image Processing (2008)

  34. Kanumuri, S., Cosman, P.C., Reibman, A.R., Vaishampayan, V.A.: Modeling packet-loss visibility in MPEG-2 video. IEEE Trans. Multimed. 8(2), 341–355 (2006)

    Article  Google Scholar 

  35. Kai-Chieh Yang, K.C., Guest, C.C., El-Maleh, K., Das, P.K.: Perceptual temporal quality metric for compressed video. IEEE Trans. Multimed. 9(7), 1528–1535 (2007)

    Article  Google Scholar 

  36. Khan, A., Sun, L., Ifeachor, E.: Impact of video content on video quality for video over wireless networks. In: Fifth International Conference on Autonomic and Autonomous Systems (ICAS), pp. 277–282 (2009)

  37. Greengrass, J., Evans, J., Begen, A.C.: Not all packets are equal, Part 2: the impact of network packet loss on video quality. IEEE Intern. Comput. 13(2), 74–82 (2009)

    Article  Google Scholar 

  38. Aguiar, E., Cerqueira, E., Abelem, A., Mu, M., Zeadally, S.: Real-time QoE prediction for multimedia applications in wireless mesh networks. In: Network, pp. 592–596 (2012)

  39. Simone, F.D., Naccari, M., Dufaux, F., Tagliasacchi, M., Tubaro, S., Ebrahimi, T.: H.264/AVC video database for the evaluation of qualitiy metrics. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 2430–2433 (2010)

  40. Haßlinger, G., Hohlfeld, O.: The Gilbert-Elliott model for packet loss in real time services on the internet. In: 14th GI/ITG Conference on Measurement, Modeling, and Evaluation of Computer and Communication Systems (MMB), pp. 269–283 (2008)

  41. Jiang, X., Wang, Y., Wang, C.: No-reference video quality assessment for MPEG-2 video streams using BP neural networks. In: Proceedings of the 2nd International Conference on Interaction Sciences Information Technology, Culture and Human—ICIS’09, pp. 307–311 (2009)

  42. Sousa, R., Mota, E., Silva, E.N., Paixao, K.S., Faria, B., Neto, J.B.P.: GOP size influence in high resolution video streaming over wireless mesh network. In: The IEEE Symposium on Computers and Communications, pp. 1–3 (2010)

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, COMSATS Institute of Information Technology, Islamabad, Pakistan

    Safi Mustafa

  2. Department of Computing and Technology, Iqra University, Islamabad Campus, Islamabad, Pakistan

    Abdul Hameed

Authors
  1. Safi Mustafa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abdul Hameed
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Safi Mustafa.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mustafa, S., Hameed, A. Perceptual quality assessment of video using machine learning algorithm. SIViP 13, 1495–1502 (2019). https://doi.org/10.1007/s11760-019-01494-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-019-01494-5

Keywords

Search

Navigation