Abstract
Pseudo-Subjective Quality Assessment (PSQA) is an effective way to prediction the Quality of experience (QoE) of video stream. The ANN-based PSQA model gives a decent QoE prediction accuracy when it is tested under the same condition as training. However, the performance of the model under mismatched conditions is little studied, and how to effectively adapt the models from one condition to another is still an open question. In this work, we first evaluated the performance of the ANN-based QoE prediction model under mismatched conditions. Our study shows that the QoE prediction accuracy degrades significantly when the model is applied to conditions different from the training condition. Further, we developed a feature transformation based model adaptation method to adapt the model from one condition to another. Experiments results show that the QoE prediction accuracy under mismatched conditions can be improved substantially using as few as five data samples under the new condition for model adaptation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
cisco: Cisco Visual Networking Index: Forecast and Methodology, 2012-2017 (2013)
Lin, C., Hu, J., Kong, X.Z.: Survey on Models and Evaluation of Quality of Experience. Chinese Journal of Computers 35, 1–15 (2012) (in Chinese)
R. ITU-T, P910: Subjective video quality assessment methods for multimedia applications (2008)
Pinson, M.H., Wolf, S.: A new standardized method for objectively measuring video quality. IEEE Transactions on Broadcasting 50, 312–322 (2004)
Mohamed, S.G.: A study of real-time packet video quality using random neural networks. IEEE Transactions on Circuits and Systems for Video Technology 12, 1071–1083 (2002)
Venkataraman, M., Chatterjee, M.: Inferring video QoE in real time. IEEE Network 25, 4–13 (2011)
Aguiar, E., Riker, A., Abelém, A., Cerqueira, E., Mu, M.: Video quality estimator for wireless mesh networks. In: IEEE 20th International Workshop on Quality of Service (IWQoS), pp. 1–9. IEEE (2012)
Menkovski, V., Exarchakos, G., Liotta, A.: Online QoE prediction. In: 2010 IEEE Second International Workshop on Quality of Multimedia Experience (QoMEX), pp. 118–123. IEEE (2010)
Chen, K.T., Tu, C.C., Xiao, W.C.: OneClick: A framework for measuring network quality of experience. In: INFOCOM 2009, pp. 702–710. IEEE (2009)
Piamrat, K., Viho, C., Bonnin, J.M., Ksentini, A.: Quality of experience measurements for video streaming over wireless networks. In: Sixth International Conference on Information Technology: New Generations, pp. 1184–1189. IEEE (2009)
Menkovski, V., Oredope, A., Liotta, A., Sánchez, A.C.: Predicting quality of experience in multimedia streaming. In: Proceedings of the 7th International Conference on Advances in Mobile Computing and Multimedia, pp. 52–59. ACM (2009)
Balachandran, A., Sekar, V., Akella, A., Seshan, S., Stoica, I., Zhang, H.: Developing a predictive model of quality of experience for internet video. In: Proceedings of the ACM SIGCOMM 2013, pp. 339–350. ACM (2013)
Agboma, F., Liotta, A.: QoE-aware QoS management. In: Proceedings of the 6th International Conference on Advances in Mobile Computing and Multimedia, pp. 111–116. ACM (2008)
Menkovski, V., Exarchakos, G., Liotta, A.: Machine learning approach for quality of experience aware networks. In: 2010 2nd International Conference on Intelligent Networking and Collaborative Systems (INCOS), pp. 461–466. IEEE (2010)
Agboma, F., Liotta, A.: Addressing user expectations in mobile content delivery. Mobile Information Systems 3, 153–164 (2007)
Mitchell, T.M.: Machine learning. McGraw-Hill Science/Engineering/Math. (1997)
Klaue, J., Rathke, B., Wolisz, A.: Evalvid – A framework for video transmission and quality evaluation. In: Kemper, P., Sanders, W.H. (eds.) TOOLS 2003. LNCS, vol. 2794, pp. 255–272. Springer, Heidelberg (2003)
Lei, X., Hamaker, J., He, X.: Robust feature space adaptation for telephony speech recognition. In: INTERSPEECH (2006)
Wang, H., He, X., Chang, M.W., Song, Y., White, R.W., Chu, W.: Personalized Ranking Model Adaptation for Web Search. In: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 323–332. ACM, Dublin (2013)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Deng, J., Zhang, L., Hu, J., He, D. (2014). Adaptation of ANN Based Video Stream QoE Prediction Model. In: Ooi, W.T., Snoek, C.G.M., Tan, H.K., Ho, CK., Huet, B., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2014. PCM 2014. Lecture Notes in Computer Science, vol 8879. Springer, Cham. https://doi.org/10.1007/978-3-319-13168-9_35
Download citation
DOI: https://doi.org/10.1007/978-3-319-13168-9_35
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13167-2
Online ISBN: 978-3-319-13168-9
eBook Packages: Computer ScienceComputer Science (R0)