A QoE Assessment System in Distance Education
Dengyin Zhang, Yangyang Xu, Chunling Cheng
DOI: 10.4236/eng.2011.31011   PDF    HTML     4,941 Downloads   9,334 Views   Citations


It is a challenging task to improve the real-time property and objectivity of the effect assessment for the distance education. This paper presents a QoE (Quality of Experience) assessment system based on the attention of online user. The system captures the video frames from two cameras periodically and synchronously, using the adaptive image binarization based on the linear average threshold for the pretreatment, then processing with edge detection and filtering in the cross-directions at the same time. System gets the position of computer screen and user eyeball. Analyzing the detection results comprehensively obtains the attention of online user by some judging conditions, and finally acquires the quality of user experience. Experimental results demonstrate the feasibility and efficiency.

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D. Zhang, Y. Xu and C. Cheng, "A QoE Assessment System in Distance Education," Engineering, Vol. 3 No. 1, 2011, pp. 90-96. doi: 10.4236/eng.2011.31011.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] M. Fiedler, T. Hossfeld and T. G. Phuoc, “A Generic Quantita-tive Relationship between Quality of Experience and Quality of Service,” IEEE Transactions on Network, Vol. 24, No. 2, March-April 2010, pp. 36-41.
[2] H. J. Kim and S. G. Choi, “A Study on a QoS/ QoE Correlation Model for QoE Evaluation on IPTV Service,” The 12th International Conference on Advanced Communication Technology (ICACT), Vol. 2, Feb-ruary 2010, pp. 1377-1382.
[3] J. Y. Zhang, Y. G. Wang and B. Rong, “QoS/QoE Techniques for IPTV Transmissions,” IEEE International Symposium on Broadband Multimedia Sys-tems and Broadcasting (BMSB’09), Vol. 5, May 2009, pp. 1-6. doi:10.1109/ISBMSB.2009.5133817
[4] B. Towie, “Distance Learning on the Rise,” Metro Canada, November 2008.
[5] D. A. Harris and C. Krousgrill, “Distance Education: New Tech-nologies and New Directions,” Proceedings of the IEEE, Vol. 96, No. 6, 2008, pp. 917-930. doi:10.1109 /JPROC.2008.921612
[6] C. H. Muntean, “Improving Learner Quality of Experience by Content Adaptation Based on Network Conditions,” Computers in Human Behavior, Vol. 24, No. 2, July 2008, pp. 1452-1472. doi:10.1016/j.chb.2007.07.016
[7] R. Acevedo, F. Martinez and D. Gonzalez, “Case-Based Reasoning and System Identifi-cation for Control Engineering Learning,” IEEE Transactions on Education, Vol. 51, No. 2, May 2008, pp. 271-281. doi:10.1109/TE.2007. 909361
[8] A. A. Hopgood and A. J. Hirst, “Keeping a Distance- Education Course Current Through E-Learning and Contextual Assessment,” IEEE Transactions on Education, Vol. 50, No. 1, 2007, pp. 85-96. doi:10.1109/TE.2006.88 8905
[9] W. M. K. W. M. Khairos-faizal and A. J.Nor'aini, “Eyes Detection in Facial Images Using Circular Hough Transform,” Proceedings of 2009 5th In-ternational Colloquium on Signal Processing and Its Applica-tions, Vol. 3, March 2009, pp. 238-242.
[10] J. G. Gao, S. Q. Zhang and W. Lu, “Application of Hough Transform in Eye Tracking and Targeting,” 9th International Conference on Electronic Measurement & Instruments (ICEMI'09), Vol. 3, August 2009, pp. 751- 754.
[11] A. Dawoud, M. S. Kamel, “Iterative Multimodel Subimage Binarization for Handwritten Character Segmentation,” IEEE Transactions on Image Processing, Vol. 13, No. 9, pp. 1223-1230, 2004. doi:10.1109/TIP.2004.8331 01
[12] R. Medina-Carnicer, F. J. Madrid-Cuevas, N. L. Fernández-García and A. Carmo-na-Poyato, “Evaluation of Global Thresholding Techniques in Non-Contextual edge Detection,” Pattern Recognition Letters, Vol. 26, No. 10, July 2005, pp. 1423-1434. doi:10.1016/j.patrec. 2004.11.024
[13] K. L. Chung, W. J. Yang, W.M. Yan and C. C. Wang, “Demosaicing of Color Filter Array Captured Images Using Gradient Edge Detection Masks and Adaptive Hetero-geneity-Projection,” IEEE Transactions on Image Processing, Vol. 17, 2008, pp. 2356-2367. doi:10.1109/ TIP.2008.2005561
[14] R. Medina-Carnicer and F. J. Madrid Cuevas, “Unimodal Thresholding for Edge Detection,” Pattern Recognition, Vol. 41, No. 7, July 2008, pp. 2337-2346. doi:10.1016/j. patcog.2007.12.007
[15] J. Bernsen, “Dynamic Thresholding of Gray Level Image,” ICPR`86: Proceedings of International Conference on Pattern Recognition, Berlin, 1986, pp. 1251-1255.
[16] K. K. V. Toh, H. Ibrahim and M. N. Ma-hyuddin, “Salt- and-Pepper Noise Detection and Reduction Using Fuzzy Switching Median Filter,” IEEE Transactions on Consumer Electronics, Vol. 54, No. 4, November 2008, pp. 1956-1961. doi:10.1109/TCE.2008.4711258
[17] Z. Pan, G. Healey, M. Prasad and B. Tromberg, “Face recognition in hyperspectral images,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 25, No. 12, December 2003, pp. 1552-1560. doi:10.1109/ TPAMI.2003.1251148
[18] P. Y. Chen and C. Y. Lien, “An Efficient Edge-Preserving Algorithm for Removal of Salt-and- Pepper Noise,” IEEE Signal Processing Letters, Vol. 15, pp. 833-836, 2008. doi:10.1109/LSP.2008.2005047
[19] I. Sobel and G. Feldman, “A 3x3 Isotropic Gradient Operator for Image Processing,” Pattern Classification and Scene Analysis, 1973, pp. 271-272.

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