[1. Akhtar, Z., Siddique, K., Rattani, A., Lutfi S.L., Falk, T.H. (2019) Why is Multimedia Quality of Experience Assessment a Challenging Problem? IEEE Access, 7, pp. 117897-117915.]Search in Google Scholar
[2. Anegekuh, L., Sun, L., Jammeh, E., Mkwawa, I., Ifeachor, E. (2015) Content-Based Video Quality Prediction for HEVC Encoded Videos Streamed Over Packet Networks. IEEE Transactions on Multimedia, 17(8), pp. 1323-1334.10.1109/TMM.2015.2444098]Search in Google Scholar
[3. Bampis, C.G., Li Z., Bovik, A.C. (2019) Spatiotemporal Feature Integration and Model Fusion for Full Reference Video Quality Assessment. IEEE Transactions on Circuits and Systems for Video Technology, 29(8), pp. 2256-2270.10.1109/TCSVT.2018.2868262]Search in Google Scholar
[4. Blazekova, O., Vojtekova. M. (2019) Using of Parallel Coordinates in Finding Minimum Distance in Time-Space. Communications - Scientific Letters of the University of Zilina, 21(3), pp. 3-7.10.26552/com.C.2019.3.3-7]Search in Google Scholar
[5. Carvalho, F.D.T., Bertrand, P., Simoes, E.C. (2016) Batch SOM algorithms for interval-valued data with automatic weighting of the variables. Neurocomputing, vol. 182, pp. 66-81.10.1016/j.neucom.2015.11.084]Search in Google Scholar
[6. Cheng, Z., Ding, L., Huang, W., Yang, F., Qian, L. (2017) A unified QoE prediction framework for HEVC encoded video streaming over wireless networks. IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), pp. 1-6.10.1109/BMSB.2017.7986156]Search in Google Scholar
[7. Dybskaya, V. V., Sverchkov, P. A. (2017) Designing a Rational Distribution Network for Trading Companies. Transport and Telecommunication Journal, 18(3), pp. 181-193.10.1515/ttj-2017-0016]Search in Google Scholar
[8. Frnda, J., Nedoma, J., Vanus, J., Martinek, R. (2019) A Hybrid QoS-QoE Estimation System for IPTV Service. Electronics, 8(5), 585.10.3390/electronics8050585]Search in Google Scholar
[9. Gu, K., Tao, D., Qiao J., Lin, W. (2018) Learning a No-Reference Quality Assessment Model of Enhanced Images With Big Data. IEEE Transactions on Neural Networks and Learning Systems, 29(4), pp. 1301-1313.10.1109/TNNLS.2017.2649101]Search in Google Scholar
[10. International Telecommunications Union, ITU-T P.910. (2008) Subjective video quality assessment methods for multimedia applications.]Search in Google Scholar
[11. International Telecommunications Union, ITU-T P.913. (2016) Methods for the subjective assessment of video quality, audio quality and audiovisual quality of Internet video and distribution quality television in any environment.]Search in Google Scholar
[12. Kohonen, T. (1998) The self-organizing map. Neurocomputing, 21, pp. 1-6.10.1016/S0925-2312(98)00030-7]Search in Google Scholar
[13. Loh, W., Bong, D.B.L. (2018) A Just Noticeable Difference-Based Video Quality Assessment Method with Low Computational Complexity. Sensing and Imaging, 19, Article number: 33.10.1007/s11220-018-0216-9]Search in Google Scholar
[14. Loktev, Daniil A., Loktev, Alexey A., Salnikova, Alexandra V., Shaforostova, Anna A. (2019) Determination of the Dynamic Vehicle Model Parameters by Means of Computer Vision. Communications - Scientific letters of the University of Zilina, 21(3), pp. 28-34.10.26552/com.C.2019.3.28-34]Search in Google Scholar
[15. Mocanu, D.C., Pokhrel, J., Pablo Garella, J., Seppänen, J., Liotou, E., Narwaria, M. (2015) No-reference video quality measurement: added value of machine learning. Journal of Electronic Imaging, 24(6).10.1117/1.JEI.24.6.061208]Search in Google Scholar
[16. Mohamed, S., Rubino, G. (2002) A Study of Real-Time Packet Video Quality Using Random Neural Networks. IEEE Transactions on Circuits and Systems for Video Technology, 12(12).10.1109/TCSVT.2002.806808]Search in Google Scholar
[17. Mustafa, S., Hameed, A. (2019) Perceptual quality assessment of video using machine learning algorithm. Signal, Image and Video Processing, 13, pp. 1495–1502.10.1007/s11760-019-01494-5]Search in Google Scholar
[18. Ramirez-Alonso, G., Chacon-Murguia, M.I. (2016) Object detection in video sequences by a temporal modular self-adaptive SOM. Neural Computing & Applications, 27, pp. 411-430.10.1007/s00521-015-1859-2]Search in Google Scholar
[19. Sevcik, L., Voznak, M., Frnda, J. (2014) QoE Prediction Model for Multimedia Services in IP Network Applying Queuing Policy. In: 17th International Symposium on Performance Evaluation of Computer and Telecommunication Systems (SPECTS) part of SummerSim Multiconference, pp. 593-598.10.1109/SPECTS.2014.6879998]Search in Google Scholar
[20. Søgaard, J., Forchhammer, S., Korhonen, J. (2015) Video quality assessment and machine learning: Performance and interpretability. In: 7th International Workshop on Quality of Multimedia Experience (QoMEX).10.1109/QoMEX.2015.7148149]Search in Google Scholar
[21. Song, L., Tang, X., Zhang, W., Yang, X., Xia, P. (2013) The SJTU 4K video sequence dataset. In: 5th International Workshop on Quality of Multimedia Experience (QoMEX).10.1109/QoMEX.2013.6603201]Search in Google Scholar
[22. Valderrama, D., Gómez, N. (2016) Nonintrusive Method Based on Neural Networks for Video Quality of Experience Assessment. Advances in Multimedia, volume 2016.10.1155/2016/1730814]Search in Google Scholar
[23. Yuana, Y., Wang, C. (2019) IPTV video quality assessment model based on neural network. Journal of Visual Communication and Image Representation, 64, 102629.10.1016/j.jvcir.2019.102629]Search in Google Scholar