3D video quality evaluation based on adaptive streaming over HTTP

被引：0

作者：

Zhai Y. ^{[1
]}

Liu Y. ^{[1
]}

Xu Y. ^{[1
]}

Chen Z. ^{[1
]}

Fang Y. ^{[1
]}

Zhao T. ^{[1
]}

机构：

[1] College of Physics and Information Engineering, Fuzhou University, Fuzhou

来源：

Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics | 2019年 / 45卷 / 12期

基金：

中国国家自然科学基金;

关键词：

3D video; Convolutional neural network (CNN); HTTP adaptive streaming; Quality of experience; Video quality assessment;

D O I：

10.13700/j.bh.1001-5965.2019.0383

中图分类号：

学科分类号：

摘要：

The key for 3D video network service is to improve the quality of experience (QoE) of users, which can be, however, affected by mutable network conditions and video contents. For conventional 2D videos, the HTTP adaptive streaming (HAS) technique has demonstrated its significance in improving user QoE by utilizing dynamically switched bitrates, while for 3D video transmission with at least two video streams, this technique has not yet been extensively explored. Dynamic conversion policy of the video quality level is the core of HAS technique. In this work, we investigate the impact on user QoE when introducing dynamic bitrates to different 3D videos. A subjective database is built to illustrate the connection between block-level objective quality, which changes with bitrates, and the QoE of 3D vision. Through this, we propose a convolutional neural network (CNN) based QoE model that effectively assesses the QoE by block-level objective quality. The Pearson linear correlation coefficient (PLCC) of the model predictive value and the mean opinion score (MOS) is 0.906.The proposed framework can provide guidance to inter-view bitrate balancing of HAS for 3D video transmission. © 2019, Editorial Board of JBUAA. All right reserved.

引用

页码：2456 / 2462

页数：6

共 24 条

[1] Miller K., Quacchio E., Gennari G., Et al., Adaptation algorithm for adaptive streaming over HTTP, Proceedings of the 19th International Packet Video Workshop, pp. 173-178, (2012)
[2] Zhang X., Toni L., Frossard P., Et al., Adaptive streaming in interactive multiview video systems, IEEE Transactions on Circuits and Systems for Video Technology, 29, 4, pp. 1130-1144, (2019)
[3] Zhao S., Medhi D., SDN-Assisted adaptive streaming framework for tile-based immersive content using MPEG-DASH, Proceedings of the IEEE Conference on Network Function Virtualization and Software Defined Networks, pp. 1-6, (2017)
[4] Park G., Lee J., Lee G., Et al., Efficient 3D adaptive HTTP streaming scheme over internet TV, Proceedings of the IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, pp. 1-6, (2012)
[5] Garcia M.N., Simone F.D., Tavakoli S., Et al., Quality of Experience and HTTP adaptive streaming: A review of subjective studies, Proceedings of the 6th International Workshop on Quality of Multimedia Experience, QoMEX, pp. 141-146, (2014)
[6] Mok R., Luo X., Chan E., Et al., QDASH: A QoE-aware DASH system, Proceedings of the 3rd Multimedia Systems Conference, pp. 11-22, (2012)
[7] Tavakoli S., Gutierrez J., Garcia N., Subjective quality study of adaptive streaming of monoscopic and stereoscopic video, IEEE Journal on Selected Areas in Communications, 32, 4, pp. 684-692, (2014)
[8] Shao F., Lin W., Gu S., Et al., Perceptual full-reference quality assessment of stereoscopic images by considering binocular visual characteristics, IEEE Transactions on Image Processing, 22, 5, pp. 1940-1953, (2013)
[9] Lin Y.H., Wu J.L., Quality assessment of stereoscopic 3D image compression by binocular integration behaviors, IEEE Transactions on Image Processing, 23, 4, pp. 1527-1542, (2014)
[10] Zhang Y., Chandler D.M., 3D-MAD: A full reference stereoscopic image quality estimator based on binocular lightness and contrast perception, IEEE Transactions on Image Processing, 24, 11, pp. 3810-3825, (2015)

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