Streaming Video QOE
An important aspect of streaming video delivery is the Quality of Experience (QoE) that is experienced by the ultimate viewer or video client, who may be watching the stream on a smartphone, tablet, laptop, or big-screen HD television. QoE can be defined in various ways, including audio, visual proprioception, and tactile aspects. In LIVE, our research is primarily being conducted on the key problem of controlling bit rate as it is balanced against other video impairments, such as stalling (rebuffering) events and start-up delays. Towards this end we have been developing models, and algorithms that predict visual QoE, and we have also spent considerable time on developing first-of- a-kind QoE databases that specifically deal with human subjective opinions of stalls, compression, and combinations of these. Understanding the relationship between human reactions to compression and stalls is critical, as is understanding the role of the network and the video buffer in most devices. Some of the key papers follow:
C. Yim and A.C. Bovik, “Evaluation of temporal variation of video quality in packet loss networks,” Signal Processing: Image Communication, vol. 26, no. 1, pp. 24-38, January 2011.
K. Seshadrinathan and A.C. Bovik, “Automatic prediction of perceptual quality of multimedia signals – A survey,” International Journal of Multimedia Tools and Applications, Special Issue on Survey Papers in Multimedia by World Experts, vol. 51, no. 1, pp. 163-186, January 2011.
A.K. Moorthy and A.C. Bovik “Visual Quality Assessment Algorithms: What Does the Future Hold?” International Journal of Multimedia Tools and Applications, Special Issue on Survey Papers in Multimedia by World Experts, vol. 51, no. 2, pp. 675-696, February 2011.
A.K. Moorthy, L.K. Choi, A.C. Bovik and G. de Veciana, “Video quality assessment on mobile devices: Subjective, behavioral, and objective studies,” IEEE Journal of Selected Topics in Signal Processing, Special Issue on New Subjective and Objective Methodologies for Audio and Visual Signal Processing, vol. 6, no. 6, pp. 652-671, October 2012.
T. Kim, J. Kang, S. Lee and A.C. Bovik, “Interactive continuous quality evaluation of subjective 3D video quality of experience,” IEEE Transactions on Multimedia, vol. 16. no. 2, pp. 387-402, February 2014.
C. Chen, L.K. Choi, G. de Veciana, C. Caramanis, R.W. Heath, Jr. and A.C. Bovik, “A model of the time-varying subjective quality of HTTP video streams with rate adaptations,” IEEE Transactions on Image Processing, vol. 23, no. 5, pp. 2206-2221, May 2014.
S. Gunasekar, J. Ghosh and A.C. Bovik, “Face detection on distorted images augmented by perceptual quality-aware features,” IEEE Transactions on Information Forensics and Security, Special Issue on Facial Biometrics in the Wild, vol. 9, no. 12, pp. 2119-2131, December 2014.
M.H. Pinson, L.K. Choi, A.K. Moorthy, and A.C. Bovik, “Temporal video quality model accounting for variable frame delay distortions,” IEEE Transactions on Broadcasting, vol. 60, no. 4, pp. 637-649, December 2014.
H. Yeganeh, R. Kordasiewicz, M. Gallant, D. Ghadiyaram and A.C. Bovik, “Delivery quality score model for internet video,” IEEE International Conference on Image Processing, Paris, France, October 27-30, 2014.
D. Ghadiyaram, A.C. Bovik, H.Yeganeh, R. Kordasiewicz and M. Gallant, “Study of the effects of stalling events on the quality of experience of mobile streaming videos,” IEEE Global Conference on Signal and Information Processing, Atlanta, Georgia, December 3-5, 2014.
D. Ghadiyaram, J. Pan and A.C. Bovik, “A time-varying subjective quality model for mobile streaming videos with stalling events,” SPIE Conference on Applications of Digital Image Processing XXXVIII, San Diego, California, August 10-13, 2015.
C. Chen, X. Zhu, G. de Veciana, A.C. Bovik and R.W. Heath, “Rate adaptation and admission control for video transmission with subjective quality constraints,” IEEE Journal on Selected Topics in Signal Processing, Special Issue on Visual Signal Processing for Wireless Networks (VSPWN), vol. 9, no. 1, pp. 22-36, February 2015.
C. Bampis and A.C. Bovik, “Continuous prediction of streaming video QoE using dynamic networks,” IEEE Signal Processing Letters, vol. 24, no. 7, pp. 1083-1087, July 2017.
D. Ghadiyaram, J. Pan, A.C. Bovik, A. Moorthy, P. Panda and K.C. Yang, “In-capture mobile video distortions: A study of subjective behavior and objective algorithms,” IEEE Transactions on Circuits and Systems for Video Technology, to appear, in press.
C. Bampis, Z. Li, A.K. Moorthy, I. Katsavounidis, A. Aaron and A.C. Bovik, “Study of temporal effects on subjective video quality of experience,” IEEE Transactions on Image Processing, to appear, in press.
D. Ghadiyaram, J. Pan and A.C. Bovik, “Perceptual quality of experience predictor,” U.S. Patent pending, 2017.