Abstract:
In video streaming service, the user's Quality of Experience (QoE) is not only related to video signal quality received at consumer's devices, the users’ subjectivity mus...Show MoreMetadata
Abstract:
In video streaming service, the user's Quality of Experience (QoE) is not only related to video signal quality received at consumer's devices, the users’ subjectivity must also be considered. In this context, a video quality assessment method that takes into account the user's preference for video content is proposed in this research. In order to perform this task, the users' profiles that include their preferences were stored in a video server. Then, subjective tests of video quality assessment were conducted, in which evaluators had different video content preferences. Results show that the evaluators' QoE is highly correlated with the user's preference for video content type. Based on these experimental results, a function named Preference Factor (PF) is defined and used to adjust the quality index values obtained by an objective video quality metric running in the end user's device. The PF function also depends on video content type and quality index score. Using the PF function, the enhanced Video streaming Quality Metric (e-VsQM) is proposed and the results of its performance evaluation demonstrate that PF improves an objective video quality metric. Furthermore, e-VsQM has low complexity and can be utilized in different video services. Thus, an application scenario is presented, in which the proposed video quality metric is implemented.
Published in: IEEE Transactions on Consumer Electronics ( Volume: 60, Issue: 3, August 2014)
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1.
U. Reiter “Factors influencing Quality of Experience,” in Quality of Experience, S. Moeller and A. Raake, Eds. London : Springer, 2014, pp. 55–72.
2.
B. Dedtweiler-Bedell, J. Dedtweiler, and P. Salovey, “Mood-Congruent Perceptions of Success Depend on Self-other Framing,” Psychology Press-Cognition and Emotion, vol. 20, no. 2, pp. 196–216, 2006.
3.
M. Silva, J. Groeger, and M. Bradshaw, “Attention-memory interactions in scene perception,” Spatial Vision, vol. 19, no. 1, pp. 9–19, Netherlands, 2006.
4.
D. Rodriguez, J. Abrahão D. Begazo, R. Lopes, and G. Bressan, “Video quality subjective assessment considering cognitive criteria and user preferences on video content,” in Proc. 18th ACM Brazilian Symposium on Multimedia and the Web, pp. 269–272, Sao Paulo, Brazil, Oct. 2012.
5.
A. Khan, L. Sun, J. Fajardo, F. Liberal, E. Ifeachor, “Impact of end devices on subjective video quality assessment for QCIF video sequences,” in Proc. International Workshop on Quality Multimedia Experience, pp. 177–182, Mechelen, Belgium., Sep. 2011.
6.
A. Floris, L. Atzori, G. Ginesu, and D. Giusto, “QoE assessment of multimedia video consumption on tablet devices,” in Proc. Globecom Workshops: Quality of Experience for Multimedia Communications, pp. 1329–1334, California, US., Dec. 2012.
7.
F. Pereira, “Sensations, perceptions and emotions: towards quality of experience evaluation for consumer electronics video adaptations ” in Proc. International Workshop on Video Processing and Quality Metrics for Consumer Electronics, Scottsdale, US., Jan. 2005.
8.
A. Banitalebi-Dehkordi, M. Pourazad, and P. Nasiopoulos, “Effect of high frame rates on 3D video quality of experience,” in Proc. IEEE International Conference on Consumer Electronics, pp. 416–417, Las Vegas, US., Jan. 2014.
9.
V. Adzic, H. Kalva, and B. Furht, “Optimizing video encoding for adaptive streaming over HTTP,” IEEE Trans. on Consumer Electron, vol. 58, no. 2, pp. 397–403, May 2012.
10.
H-J. Park and D-H. Har, “Subjective Image Quality Assessment based on Objective Image Quality Measurement Factors,” IEEE Trans. on Consumer Electron., vol. 57, no. 3, pp. 1176–1184, Aug. 2011.
11.
S. Chikkerur, V. Sundaram, M. Reisslein, and L. Karam, “Objective Video Quality Assessment Methods: A Classification, Review, and Performance Comparison.,” IEEE Trans. on Broadcasting, vol. 57, no. 2, pp. 165–182, Jun. 2011.
12.
D. Rodriguez, R. Lopes, and G. Bressan, “Video quality assessment in video streaming services considering user preference for video content,” in Proc. International Conference on Consumer Electronics, pp. 570–571, Las Vegas, US., Jan. 2014.
13.
ISO/IEC IS 23009–1, “Information Technology - Dynamic adaptive streaming over HTTP (DASH),” Apr. 2012.
14.
ITU-R Recommendation BT.500, “Methodology for the Subjective Assessment of the Quality of Television Pictures,” ITU-T, Geneva, Switzerland, Jan. 2012.
15.
ITU-T Recommendation P.910, “Subjective Video Quality Assessment Methods for Multimedia Applications,” ITU-T, Geneva, Switzerland, Apr. 2008.
16.
Q. Xu, Q. Huang, and Y. Yao, “Online Crowdsourcing Subjective Image Quality Assessment,” in Proc. of the 20th ACM international conference on Multimedia, pp. 359–368, Nara, Japan, Oct. 2012.
17.
F. Ribeiro, D. Florencio, and V. Nascimento, “Crowdsourcing Subjective Image Quality Evaluation,” in Proc. of 18th IEEE International Conference on Image Processing, pp. 3097–3100, Brussels, Belgium, Sep. 2011.
18.
C. Keimel, J. Habigt, C. Horch, and K. Diepold, “QualityCrowd-A framework for crowd-based quality evaluation,” in Proc. Picture Coding Symposium, pp. 245–248, Krakow, Poland, May. 2012.
19.
T. Hossfeld, C. Keimel, M. Hirth, B. Gardlo, J. Habigt, K. Diepold, and P. Tran-Gia, “Best Practices for QoE Crowdtesting: QoE Assessment with Crowdsourcing,” IEEE Trans. on Multimedia, vol. 16, no. 2, pp. 541–558, Feb. 2014.
20.
B. Gardlo, M. Ries, T. Hossfeld, and R. Schatz, “The impact of crowdsourcing platform choice on experimental results,” in Proc. International Workshop on Quality of Multimedia Experience, pp. 35–36, Yarra Valley, Australia, Jul. 2012
21.
A. Takahashi, D. Hands, and V. Barriac, “Standardization activities in the ITU for a QoE assessment of IPTV,” IEEE Communications Magazine, vol. 46, no. 2, pp. 78–84, Feb. 2008.
22.
K. Yamagishi and T. Hayashi, “Parametric packet-layer model for monitoring video quality of IPTV services,” in Proc. Int. Conference of Communications, pp. 110–114, Beijing, China, May 2008.
23.
M. Garcia and A. Raake, “Impairment-factor-based audio-visual quality model for IPTV,” in Proc. International Workshop on Quality Multimedia Experience, pp. 1–6, California, US., Jul. 2009.
24.
M. Martines, M. Lopez, P. Pinol, M. Malumbres, and J. Oliver, “Study of Objective Quality Assessment Metrics for Video CodecDesign and Evaluation,” in Proc. IEEE International Symposium on Multimedia, pp. 517–524, California, US., Dec. 2006.
25.
B. Ciubotaru, G.-M. Muntean, and G. Ghinea, “Objective assessment of region of interest-aware adaptive multimedia streaming quality,” IEEE Trans. on Broadcasting, vol. 55, no. 2, pp. 202–212, Jun. 2009.
26.
T. Porter and X. H. Peng, “An Objective Approach to Measuring Video Playback Quality in Loss Networks using TCP,” IEEE Communications Letters, vol. 15, no. 1, pp. 76–78, Jan. 2011.
27.
R. Mok, E. Chan, and R. Chang, “Measuring the Quality of Experience of HTTP Video Streaming,” in Proc. IEEE International Symposium on Integrated Network Management, pp. 485–492, Dublin, Ireland, May 2011.
28.
D. Rodriguez, J. Abrahao, D. Begazo, R. Lopes, and G. Bressan, “Quality metric to assess video streaming service over TCP considering temporal location of pauses,” IEEE Trans. on Consumer Electron., vol. 58, no. 3, pp. 985–992, Aug. 2012.
29.
Q. Dai and R. Lehnert, “Impact of Packet Loss on the Perceived Video Quality,” in Proc. International Conference. on Evolving Internet, pp. 206–209, Valencia, Spain, Sept. 2010.
30.
M. Pinson and S. Wolf, “Application of the NTIA General Video Quality Metric VQM to HDTV quality monitoring,” in Proc. International Workshop Video Processing Quality Metrics for Consumer Electronics, Chandler, US., Jan. 2007.