Auto-stereoskopik 3BTV için Öznel Deneyim Kalitesi Modellerinin

Year 2019, , 1061 - 1072, 31.07.2019

Nükhet Özbek

https://doi.org/10.29130/dubited.489137

Abstract

Comparison of Subjective QoE Models for Auto-Stereoscopic 3DTV

Abstract

Measuring
Quality of Experience (QoE) of stereoscopic 3D video is a hot research topic.
Subjective models are considered as the most reliable and facilitate development
of objective models. However, to collect user opinion scores takes long time.
Therefore, new subjective assessment models should be proposed providing not
only time-efficiency but also good accuracy and reliability. In this study, two
novel subjective QoE models are proposed as alternative to the conventional
Double Stimulus Continuous Quality Scale method. Also, a fair comparison method
is proposed to evaluate performances of the three subjective methods using the
same stimuli prepared with the most recent multi-view video codec on an
auto-stereoscopic 3DTV. Correlations are calculated using two objective QoE
measures using depth maps and structural similarities. The results demonstrate
that the performances of the proposed models are comparable to each other and
both models are superior to the conventional method.

Keywords

QoE, 3DTV, SSIM, multi-view video plus depth, paired comparison

References

• [1] ITU-R Recommendations Methodology for the Subjective Assessment of the Quality of Television Pictures, ITU-R Recommendation BT.500-11, 2002.
• [2] ITU-R Recommendations Methodology for the Subjective Assessment of Video Quality in Multimedia Applications, ITU-R Recommendation BT.1788, 2007.
• [3] N. Özbek, G. Ertan, and O. Karakuş, “Perceptual quality evaluation of asymmetric stereo video coding for efficient 3D rate scaling”, Turk. J. Elec. Eng. & Comp. Sci., vol. 22, no. 3, pp. 663–678, 2014.
• [4] P. Aflaki, M. M. Hannuksela, and M. Gabbouj, “Subjective quality assessment of asymmetric stereoscopic 3D video,” Signal, Image and Video Processing, vol. 9, pp. 1–15, 2015.
• [5] J. Li, O. Kaller, F. D. Simone, J. Hakala, D. Juzska, and P. L. Callet, “Cross-lab Study on Preference of Experience in 3DTV: Influence from Display Technology and Test Environment”, IEEE Workshop: Quality of Multimedia Experience (QoMEX), 2013, pp. 46–47.
• [6] ITU-R Recommendations Subjective Video Quality Assessment Methods for Multimedia Applications, ITU-R Recommendation P.910, 2008.
• [7] J. Li, M. Barkowsky, and P. Le Callet, “Analysis and improvement of a paired comparison method in the application of 3DTV subjective experiment”, IEEE International Conference on Image Processing (ICIP), 2012, pp. 629–632.
• [8] Z. Duanmu, A. Rehman, K. Zeng and Z. Wang, “Quality-of-Experience Prediction for Streaming Video,” IEEE International Conference on Multimedia and Expo (ICME), 2016, pp. 145–151.
• [9] F. Qi, D. Zhao, X. Fan, and T. Jiang, “Stereoscopic video quality assessment based on visual attention and just-noticeable difference models,” Signal, Image and Video Processing, vol. 10, no.4, pp. 737–744, 2016.
• [10] E. Şenol and N. Özbek, “Quality of experience measurement of compressed multi-view video”, Signal Processing: Image Communication, vol. 57, pp.147–159, 2017.
• [11] P. Merkle, A. Smolic, K. Müller, and T. Wiegand, “Multi-view Video plus Depth Representation and Coding”, IEEE International Conference on Image Processing (ICIP), 2007, pp. 201–204.
• [12] C. Zhu, Y. Zhao, L. Yu, and M. Tanimoto, 3D-TV System with Depth-Image Based Rendering: Architectures Techniques and Challenges, USA: Springer, 2013.
• [13] G. J. Sullivan, J.-R. Ohm, J.-R. Han, and T. Wiegand, “Overview of Highy Efficiency Video Coding (HEVC) Standard”, IEEE Trans. Circuits and Systems for Video Technology, vol. 22, no. 7, pp. 1649–1668, 2012.
• [14] ITU-R Recommendations Subjective Methods for the Assessment of Stereoscopic 3DTV Systems, ITU-R Recommendation BT.2021, 2012.
• [15] G.J. Sullivan, J. M. Boyce, Y. Chen, J.-R. Ohm, C. A. Segall, and A. Vetro, “Standardized Extensions of High Efficiency Video Coding (HEVC)”, IEEEJ. Sel. Top. Signal Proc., vol. 7, no. 6, pp. 1001–1016, 2013.
• [16] L. Zhang, G. Tech, K. Wegner, and S. Yea, “3D-HEVC Test Model 5”, JCT-3V, 2013.
• [17] Fraunhofer Heinrich Hertz Institute, 3D-HEVC Reference Software. (2015) [Online]. Available: https://hevc.hhi.fraunhofer.de/3dhevc.
• [18] D. Rusanovskyy, K. Müller, and A. Vetro, “Common Test Condition of 3DV Core Experiments”, JCT-3V, 2012.
• [19] J. Lee, F. De Simone, and T. Ebrahimi, “Subjective quality evaluation via paired comparison: application to scalable video coding”, IEEE Transactions on Multimedia, vol. 13, no. 5, pp. 882–893, 2011.
• [20] J. Lee, L. Goldmann, and T. Ebrahimi, “Paired comparison-based subjective quality assessment of stereoscopic images”, Multimedia Tools and Applications, vol. 67, no. 1, pp. 31–49, 2013.
• [21] R. Bradley and M. Terry, “Rank analysis of incomplete block designs: I. the method of paired comparisons”, Biometrika, vol. 39, no. 3,4, pp. 324–345, 1952.
• [22] F. Wickelmaier and C. Schmid, “A matlab function to estimate choice model parameters from paired comparison data”, Behavior Research Methods, vol. 36, no. 1, pp. 29–40, 2004.
• [23] V. De Silva, H. K. Arachchi, E. Ekmekcioglu, and A. Kondoz, “Toward an Impairment Metric for Stereoscopic Video: A Full-Reference Video Quality Metric to Assess Compressed Stereoscopic Video”, IEEE Transactions Image Processing, vol. 22, no. 9, pp. 3392–3404, 2013.
• [24] Z. Wang, A.C. Bovik, H.R. Sheikh, and E.P. Simoncelli, “Image quality assessment: from error visibility to structural similarity”, IEEE Transactions on Image Processing, vol. 13, no. 4, pp. 600–612, 2004.