Visual Comfort Assessment Metric Based on Motion Features in Salient Motion Regions for Stereoscopic 3D Video

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 331)


Visual comfort assessment for stereoscopic 3D video is of great importance for stereoscopic safety and health issue. In order to investigate visual discomfort induced by motion features in salient motion regions, we propose a visual comfort assessment metric that focuses on pixel-level motion features in salient motion regions. In our framework, we propose the pixel-level motion features extraction method based on point detector, Kanade-Lucas-Tomasi(KLT) feature tracker, and Salient Motion Depth Extraction (SMDE) approach. The motion features are spatially pooled and temporally pooled to predict visual comfort score. Subjective assessments have been conducted to evaluate our proposed visual comfort metric using natural stereoscopic videos. The experiment results have been demonstrated that our proposed visual comfort metric improves the correlation with subjective assessments.


visual comfort assessment point detector KLT feature tracker SMDE approach 


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  1. 1.
    Tam, W.J., Speranza, F., Yano, S., Shimono, K.: Ono: Stereoscopic 3D-TV: Visual Comfort. IEEE Transactions on Broadcasting 99, 1–1 (2011)Google Scholar
  2. 2.
    Choi, J., Kim, D., Ham, B., Choi, S., Sohn, K.: Visual fatigue evaluation and enhancement for 2D-plus-depth video. In: 2010 17th IEEE International Conference on Image Processing (ICIP), pp. 2981–2984 (2010)Google Scholar
  3. 3.
    Yano, S., Ide, S., Mitsuhashi, T., Thwaites, H.: A study of visual fatigue and visual comfort for 3D HDTV/HDTV images. Displays 23(4), 191–201 (2002)CrossRefGoogle Scholar
  4. 4.
    Jung, Y.J., Lee, S., Sohn, H., Park, H.W., Ro, Y.M.: Visual comfort assessment metric based on salient object motion information in stereoscopic video. Journal of Electronic Imaging 21, 011008 (2012)CrossRefGoogle Scholar
  5. 5.
    ITU Recommendation. 500-11, Methodology for the subjective assessment of the quality of television pictures, International Telecommunication Union, Geneva, Switzerland (2002)Google Scholar
  6. 6.
    Tomasi, C., Kanade, T.: Detection and tracking of point features (1991)Google Scholar
  7. 7.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints 60(2), 91–110 (2004)Google Scholar
  8. 8.
    Culibrk, D., Mirkovic, M., Zlokolica, V., Pokric, M., Crnojevic, V., Kukolj, D.: Salient motion features for video quality assessment. IEEE Transactions on Image Processing 99, 1–1 (2011)MathSciNetGoogle Scholar
  9. 9.
  10. 10.
    Sheikh, H.R., Sabir, M.F., Bovik, A.C.: A statistical evaluation of recent full reference image quality assessment algorithms. IEEE Transactions on Image Processing 15(11), 3440–3451 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Institute of Image Communication and Information ProcessingShanghai Jiao Tong UniversityShanghaiChina
  2. 2.Shanghai Key Laboratory of Digital Media Processing and TransmissionsShanghai Jiao Tong UniversityShanghaiChina

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