Advertisement

Study on Comfort Prediction of Stereoscopic Images Based on Improved Saliency Detection

  • Minghan Du
  • Guangyu Nie
  • Yue Liu
  • Yongtian Wang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 875)

Abstract

This paper proposed a saliency-dependent measure to predict visual comfort of stereoscopic. Considering the drawbacks of traditional stereoscopic display visual comfort assessment, a more accurate visual comfort prediction method based on improved stereoscopic saliency detection algorithm was proposed in this paper. The proposed approach includes 3 steps. The first step involves the calculation of region contrast, background prior, surface orientation prior and depth prior which aims to generate a stereoscopic saliency map. The second step is the extraction of visual comfort perception features. Finally, the prediction performance is evaluated by using SVR. The experimental results demonstrate that our method improves the prediction accuracy a lot compared with the related work.

Keywords

Stereoscopic display Visual comfort Saliency detection Assessment system 

Notes

Acknowledgement

This work has been supported by the National Technology Support Program of China (Grant No. 2015BAK01B05).

References

  1. 1.
    Lambooij, M., Fortuin, M., Heynderickx, I.: Visual discomfort and visual fatigue of stereoscopic displays: a review. J. Imag. Sci. Technol. 53(3), 30201-1–30201-14(14) (2009)Google Scholar
  2. 2.
    Hoffman, D.M., Girshick, A.R.: Vergence–accommodation conflicts hinder visual performance and cause visual fatigue. J. Vis. 8(3), 33.1 (2008)CrossRefGoogle Scholar
  3. 3.
    Choi, J., Kim, D., Ham, B.: Visual fatigue evaluation and enhancement for 2D-plus-depth video, pp. 2981–2984 (2010)Google Scholar
  4. 4.
    Lambooij, M., Ijsselsteijn, W.A., Heynderickx, I.: Visual discomfort of 3D TV: assessment methods and modeling. Displays 32(4), 209–218 (2011)CrossRefGoogle Scholar
  5. 5.
    Jung, Y.J., Sohn, H., Lee, S.I.: Predicting visual discomfort of stereoscopic images using human attention model. IEEE Trans. Circ. Syst. Video Technol. 23(12), 2077–2082 (2013)CrossRefGoogle Scholar
  6. 6.
    Sohn, H., Jung, Y.J., Lee, S.I.: Predicting visual discomfort using object size and disparity information in stereoscopic Images. Trans. Broadcast. 59(1), 28–37 (2013)CrossRefGoogle Scholar
  7. 7.
    Li, H., Luo, T., Xu, H.: Saliency detection of stereoscopic 3D images with application to visual discomfort prediction. 3D Res. 8(2), 14 (2017)CrossRefGoogle Scholar
  8. 8.
    Fan, X., Liu, Z., Sun, G.: Salient region detection for stereoscopic images. In: International Conference on Digital Signal Processing, pp. 454–458. IEEE (2014)Google Scholar
  9. 9.
    Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell. 20(11), 1254–1259 (1998)CrossRefGoogle Scholar
  10. 10.
    Yang, C., Zhang, L., Lu, H.: Saliency detection via graph-based manifold ranking. In: Computer Vision and Pattern Recognition, pp. 3166–3173. IEEE (2013)Google Scholar
  11. 11.
    Li, J., Levine, M.D., An, X.: Visual saliency based on scale-space analysis in the frequency domain. IEEE Trans. Pattern Anal. Mach. Intell. 35(4), 996–1010 (2016)CrossRefGoogle Scholar
  12. 12.
    Peng, H., Li, B., Xiong, W., Hu, W., Ji, R.: RGBD salient object detection: a benchmark and algorithms. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8691, pp. 92–109. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-10578-9_7CrossRefGoogle Scholar
  13. 13.
    Cheng, H., Zhang, J., An, P.: A novel saliency model for stereoscopic images. In: International Conference on Digital Image Computing: Techniques and Applications, pp. 1–7. IEEE (2015)Google Scholar
  14. 14.
    Tang, C., Hou, C.: RGBD salient object detection by structured low-rank matrix recovery and Laplacian constraint. Trans. Tianjin Univ. 23(2), 176–183 (2017)CrossRefGoogle Scholar
  15. 15.
    Chen, Q.X., Fu, L.H., Li, C.C.: A new depth saliency method based on selective difference, vol. 12, p. 03018 (2017)Google Scholar
  16. 16.
    Qu, L., He, S., Zhang, J.: RGBD salient object detection via deep fusion. IEEE Trans. Image Process. 26(5), 2274–2285 (2016)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Ren, J., Gong, X., Yu, L.: Exploiting global priors for RGB-D saliency detection. In: Computer Vision and Pattern Recognition Workshops, pp. 25–32. IEEE (2015)Google Scholar
  18. 18.
    Cheng, M.M., Mitra, N.J., Huang, X.: Salient object detection and segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 1 (2013)Google Scholar
  19. 19.
    Binocular, D.S., Motion, B., Parallax, M.: Perception of three-dimensional space: how do we use information derived from one or both eyes to perceive the spatial layout of our surroundings. In: Perception of the Visual Environment, pp. 257–294. Springer, New York (2002).  https://doi.org/10.1007/0-387-21650-2_9
  20. 20.
    Ciptadi, A., Hermans, T., Rehg, J.: An in depth view of saliency. In: British Machine Vision Conference, pp. 112.1–112.11 (2013)Google Scholar
  21. 21.
    Lang, M., Hornung, A., Wang, O.: Nonlinear disparity mapping for stereoscopic 3D. ACM Trans. Graph. 29(4), 1–10 (2010)CrossRefGoogle Scholar
  22. 22.
    Shao, F., Jiang, Q.P., Jiang, G.Y.: Prediction of visual discomfort of stereoscopic images based on saliency analysis. Opt. Precis. Eng. 22(6), 1631–1638 (2014)CrossRefGoogle Scholar
  23. 23.
    IVY Lab stereoscopic image database. http://ivylab.kaist.ac.kr/demo/3DVCA/3DVCA.htm

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Minghan Du
    • 1
  • Guangyu Nie
    • 1
  • Yue Liu
    • 1
  • Yongtian Wang
    • 1
  1. 1.Beijing Engineering Research Center of Mixed Reality and Advanced Display School of OptoelectronicsBeijing Institute of TechnologyBeijingChina

Personalised recommendations