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The Scientific World Journal
Volume 2014 (2014), Article ID 890562, 11 pages
http://dx.doi.org/10.1155/2014/890562
Research Article

A Simple Quality Assessment Index for Stereoscopic Images Based on 3D Gradient Magnitude

Faculty of Information Science and Engineering, Ningbo University, Ningbo 315211, China

Received 21 February 2014; Revised 9 June 2014; Accepted 10 June 2014; Published 15 July 2014

Academic Editor: Antonio Fernández-Caballero

Copyright © 2014 Shanshan Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. W. Lin and C. Jay Kuo, “Perceptual visual quality metrics: a survey,” Journal of Visual Communication and Image Representation, vol. 22, no. 4, pp. 297–312, 2011. View at Publisher · View at Google Scholar · View at Scopus
  2. A. K. Moorthy and A. C. Bovik, “A survey on 3D quality of experience and 3D quality assessment,” in 18th Human Vision and Electronic Imaging, vol. 8651 of Proceedings of SPIE, Burlingame, Calif, USA, February 2013. View at Publisher · View at Google Scholar
  3. R. Vlad, P. Ladret, and A. Guérin, “Three factors that influence the overall quality of the stereoscopic 3D content: image quality, comfort, and realism,” in Proceedings of the 18th Human Vision and Electronic Imaging (SPIE '13), vol. 8651, San Jose, CA, USA, February 2013. View at Publisher · View at Google Scholar · View at Scopus
  4. I. P. Howard and J. B. Rogers, Binocular Fusion and Rivalry in Binocular Vision and Stereopsis, Oxford University Press, New York, NY, USA, 1995.
  5. 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. View at Publisher · View at Google Scholar · View at Scopus
  6. Z. Wang, E. P. Simoncelli, and A. C. Bovik, “Multi-scale structural similarity for image quality assessment,” in Proceedings of the 37th Asilomar Conference on Signals, Systems and Computers, vol. 2, pp. 1398–1402, November 2003. View at Publisher · View at Google Scholar · View at Scopus
  7. Z. Wang and A. C. Bovik, “A universal image quality index,” IEEE Signal Processing Letters, vol. 9, no. 3, pp. 81–84, 2002. View at Publisher · View at Google Scholar · View at Scopus
  8. G. H. Chen, C. L. Yang, and S. L. Xie, “Gradient-based structural similarity for image quality assessment,” in Proceedings of the IEEE International Conference on Image Processing (ICIP '06), pp. 2929–2932, Atlanta, Ga, USA, October 2006. View at Publisher · View at Google Scholar · View at Scopus
  9. A. Liu, W. Lin, and M. Narwaria, “Image quality assessment based on gradient similarity,” IEEE Transactions on Image Processing, vol. 21, no. 4, pp. 1500–1512, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  10. J. Zhu and N. Wang, “Image quality assessment by visual gradient similarity,” IEEE Transactions on Image Processing, vol. 21, no. 3, pp. 919–933, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  11. W. Xue, L. Zhang, X. Mou, and A. C. Bovik, “Gradient magnitude similarity deviation: a highly efficient perceptual image quality index,” IEEE Transactions on Image Processing, vol. 23, no. 2, pp. 684–695, 2014. View at Publisher · View at Google Scholar · View at MathSciNet
  12. A. Boev, A. Gotchev, K. Egiazarian, A. Aksay, and G. B. Akar, “Towards compound stereo-video quality metric: a specific encoder-based framework,” in Proceedings of the 7th IEEE Southwest Symposium on Image Analysis and Interpretation, pp. 218–222, IEEE, Denver, Colorado, March 2006. View at Scopus
  13. P. Campisi, A. Benoit, P. Le Callet, and R. Cousseau, “Quality assessment of stereoscopic images,” EURASIP Journal on Image and Video Processing, vol. 2008, Article ID 659024, 2008. View at Publisher · View at Google Scholar · View at Scopus
  14. J. You, L. Xing, A. Perkis, and X. Wang, “Perceptual quality assessment for stereoscopic images based on 2D image quality metrics and disparity analysis,” in Proceedings of the International Workshop on Video Processing and Quality Metrics for Consumer Electronics, Scottsdale, Ariz, USA, 2010.
  15. C. T. E. R. Hewage, S. T. Worrall, S. Dogan, and A. M. Kondoz, “Prediction of stereoscopic video quality using objective quality models of 2-D video,” Electronics Letters, vol. 44, no. 16, pp. 963–965, 2008. View at Publisher · View at Google Scholar · View at Scopus
  16. J. J. Hwang and H. R. Wu, “Stereo image quality assessment using visual attention and distortion predictors,” KSII Transactions on Internet and Information Systems, vol. 5, no. 9, pp. 1613–1631, 2011. View at Publisher · View at Google Scholar · View at Scopus
  17. R. Bensalma and M. Larabi, “A perceptual metric for stereoscopic image quality assessment based on the binocular energy,” Multidimensional Systems and Signal Processing, vol. 24, no. 2, pp. 281–316, 2013. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  18. M.-J. Chen, C.-C. Su, D.-K. Kwon, L. K. Cormack, and A. C. Bovik, “Full-reference quality assessment of stereopairs accounting for rivalry,” Signal Processing: Image Communication, vol. 228, no. 9, pp. 1143–1155, 2013. View at Publisher · View at Google Scholar · View at Scopus
  19. V. de Silva, H. Kodikara Arachchi, and A. Kondoz, “Toward an impairment metric for stereoscopic video: a full-reference video quality metric to assess compressed stereoscopic video,” IEEE Transactions on Image Processing, vol. 22, no. 9, pp. 3392–3404, 2013. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  20. F. Shao, W. Lin, S. Gu, and G. Jiang, “Perceptual full-reference quality assessment of stereoscopic images by considering binocular visual characteristics,” IEEE Transactions on Image Processing, vol. 22, no. 5, pp. 1940–1953, 2013. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  21. S. Ryu, D. H. Kim, and K. Sohn, “Stereoscopic image quality metric based on binocular perception model,” in Proceedings of the 19th IEEE International Conference on Image Processing (ICIP '12), pp. 609–612, Orlando, Florida, October 2012. View at Publisher · View at Google Scholar · View at Scopus
  22. W. Hachicha, A. Beghdadi, and F. A. Cheikh, “Stereo image quality assessment using a binocular just noticeable difference model,” in Proceedings of the International Conference on Image Processing, Melbourne, Australia, September 2013.
  23. F. Qi, T. Jiang, X. Fan, S. Ma, and D. Zhao, “Stereoscopic video quality assessment based on stereo just-noticeable difference model,” in Proceedings of the 20th IEEE International Conference on Image Processing (ICIP '13), pp. 34–38, Melbourne, Australia, September 2013. View at Publisher · View at Google Scholar
  24. H. Ko, C.-S. Kim, S. Y. Choi, and C.-C. Jay Kuo, “3D image quality index using SDP-based binocular perception model,” in Proceedings of the IEEE 11th IVMSP Workshop (Image, Video, and Multidimensional Signal Processing Technical Committee), pp. 1–4, IEEE, Seoul, South Korea, June 2013. View at Publisher · View at Google Scholar
  25. R. Blake and H. Wilson, “Binocular vision,” Vision Research, vol. 51, no. 7, pp. 754–770, 2011. View at Publisher · View at Google Scholar · View at Scopus
  26. D. J. Fleet, H. Wagner, and D. J. Heeger, “Neural encoding of binocular disparity: energy models, position shifts and phase shifts,” Vision Research, vol. 36, no. 12, pp. 1839–1857, 1996. View at Publisher · View at Google Scholar · View at Scopus
  27. F. da Faria, J. Batosta, and H. Araujo, “Stereoscopic depth perception using a model based on the primary visual cortex,” PloS One, vol. 8, no. 12, Article ID e80745, 2013. View at Google Scholar
  28. W. Stürzl, U. Hoffmann, and H. A. Mallot, “vergence control and disparity estimation with energy neurons: theory and implementation,” in Proceedings of the International Conference on Artificial Neural Networks, pp. 1255–1260, 2002.
  29. V. Kolmogorov and R. Zabih, “Computing visual correspondence with occlusions using graph cuts,” in Proceedings of the 8th International Conference on Computer Vision (ICCV '01), vol. 2, pp. 508–515, Vancouver, Canada, July 2001. View at Publisher · View at Google Scholar · View at Scopus
  30. R. Szeliski and D. Scharstein, “Sampling the Disparity Space Image,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 3, pp. 419–425, 2004. View at Publisher · View at Google Scholar · View at Scopus
  31. Y. Li, J. Zhao, J. Yin, and X. Zhao, “A fast simple optical flow computation approach based on 3D gradient,” Transactions on Circuits and Systems for Video Technology, vol. 24, no. 5, pp. 842–853, 2014. View at Google Scholar
  32. M.-J. Chen, L. K. Cormack, and A. C. Bovik, “No-reference quality assessment of natural stereopairs,” IEEE Transactions on Image Processing, vol. 22, no. 9, pp. 3379–3391, 2013. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  33. P. G. Gottschalk and J. R. Dunn, “The five-parameter logistic: a characterization and comparison with the four-parameter logistic,” Analytical Biochemistry, vol. 343, no. 1, pp. 54–65, 2005. View at Publisher · View at Google Scholar · View at Scopus