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Journal of Engineering
Volume 2017 (2017), Article ID 4752378, 11 pages
https://doi.org/10.1155/2017/4752378
Research Article

Hierarchical Feature Extraction Assisted with Visual Saliency for Image Quality Assessment

College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China

Correspondence should be addressed to Yong Ding

Received 14 June 2017; Revised 11 August 2017; Accepted 20 August 2017; Published 3 October 2017

Academic Editor: Shang-Hong Lai

Copyright © 2017 Ruizhe Deng 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. J. Wu, W. Lin, G. Shi, L. Li, and Y. Fang, “Orientation selectivity based visual pattern for reduced-reference image quality assessment,” Information Sciences, vol. 351, pp. 18–29, 2016. View at Publisher · View at Google Scholar · View at Scopus
  2. E. C. Larson and D. M. Chandler, “Most apparent distortion: full-reference image quality assessment and the role of strategy,” Journal of Electronic Imaging, vol. 19, no. 1, pp. 1–21, 2010. View at Publisher · View at Google Scholar · View at Scopus
  3. A. Mittal, A. K. Moorthy, and A. C. Bovik, “No-reference image quality assessment in the spatial domain,” IEEE Transactions on Image Processing, vol. 21, no. 12, pp. 4695–4708, 2012. View at Publisher · View at Google Scholar · View at MathSciNet
  4. Y. Zhang and D. M. Chandler, “No-reference image quality assessment based on log-derivative statistics of natural scenes,” Journal of Electronic Imaging, vol. 22, no. 4, pp. 1–23, 2013. View at Publisher · View at Google Scholar · View at Scopus
  5. N. Damera-Venkata, T. D. Kite, W. S. Geisler, B. L. Evans, and A. C. Bovik, “Image quality assessment based on a degradation model,” IEEE Transactions on Image Processing, vol. 9, no. 4, pp. 636–650, 2000. View at Publisher · View at Google Scholar · View at Scopus
  6. 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
  7. 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, pp. 1398–1402, Pacific Grove, Calif, USA, November 2003. View at Scopus
  8. Z. Wang and Q. Li, “Information content weighting for perceptual image quality assessment,” IEEE Transactions on Image Processing, vol. 20, no. 5, pp. 1185–1198, 2011. View at Publisher · View at Google Scholar · View at MathSciNet
  9. H. R. Sheikh and A. C. Bovik, “Image information and visual quality,” IEEE Transactions on Image Processing, vol. 15, no. 2, pp. 430–444, 2006. View at Publisher · View at Google Scholar · View at Scopus
  10. H. Wang, J. Fu, W. Lin, S. Hu, C. J. Kuo, and L. Zuo, “Image quality assessment based on local linear information and distortion-specific compensation,” IEEE Transactions on Image Processing, vol. 26, no. 2, pp. 915–926, 2017. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  11. T. Wang, L. Zhang, H. Jia, B. Li, and H. Shu, “Multiscale contrast similarity deviation: An effective and efficient index for perceptual image quality assessment,” Signal Processing: Image Communication, vol. 45, pp. 1–9, 2016. View at Publisher · View at Google Scholar · View at Scopus
  12. B. A. Olshausen and D. J. Field, “How close are we to understanding V1?” Neural Computation, vol. 17, no. 8, pp. 1665–1699, 2005. View at Publisher · View at Google Scholar · View at Scopus
  13. N. Kruger, P. Janssen, S. Kalkan et al., “Deep hierarchies in the primate visual cortex: what can we learn for computer vision?” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 8, pp. 1847–1871, 2013. View at Publisher · View at Google Scholar · View at Scopus
  14. J. Li, L.-Y. Duan, X. Chen, T. Huang, and Y. Tian, “Finding the secret of image saliency in the frequency domain,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, no. 12, pp. 2428–2440, 2015. View at Publisher · View at Google Scholar · View at Scopus
  15. L. Zhang, Y. Shen, and H. Li, “VSI: a visual saliency-induced index for perceptual image quality assessment,” IEEE Transactions on Image Processing, vol. 23, no. 10, pp. 4270–4281, 2014. View at Publisher · View at Google Scholar · View at MathSciNet
  16. J. Y. Lin, T. J. Liu, W. Lin, and C.-C. J. Kuo, “Visual-saliency-enhanced image quality assessment indices,” in Proceedings of the 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013, pp. 1–4, Kaohsiung, Taiwan, November 2013. View at Publisher · View at Google Scholar · View at Scopus
  17. X. D. Hou and L. Q. Zhang, “Saliency detection: a spectral residual approach,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '07), pp. 2280–2287, Minneapolis, MN, USA, June 2007. View at Publisher · View at Google Scholar · View at Scopus
  18. H. R. Sheikh, M. F. Sabir, and A. C. Bovik, “A statistical evaluation of recent full reference image quality assessment algorithms,” IEEE Transactions on Image Processing, vol. 15, no. 11, pp. 3440–3451, 2006. View at Publisher · View at Google Scholar · View at Scopus
  19. E. Walia and V. Verma, “Boosting local texture descriptors with Log-Gabor filters response for improved image retrieval,” International Journal of Multimedia Information Retrieval, vol. 5, no. 3, pp. 173–184, 2016. View at Publisher · View at Google Scholar · View at Scopus
  20. D. J. Field, “Relations between the statistics of natural images and the response properties of cortical cells,” Journal of the Optical Society of America A, vol. 4, no. 12, pp. 2379–2394, 1987. View at Publisher · View at Google Scholar
  21. S. Murala, R. P. Maheshwari, and R. Balasubramanian, “Local tetra patterns: a new feature descriptor for content-based image retrieval,” IEEE Transactions on Image Processing, vol. 21, no. 5, pp. 2874–2886, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  22. C. Li, A. C. Bovik, and X. Wu, “Blind image quality assessment using a general regression neural network,” IEEE Transactions on Neural Networks, vol. 22, no. 5, pp. 793–799, 2011. View at Publisher · View at Google Scholar · View at Scopus
  23. X. Gao, F. Gao, D. Tao, and X. Li, “Universal blind image quality assessment metrics via natural scene statistics and multiple kernel learning,” IEEE Transactions on Neural Networks and Learning Systems, vol. 24, no. 12, pp. 2013–2026, 2013. View at Publisher · View at Google Scholar · View at Scopus
  24. A. J. Smola and B. Scholkopf, “A tutorial on support vector regression,” Statistics and Computing, vol. 14, no. 3, pp. 199–222, 2004. View at Publisher · View at Google Scholar · View at MathSciNet
  25. C. Chang and C. Lin, “LIBSVM: a Library for support vector machines,” ACM Transactions on Intelligent Systems and Technology, vol. 2, no. 3, pp. 27:1–27:27, 2011. View at Publisher · View at Google Scholar · View at Scopus
  26. N. Ponomarenko, V. Lukin, A. Zelensky, K. Egiazarian, M. Carli, and F. Battisti, “TID2008 - a database for evaluation of full-reference visual quality assessment metrics,” Advances of Modern Radioelectronics, vol. 10, pp. 30–45, 2008. View at Google Scholar
  27. N. Ponomarenko, L. Jin, O. Ieremeiev et al., “Image database TID2013: peculiarities, results and perspectives,” Signal Processing: Image Communication, vol. 30, pp. 57–77, 2015. View at Publisher · View at Google Scholar · View at Scopus
  28. 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
  29. H.-W. Chang, Q.-W. Zhang, Q.-G. Wu, and Y. Gan, “Perceptual image quality assessment by independent feature detector,” Neurocomputing, vol. 151, no. 3, pp. 1142–1152, 2015. View at Publisher · View at Google Scholar · View at Scopus
  30. M. Oszust, “Full-reference image quality assessment with linear combination of genetically selected quality measures,” PLoS ONE, vol. 11, no. 6, article e0158333, 2016. View at Publisher · View at Google Scholar · View at Scopus
  31. A. Saha and Q. M. J. Wu, “Full-reference image quality assessment by combining global and local distortion measures,” Signal Processing, vol. 128, pp. 186–197, 2016. View at Publisher · View at Google Scholar · View at Scopus