KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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Optimal Image Quality Assessment based on Distortion Classification and Color Perception

  • Lee, Jee-Yong (Department of Electrical and Computer Engineering, Ajou University) ;
  • Kim, Young-Jin (Department of Electrical and Computer Engineering, Ajou University)
  • Received : 2015.07.19
  • Accepted : 2015.10.25
  • Published : 2016.01.31
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Abstract

The Structural SIMilarity (SSIM) index is one of the most widely-used methods for perceptual image quality assessment (IQA). It is based on the principle that the human visual system (HVS) is sensitive to the overall structure of an image. However, it has been reported that indices predicted by SSIM tend to be biased depending on the type of distortion, which increases the deviation from the main regression curve. Consequently, SSIM can result in serious performance degradation. In this study, we investigate the aforementioned phenomenon from a new perspective and review a constant that plays a big role within the SSIM metric but has been overlooked thus far. Through an experimental study on the influence of this constant in evaluating images with SSIM, we are able to propose a new solution that resolves this issue. In the proposed IQA method, we first design a system to classify different types of distortion, and then match an optimal constant to each type. In addition, we supplement the proposed method by adding color perception-based structural information. For a comprehensive assessment, we compare the proposed method with 15 existing IQA methods. The experimental results show that the proposed method is more consistent with the HVS than the other methods.

Keywords

Acknowledgement

Grant : 인간 시각 만족도 기반의 AMOLED 디스플레이 대상의 전력 절감 및 화질 최적화 기술 개발

Supported by : 아주대학교

References

  1. K. Thung and R. Paramesran, "A survey of image quality measures," in Proc. of International Conference for Technical Postgraduates (TECHPOS), pp. 1-4, 2009. Article(CrossRef Link)
  2. Z. Wang and A. C. Bovik, “A universal image quality index,” IEEE Signal Processing Letter, vol. 9, no.3, pp. 81-84, Mar. 2002. Article(CrossRef Link) https://doi.org/10.1109/97.995823
  3. Z. Wang and A. C. Bovik, “Mean squared error: love it or leave it?,” IEEE Signal Processing Magazine, vol. 26, no.1, pp. 98-117, 2009. Article(CroosRef Link) https://doi.org/10.1109/MSP.2008.930649
  4. Z. Wang and A. C. Bovik, “Image quality assessment: from error visibility to structural similarity,” IEEE Transactions on Image Processing, vol. 13, no. 4, pp.600-612, 2004. Article(CroosRef Link) https://doi.org/10.1109/TIP.2003.819861
  5. H. Tang, N. Joshi, and A. Kapoor, "Learning a blind measure of perceptual image quality," in Proc. of International Computer Vision and Pattern Recognition (CVPR), pp. 305-312, Jun. 2011. Article(CroosRef Link)
  6. Y. Shi, Y. Ding, R. Zhang, and J. Li, "Structure and hue similarity for color image quality assessment," in Proc. of the International Conference on Electronic Computer Technology (ICECT), pp. 329-333, Feb. 2009. Article(CroosRef Link)
  7. M.A. Webster, “Human colour perception and its adaptation,” Network: Computation in Neural Systems, vol. 7, no. 4, pp. 587-634, Nov. 1996. Article(CroosRef Link) https://doi.org/10.1088/0954-898X_7_4_002
  8. Z. Wang, E. P. Simoncelli and A. C. Bovik, "Multi-scale structural similarity for image quality assessment," in Proc. of IEEE Asilomar Conference on Signals, Systems and Computers, pp. 1398-1402, 2003. Article(CroosRef Link)
  9. 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, May. 2011. Article(CroosRef Link) https://doi.org/10.1109/TIP.2010.2092435
  10. 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, Apr. 2012. Article(CroosRef Link) https://doi.org/10.1109/TIP.2011.2175935
  11. L. Zhang, D. Zhang, X. Mou and D. Zhang, “FSIM: A feature similarity index for image quality assessment,” IEEE Transactions on Image Processing, vol. 20, no. 8, pp. 2378-2386, Aug. 2011. Article(CroosRef Link) https://doi.org/10.1109/TIP.2011.2109730
  12. Z. Li, J. Liu, J. Tang, and H. Lu, “Robust structured subspace learning for data representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Feb. 2015, doi:// 10.1109/TPAMI.2015.2400461. Article(CroosRef Link)
  13. Z. Li, J. Liu, Y. Yang, X. Zhou, and H. Lu, “Clustering-guided sparse structural learning for unsupervised feature selection,” IEEE Transactions on Knowledge and Data Engineering, vol.26, no. 9, pp. 2138-2150, Sep. 2014. Article(CroosRef Link) https://doi.org/10.1109/TKDE.2013.65
  14. L. C. H. R. Sheikh, Z. Wang, and A. C. Bovik, "Live image quality assessment database release 2," 2007. [Online] Available: http://live.ece.utexas.edu/research/quality
  15. M. C. Morrone and D.C. Burr, "Feature detection in human vision: a phase-dependent energy model," in Proc. of the Royal Society of London B, vol. 235, no. 1280, pp. 221-245, Dec. 1988. Article(CroosRef Link)
  16. P. Kovesi, “Image features from phase congruency,” Videre: Journal of Computer Vision Research, vol. 1, no. 3, pp. 1-26, 1999.
  17. 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, Dec. 1987. Article(CroosRef Link) https://doi.org/10.1364/JOSAA.4.002379
  18. C. Cortes, and V. Vapnik, “Support vector networks,” Machine learning, vol. 20, no. 3, pp. 273-297, Sep. 1995. Article(CroosRef Link) https://doi.org/10.1007/BF00994018
  19. G. Wyszecki and W.S. Styles, “Color Science: Concepts and Methods, Quantitative Data and Formulae,” Wiley, New York, 1982. Article(CroosRef Link)
  20. A. Ford and A. Roberts, “Color space conversions,” Westminster University, London, pp. 1-31, 1998.
  21. H. R. Sheikh, M. 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, Nov. 2006. Article(CroosRef Link) https://doi.org/10.1109/TIP.2006.881959
  22. C. Spearman, “The proof and measurement of association between two things,” American Journal of Psychology, vol. 15, no. 1, pp. 72-101, Jan. 1904. Article(CroosRef Link) https://doi.org/10.2307/1412159
  23. M. G. Kendall, “A new measure of rank correlation,” Biometrika, vol. 30, pp. 81-89, 1938. https://doi.org/10.1093/biomet/30.1-2.81
  24. N. Damera-Venkata, T.D. Kite, W.S. Geisler, B.L. Evans, and A.C. Bovik, “Image quality assessment based on degradation model,” IEEE Transactions on Image Processing, vol. 9, no. 4, pp.636-650, 2000. Article(CroosRef Link) https://doi.org/10.1109/83.841940
  25. H.R. Sheikh, A.C. Bovik, and G. de Veciana, “An information fidelity criterion for image quality assessment using natural scene statistics,” IEEE Transactions on Image Processing, vol. 14, no. 12, pp. 2117-2128, 2005. Article(CroosRef Link) https://doi.org/10.1109/TIP.2005.859389
  26. D.M. Chandler and S.S. Hemami, “VSNR: A wavelet-based visual signal-to-noise-ratio for natural images,” IEEE Transactions on Image Processing, vol. 16, pp.2284-2298, 2007. Article(CroosRef Link) https://doi.org/10.1109/TIP.2007.901820
  27. L. Zhang, L. Zhang, and X. Mou, "RFSIM: a feature based image quality assessment metric using Riesz transforms," in Proc. of IEEE International Conference on Image Processing (ICIP), pp. 321-324, Sep. 2010. Article(CroosRef Link)
  28. H. R. Sheikh and A. C. Bovik, “Image information and visual quality,” IEEE Transactions on Image Processing, vol. 15, no. 2, pp. 430-444, Feb. 2007. Article(CroosRef Link) https://doi.org/10.1109/TIP.2005.859378
  29. 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. Article(CroosRef Link)

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