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http://dx.doi.org/10.3837/tiis.2016.01.015

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)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.10, no.1, 2016 , pp. 257-271 More about this Journal
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
image quality assessment; human visual system; structural similarity index; distortion classification; color perception;
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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)   DOI
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)   DOI
4 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)   DOI
5 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)   DOI
6 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)
7 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)
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)   DOI
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)   DOI
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)   DOI
12 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)
13 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)   DOI
14 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)   DOI
15 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
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)   DOI
18 C. Cortes, and V. Vapnik, “Support vector networks,” Machine learning, vol. 20, no. 3, pp. 273-297, Sep. 1995. Article(CroosRef Link)   DOI
19 G. Wyszecki and W.S. Styles, “Color Science: Concepts and Methods, Quantitative Data and Formulae,” Wiley, New York, 1982. Article(CroosRef Link)
20 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)
21 A. Ford and A. Roberts, “Color space conversions,” Westminster University, London, pp. 1-31, 1998.
22 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)   DOI
23 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)   DOI
24 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)   DOI
25 M. G. Kendall, “A new measure of rank correlation,” Biometrika, vol. 30, pp. 81-89, 1938.   DOI
26 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)   DOI
27 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)   DOI
28 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)
29 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)   DOI