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A Novel Spatial Pooling Strategy for Image Quality Assessment

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Abstract

A variety of existing image quality assessment (IQA) metrics share a similar two-stage framework: at the first stage, a quality map is constructed by comparison between local regions of reference and distorted images; at the second stage, the spatial pooling is adopted to obtain overall quality score. In this work, we propose a novel spatial pooling strategy for image quality assessment through statistical analysis of the quality map. Our in-depth analysis indicates that the overall image quality is sensitive to the quality distribution. Based on the analysis, the quality histogram and statistical descriptors extracted from the quality map are used as input to the support vector regression to obtain the final objective quality score. Experimental results on three large public IQA databases have demonstrated that the proposed spatial pooling strategy can greatly improve the quality prediction performance of the original IQA metrics in terms of correlation with human subjective ratings.

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References

  1. Lin W, Jay Kuo C C. Perceptual visual quality metrics: A survey. Journal of Visual Communication and Image Representation, 2011, 22(4): 297–312.

    Article  Google Scholar 

  2. Daly S J. Visible differences predictor: An algorithm for the assessment of image fidelity. In Proc. SPIE/IS&T 1992 Symposium on Electronic Imaging: Science and Technology, May 1992, pp.2-15.

  3. Lubin J. A human vision system model for objective picture quality measurements. In Proc. the 1997 International Broadcasting Convention, Sept. 1997, pp.498-503.

  4. Wang Z, Bovik A C, Sheikh H R, Simoncelli E P. Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 2004, 13(4): 600–612.

    Article  Google Scholar 

  5. Sheikh H R, Bovik A C, de Veciana G. An information fidelity criterion for image quality assessment using natural scene statistics. IEEE Transactions on Image Processing, 2005, 14(12): 2117–2128.

    Article  Google Scholar 

  6. Sheikh H R, Bovik A C. Image information and visual quality. IEEE Transactions on Image Processing, 2006, 15(2): 430–444.

    Article  Google Scholar 

  7. Larson E C, Chandler D M. Most apparent distortion: Fullreference image quality assessment and the role of strategy. Journal of Electronic Imaging, 2010, 19(1): 011006.

    Article  Google Scholar 

  8. Zhang L, Zhang D, Xuanqin M. FSIM: A feature similarity index for image quality assessment. IEEE Transactions on Image Processing, 2011, 20(8): 2378–2386.

    Article  MathSciNet  Google Scholar 

  9. Xue W, Zhang L, Mou X, Bovik A C. Gradient magnitude similarity deviation: A highly efficient perceptual image quality index. IEEE Transactions on Image Processing, 2014, 23(2): 684–695.

    Article  MathSciNet  Google Scholar 

  10. Wang Z. Applications of objective image quality assessment methods [applications corner]. IEEE Signal Processing Magazine, 2011, 28(6): 137–142.

    Article  Google Scholar 

  11. Bovik A C. Automatic prediction of perceptual image and video quality. Proceedings of the IEEE, 2013, 101(9): 2008–2024.

    MathSciNet  Google Scholar 

  12. Wang Z, Li Q. Information content weighting for perceptual image quality assessment. IEEE Transactions on Image Processing, 2011, 20(5): 1185–1198.

    Article  MathSciNet  Google Scholar 

  13. Shnayderman A, Gusev A, Eskicioglu A M. An SVD-based grayscale image quality measure for local and global assessment. IEEE Transactions on Image Processing, 2006, 15(2): 422–429.

    Article  Google Scholar 

  14. Seshadrinathan K, Bovik A C. Motion tuned spatiotemporal quality assessment of natural videos. IEEE Transactions on Image Processing, 2010, 19(2): 335–350.

    Article  MathSciNet  Google Scholar 

  15. Moorthy A K, Bovik A C. Visual importance pooling for image quality assessment. IEEE Journal of Selected Topics in Signal Processing, 2009, 3(2): 193–201.

    Article  Google Scholar 

  16. Wang Z, Shang X. Spatial pooling strategies for perceptual image quality assessment. In Proc. the 2006 IEEE International Conference on Image Processing, Oct. 2006, pp. 2945–2948.

  17. Engelke U, Kaprykowsky H, Zepernick H, Ndjiki-Nya P. Visual attention in quality assessment. IEEE Signal Processing Magazine, 2011, 28(6): 50–59.

    Article  Google Scholar 

  18. Gong M, Pedersen M. Spatial pooling for measuring color printing quality attributes. Journal of Visual Communication and Image Representation, 2012, 23(5): 685–696.

    Article  Google Scholar 

  19. Schlkopf B, Smola A J. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, 2002.

  20. Sheikh H R, Wang Z, Cormack L, Bovik A C. LIVE image quality assessment database release 2, 2005. http://live.ece.utexas.edu/research/quality/, Feb. 2016.

  21. Larson E C, Chandler D. Categorical image quality (CSIQ) database, 2010. http://vision.okstate.edu/csiq, Feb. 2016.

  22. Ponomarenko N, Lukin V, Zelensky A, Egiazarian K, Carli M, Battisti F. TID2008 — a database for evaluation of full-reference visual quality assessment metrics. Advances of Modern Radioelectronics, 2009, 10(4): 30–45.

    Google Scholar 

  23. Rohaly A M, Libert J, Corriveau P, Webster A et al. Final report from the video quality experts group on the validation of objective models of video quality assessment. ITU-T Standards Contribution COM, 2000, 1: 9–80.

    Google Scholar 

  24. Chandler D M, Hemami S S. VSNR: A wavelet-based visual signal-to-noise ratio for natural images. IEEE Transactions on Image Processing, 2007, 16(9): 2284–2298.

    Article  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. School of Computer Engineering, Nanyang Technological University, Singapore, 639798, Singapore

    Qiaohong Li

  2. School of Information Technology, Jiangxi University of Finance and Economics, Nanchang, 330013, China

    Yu-Ming Fang

  3. School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, 100086, China

    Jing-Tao Xu

Authors
  1. Qiaohong Li
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  2. Yu-Ming Fang
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  3. Jing-Tao Xu
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Corresponding author

Correspondence to Yu-Ming Fang.

Additional information

Special Section on Video Coding and Assessment

This work was supported by the National Natural Science Foundation of China under Grant No. 61571212 and the Natural Science Foundation of Jiangxi Province of China under Grant No. 20151BDH80003.

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Li, Q., Fang, YM. & Xu, JT. A Novel Spatial Pooling Strategy for Image Quality Assessment. J. Comput. Sci. Technol. 31, 225–234 (2016). https://doi.org/10.1007/s11390-016-1623-9

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  • DOI: https://doi.org/10.1007/s11390-016-1623-9

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