Advertisement

SMIM: Superpixel Mutual Information Measurement for Image Quality Assessment

  • Jiaming Wang
  • Tao Lu
  • Yanduo Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11335)

Abstract

The image quality assessment (IQA) is a fundamental problem in signal processing that aims to measure the objective quality of an image by designing a mathematical model. Most full-reference (FR) IQA methods use fixed sliding windows to obtain structure information but ignore the variable spatial configuration information. In this paper, we propose a novel full-reference IQA method, named “superpixel normalized mutual information (SMIM)” based on the perspective of variable receptive field and information entropy. First, we find that consistence relationship exists between the information fidelity and human visual of individuals. Thus, we reproduce the human visual system (HVS) to semantically divide the image into multiple patches via superpixel segmentation. Then the weights of each image patches are adaptively calculated via its information volume. We verified the effectiveness of SMIM by applying it to data from the TID2008 database and data generated using some real application scenarios. Experiments show that SMIM outperforms some state-of-the-art FR IQA algorithms, including visual information fidelity (VIF).

Keywords

Image quality assessment Mutual information Superpixel segmentation 

References

  1. 1.
    Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S.: Slic superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012)CrossRefGoogle Scholar
  2. 2.
    Chandler, D.M., Hemami, S.S.: VSNR: a wavelet-based visual signal-to-noise ratio for natural images. IEEE Trans. Image Process. 16(9), 2284–2298 (2007)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Damera-Venkata, N., Kite, T.D., Geisler, W.S., Evans, B.L., Bovik, A.C.: Image quality assessment based on a degradation model. IEEE Trans. Image Process. 9(4), 636–650 (2000)CrossRefGoogle Scholar
  4. 4.
    Flannery, B.P., Flannery, B.P., Teukolsky, S.A., Vetterling, W.T.: Numerical Recipes: The Art of Scientific Computing. Cambridge University Press, New York (1986)zbMATHGoogle Scholar
  5. 5.
    Kim, J., Lee, J.K., Lee, K.M.: Accurate image super-resolution using very deep convolutional networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR Oral), June 2016Google Scholar
  6. 6.
    Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 105–114, July 2017.  https://doi.org/10.1109/CVPR.2017.19
  7. 7.
    Li, J., Zhang, X., Ding, M.: Image quality assessment based on regional mutual information. AEUE - Int. J. Electron. C. 66(9), 784–787 (2012)CrossRefGoogle Scholar
  8. 8.
    Maes, F., Collignon, A., Vandermeulen, D., Marchal, G., Suetens, P.: Multimodality image registration by maximization of mutual information. IEEE Trans. Med. Imaging 16(2), 187–198 (1997)CrossRefGoogle Scholar
  9. 9.
    Ponomarenko, N., Lukin, V., Zelensky, A., Egiazarian, K., Carli, M., Battisti, F.: TID 2008 - a database for evaluation of full-reference visual quality assessment metrics. Adv. Modern Radioelectron. 10, 30–45 (2004)Google Scholar
  10. 10.
    Sheikh, H.R., Bovik, A.C.: Image information and visual quality. IEEE Trans. Image Process. 15(2), 430–444 (2006). A Publication of the IEEE Signal Processing SocietyGoogle Scholar
  11. 11.
    Sheikh, H.R., Bovik, A.C., Veciana, G.D.: An information fidelity criterion for image quality assessment using natural scene statistics. IEEE Trans. Image Process. 14(12), 2117–2128 (2005)CrossRefGoogle Scholar
  12. 12.
    Sheikh, H.R., Sabir, M.F., Bovik, A.C.: A statistical evaluation of recent full reference image quality assessment algorithms. IEEE Trans. Image Process. 15(11), 3440–3451 (2006)CrossRefGoogle Scholar
  13. 13.
    Thomaz, C.E., Giraldi, G.A.: A new ranking method for principal components analysis and its application to face image analysis. Image Vis. Comput. 28(6), 902–913 (2010).  https://doi.org/10.1016/j.imavis.2009.11.005CrossRefGoogle Scholar
  14. 14.
    Wang, Z., Simoncelli, E.P., Bovik, A.C.: Multiscale structural similarity for image quality assessment. In: Conference Record of the Thirty-Seventh Asilomar Conference on Signals, Systems and Computers 2004, vol. 2, pp. 1398–1402 (2004)Google Scholar
  15. 15.
    Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)CrossRefGoogle Scholar
  16. 16.
    Wang, Z., Sheikh, H.R., Bovik, A.C., et al.: Objective video quality assessment. Handb. Video Databases Des. Appl. 41, 1041–1078 (2003)Google Scholar
  17. 17.
    Zhang, L., Zhang, L., Mou, X., Zhang, D.: Fsim: A feature similarity index for image quality assessment. IEEE Trans. Image Process. 20(8), 2378–2386 (2011)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Hubei Key Laboratory of Intelligent Robot, School of Computer Science and EngineeringWuhan Institute of TechnologyWuhanChina

Personalised recommendations