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A comprehensive assessment of the structural similarity index

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Abstract

In recent years the structural similarity index has become an accepted standard among image quality metrics. Made up of three components, this technique assesses the visual impact of changes in image luminance, contrast, and structure. Applications of the index include image enhancement, video quality monitoring, and image encoding. As its status continues to rise, however, so do questions about its performance. In this paper, it is shown, both empirically and analytically, that the index is directly related to the conventional, and often unreliable, mean squared error. In the first evaluation, the two metrics are statistically compared with one another. Then, in the second, a pair of functions that algebraically connects the two is derived. These results suggest a much closer relationship between the structural similarity index and mean squared error.

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References

  1. Acharya T., Tsai P.S.: JPEG2000 Standard for Image Compression: Concepts, Algorithms and VLSI Architectures. John Wiley & Sons Inc., New York (2005)

    Google Scholar 

  2. Beghdadi A., Iordache R.: Image quality assessment using the joint spatial/spatial-frequency representation. EURASIP J. Appl. Signal Process. 2006, 1–8 (2006)

    Google Scholar 

  3. Channappayya, S.S., Bovik, A.C., Caramanis, C., Heath Jr., R.W.: Ssim-optimal linear image restoration. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 765–768 (2008)

  4. Chih-Che Lin, D., Chau, P.M.: Objective human visual system based video quality assessment metric for low bit-rate video communication systems. In: Proceedings of the IEEE Workshop on Multimedia Signal Processing, pp. 320–323 (2006)

  5. Cockshott, W.P., Balasuriya, S.L., Gunawan, I.P., Siebert, J.P.: Image enhancement using vector quantisation based interpolation. In: Proceedings of the British Machine Vision Conference (2007)

  6. Coskun, B., Sankur, B.: Robust video hash extraction. In: Proceedings of the IEEE Signal Processing and Communications Applications Conference, pp. 292–295 (2004)

  7. Daly, S.: Digital Images and Human Vision, chap. The Visible Differences Predictor: An Algorithm for the Assessment of Image Fidelity, pp. 179–206. MIT Press, Cambridge (1993)

  8. Dosselmann, R.: An evaluation of existing and emerging digital image and video quality metrics. Master’s thesis, University of Regina, Regina, Saskatchewan, Canada (2006)

  9. Dosselmann, R., Yang, X.D.: A prototype no-reference video quality system. In: Proceedings of the Canadian Conference on Computer and Robot Vision, pp. 411–417 (2007)

  10. Dosselmann, R., Yang, X.D.: An empirical assessment of the structural similarity index. In: Proceedings of the IEEE Canadian Conference on Electrical and Computer Engineering, pp. 112–116 (2009)

  11. Gonzalez R., Woods R.: Digital Image Processing. Prentice Hall, Upper Saddle River (2002)

    Google Scholar 

  12. Lay D.C.: Linear Algebra and Its Applications. Addison Wesley Longman, Reading (1997)

    MATH  Google Scholar 

  13. Lewis R.: Practical Digital Image Processing. Ellis Horwood, Chichester (1990)

    Google Scholar 

  14. Lubin, J.: Vision models for target detection and recognition, chap. 10. A Visual Discrimination Model for Imaging System Design and Evaluation, pp. 245–283. World Scientific, Singapore (1995)

  15. Mai, Z.Y., Yang, C.L., Kuang, K.Z., Po, L.M.: A novel motion estimation method based on structural similarity for h.264 inter prediction. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, vol. 2, pp. 913–916 (2006)

  16. Miyahara M., Kotani K., Algazi V.R.: Objective picture quality scale (pqs) for image coding. IEEE Trans. Commun. 46, 1215–1226 (1998)

    Article  Google Scholar 

  17. Reibman, A.R., Poole, D.: Characterizing packet-loss impairments in compressed video. In: Proceedings of the IEEE International Conference on Image Processing vol. 5, pp. 77–80 (2007)

  18. Rouse, D.M., Hemami, S.S.: Analyzing the role of visual structure in the recognition of natural image content with multi-scale ssim. In: Proceedngs of the SPIE Human Vision and Electronic Imaging Conference, vol. 6806 (2008)

  19. Rouse, D.M., Hemami, S.S.: Understanding and simplifying the structural similarity metric. In: Proceedings of the IEEE International Conference on Image Processing, pp. 1188–1191 (2008)

  20. Seshadrinathan, K., Bovik, A.C.: New vistas in image and video quality assessment. In: Proceedings of the SPIE Human Vision and Electronic Imaging Conference, vol. 6492 (2007)

  21. Seshadrinathan, K., Bovik, A.C.: Unifying analysis of full reference image quality assessment. In: Proceedings of the IEEE International Conference on Image Processing, pp. 1200–1203 (2008)

  22. 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)

    Article  Google Scholar 

  23. Shnayderman, A., Gusev, A., Eskicioglu, A.M.: A multidimensional image quality measure using singular value decomposition. In: Proceedings of the SPIE Image Quality and System Performance Conference, vol. 5294, pp. 82–92 (2004)

  24. Sonka M., Hlavac V., Boyle R.: Image Processing, Analysis and Machine Vision. Brooks/Cole, Belmont (1999)

    Google Scholar 

  25. Sung C.C., Ruan S.J., Lin B.Y., Shie M.C.: Quality and power efficient architecture for the discrete cosine transform. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. E88A(12), 3500–3507 (2005)

    Article  Google Scholar 

  26. Süsstrunk, S., Winkler, S.: Color image quality on the internet. In: Proceedings of the SPIE Electronic Imaging Conference on Internet Imaging, vol. 5304, pp. 118–131 (2004)

  27. Triola M.F.: Elementary Statistics. Pearson, Boston (2005)

    Google Scholar 

  28. Vorren, S.S.: Subjective quality evaluation of the effect of packet loss in high-definition video. Master’s thesis, Norwegian University of Science and Technology, Trondheim, Norway (2006)

  29. Wang Z., Bovik A.C.: A universal image quality index. IEEE Signal Process. Lett. 9(3), 81–84 (2002)

    Article  Google Scholar 

  30. Wang, Z., Bovik, A.C.: Why is image quality assessment so difficult? In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, vol. 4, pp. 3313–3316 (2002)

  31. Wang Z., Bovik A.C.: Mean squared error: love it or leave it?. IEEE Signal Process. Mag. 26(1), 98–117 (2009)

    Article  Google Scholar 

  32. 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)

    Article  Google Scholar 

  33. Wang, Z., Bovik, A.C., Simoncelli, E.P.: Handbook of Image and Video Processing, 2 edn, chap. 8.3: Structural Approaches to Image Quality Assessment, pp. 961–974. Academic Press, New York (2005)

  34. Wang, Z., Li, Q., Shang, X.: Perceptual image coding based on a maximum of minimal structural similarity criterion. In: Proceedings of the IEEE International Conference on Image Processing, vol. 2, pp. 121–124 (2007)

  35. Wang Z., Lu L., Bovik A.C.: Video quality assessment based on structural distortion measurement. Signal Process. Image Commun. 19(2), 121–132 (2004)

    Article  Google Scholar 

  36. Wang, Z., Sheikh, H.R., Bovik, A.C.: The Handbook of Video Databases: Design and Applications, chap. 41: Objective Video Quality Assessment, pp. 1041–1078. CRC Press, Boca Raton (2003)

  37. Wang, Z., Simoncelli, E.P.: Translation insensitive image similarity in complex wavelet domain. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, vol. II, pp. 573–576 (2005)

  38. Wang, Z., Simoncelli, E.P., Bovik, A.C.: Multi-scale structural similarity for image quality assessment. In: Proceedings of the IEEE Asilomar Conference on Signals, Systems and Computers, vol. 2, pp. 1398–1402 (2003)

  39. Winkler S.: Digital Video Quality: Vision Models and Metrics. John Wiley & Sons Ltd., West Sussex (2005)

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, University of Regina, 3737 Wascana Parkway, Regina, SK, S4S 0A2, Canada

    Richard Dosselmann & Xue Dong Yang

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  1. Richard Dosselmann
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  2. Xue Dong Yang
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Correspondence to Richard Dosselmann.

Additional information

This research was generously sponsored by the Natural Sciences and Engineering Research Council of Canada (NSERC). Additional resources were made available by the New Media Studio Lab (NMSL) at the University of Regina.

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Dosselmann, R., Yang, X.D. A comprehensive assessment of the structural similarity index. SIViP 5, 81–91 (2011). https://doi.org/10.1007/s11760-009-0144-1

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