Image similarity/distance measures: what is really behind MSE and SSIM?

References

• Alberga, V., 2009. Similarity measures of remotely sensed multi-sensor images for change detection applications. Remote Sensing, 1, 122–143. doi:10.3390/rs1030122 ​
   Web of Science ®Google Scholar
• Blasch, E., et al., 2008. Image quality assessment for performance evaluation of image fusion. In: Proceedings of international conference on information fusion, June 2008. Cologne, 1–6. Piscataway, NJ: IEEE. ​
   Google Scholar
• Brunet, D., Vrscay, E.R., and Wang, Z., 2012. On the mathematical properties of the structural similarity index. IEEE Transactions on Image Processing, 21, 1488–1499. doi:10.1109/TIP.2011.2173206
   PubMed Web of Science ®Google Scholar
• Cha, S.-H., 2007. Comprehensive survey on distance/similarity measures between probability density functions. International Journal of Mathematical Models and Methods in Applied Sciences, 1, 300–307.
   Google Scholar
• Dice, L.R., 1945. Measures of the amount of ecologic association between species. Ecology, 26, 297–302. doi:10.2307/1932409
   Web of Science ®Google Scholar
• Dosselmann, R. and Yang, X.D., 2011. A comprehensive assessment of the structural similarity index. SIViP, 5, 81–91. doi:10.1007/s11760-009-0144-1
   Google Scholar
• Fukunaga, K., 1972. Introduction to statistical pattern recognition. New York, NY: Academic Press.
   Google Scholar
• George, A.G. and Prabavathy, A.K., 2013. A survey on different approaches used in image quality assessment. International Journal of Emerging Technology and Advanced Engineering, 3, 197–203.
   Google Scholar
• Goshtasby, A.A., 2012. Similarity and dissimilarity measures. In: A.A. Goshtasby, ed. Image registration: principles, tools and methods. London: Springer, 27–66.
   Google Scholar
• Hanhart, P., et al., 2015. Benchmarking of objective quality metrics for HDR image quality assessment. EURASIP Journal on Image and Video Processing, 39, 1–18.
   Google Scholar
• Hodgkin, E.E. and Richards, W.G., 1987. Molecular similarity based on electrostatic potential and electric field. International Journal of Quantum Chemistry, 32, 105–110. doi:10.1002/(ISSN)1097-461X
   Google Scholar
• Horé, A. and Ziou, D., 2013. Is there a relationship between peak-signal-to-noise ratio and structural similarity index measure? IET Image Processing, 7, 12–24. doi:10.1049/iet-ipr.2012.0489
   Web of Science ®Google Scholar
• Jain, A.K. and Dubes, R.C., 1988. Algorithms for clustering data. Englewood Cliffs, NJ: Prentice Hall.
   Google Scholar
• Li, S., Li, Z., and Gong, J., 2010. Multivariate statistical analysis of measures for assessing the quality of image fusion. International Journal of Image and Data Fusion, 1 (1), 47–66. doi:10.1080/19479830903562009
   Google Scholar
• Liu, Z. and Wu, W., 2011. The use of the contrast sensitivity function in the perceptual quality assessment of fused image. International Journal of Image and Data Fusion, 2 (1), 93–103. doi:10.1080/19479832.2010.523440
   Google Scholar
• Mo, R., Ye, C., and Whitfield, P.H., 2014. Application potential of four nontraditional similarity metrics in hydrometeorology. Journal of Hydrometeorology, 15, 1862–1880. doi:10.1175/JHM-D-13-0140.1
   Web of Science ®Google Scholar
• Mohammadi, P., Ebrahimi-Moghadam, A., and Shirani, S., 2015. Subjective and objective quality assessment of image: a survey. Majlesi Journal of Electrical Engineering, 9, 55–83.
   Google Scholar
• Palubinskas, G., 2013. Fast, simple and good pan-sharpening method. Journal of Applied Remote Sensing, 7, 1–12. doi:10.1117/1.JRS.7.073526
   Web of Science ®Google Scholar
• Palubinskas, G., 2014. Mystery behind similarity measures MSE and SSIM. In: Proceedings of international conference on image processing, October 2014. Paris: IEEE, 575–579.
   Google Scholar
• Palubinskas, G., 2015. Joint quality measure for evaluation of pansharpening accuracy. Remote Sensing, 7, 9292–9310. doi:10.3390/rs70709292
   Web of Science ®Google Scholar
• Palubinskas, G., 2016. Model-based view at multi-resolution image fusion methods and quality assessment measures. International Journal of Image and Data Fusion, 7 (3), 203–218. doi:10.1080/19479832.2016.1180326
   Web of Science ®Google Scholar
• Richards, J.A. and Jia, X., 1999. Remote sensing digital image analysis. 3rd ed. Berlin: Springer.
   Google Scholar
• Sampat, M.P., et al., 2009. Complex wavelet structural similarity: a new image similarity index. IEEE Transactions on Image Processing, 18, 2385–2401. doi:10.1109/TIP.2009.2025923
   PubMed Web of Science ®Google Scholar
• Sørensen, T., 1948. A method of establishing groups of equal amplitude in plant sociology based on similarity of species and its application to analyses of the vegetation on Danish commons. Kongelige Danske Videnskabernes Selskab, 5, 1–34.
   Google Scholar
• Wang, Z., et al., 2004. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13, 600–612. doi:10.1109/TIP.2003.819861
   PubMed Web of Science ®Google Scholar
• Wang, Z. and Bovik, A.C., 2002. A universal image quality index. IEEE Signal Processing Letters, 9, 81–84. doi:10.1109/97.995823
   Web of Science ®Google Scholar
• Wang, Z. and Bovik, A.C., 2009. Mean squared error: love it or leave it? A new look at signal fidelity measures. IEEE Signal Processing Magazine, 26, 98–117. doi:10.1109/MSP.2008.930649
   Web of Science ®Google Scholar
• Ward, M.N. and Folland, C.K., 1991. Prediction of seasonal rainfall in the north Nordeste of Brasil using eigenvectors of sea-surface temperature. International Journal of Climatology, 11, 711–743. doi:10.1002/joc.3370110703
   Web of Science ®Google Scholar