Improving Measurement Bias of Structural Similarity Index (SSIM) using Absolute Difference Equation

Muhammad Irfan Jaafar, Sophan Wahyudi Nawawi, Ruzairi Abdul Rahim


Structural similarity index (SSIM) is a framework for assessing the perceptual quality from an image using the degrading and structural information of an image. It is an alternative method for quantifying visual image quality since subjective evaluation by humans, in practice, is too inconvenient, time-consuming and expensive. SSIM has been widely used for almost two decades in different research disciplines in quality assessment. However, there is a deficiency in some of the mathematical components in the SSIM that may lead to incorrect, impractical and unrealistic results. In this paper, we address the problems with the SSIM and propose replacing the luminance and contrast component with absolute difference equations that obeys Weber’s law of human perception of change. We compare the results of both SSIM and new proposed SSIM in the luminance, contrast and human perception test. The proposed SSIM seem to be more human-like in determining image quality compared to the previous version of SSIM.


Contrast; Luminance; Structural similarity index (SSIM); Structure; Weber-Fechner law.

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

Z. Wang, E. Simoncelli and A. Bovik, Multiscale structural similarity for image quality assessment, The 37th Asilomar Conference on Signals, Systems & Computers, California, USA, 2003.

A. Bovik, Content-weighted video quality assessment using a three-component image model, Journal of Electronic Imaging, 19(1), 2010, 011003.

Zhou Wang and A. Bovik, Mean squared error: Love it or leave it? A new look at signal fidelity measures, IEEE Signal Processing Magazine, 26(1), 2009, 98-117.

F. Yan and C. Min, An improved method of SSIM based on visual regions of interest, 2015 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), Ningbo, China, 2015.

R. Hassen and E. Steinbach, HSSIM: An objective haptic quality assessment measure for force-feedback signals, Tenth International Conference on Quality of Multimedia Experience (QoMEX), Cagliari, Italy, 2018.

M. Aljanabi, Z. Hussain, N. Shnain and S. Lu, Design of a hybrid measure for image similarity: a statistical, algebraic, and information-theoretic approach, European Journal of Remote Sensing, 52(4), 2019, 2-15.

E. Kandel, J. H. Schwartz and T. Jessell, Principles of Neural Science, Fifth edition, New York: McGraw-Hill Education, 2013.

Statistics How To, Correlation Coefficient: Simple Definition, Formula, Easy Steps, 2021, (Accessed 31.12.2021).

J. Terrace, M. McGreal and W. Morgenstern, A Python module for computing the Structural Similarity Image Metric (SSIM), GitHub, 2021. (Accessed 31.12.2021).

A. Khalel and S. Puranik, All Image Quality Metrics You Need in One Package, GitHub, 2021, (Accessed 31.12.2021).

J. Nilsson and T. Akenine-Möller, Understanding SSIM, 2020. ArXiv, abs/2006.13846.

H. R. Sheikh, Z. Wang, A. C. Bovik and L. K. Cormack, Laboratory for Image and Video Engineering, The University of Texas at Austin, 2021, (Accessed 31.12.2021).


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