mlsanityData to 7 Oct 2026

Understanding SSIM

arXiv 2006.13846 ↗First posted 24 Jun 2020eess.IV cs.GRcited by 75

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Abstract (arXiv)

The use of the structural similarity index (SSIM) is widespread. For almost two decades, it has played a major role in image quality assessment in many different research disciplines. Clearly, its merits are indisputable in the research community. However, little deep scrutiny of this index has been performed. Contrary to popular belief, there are some interesting properties of SSIM that merit such scrutiny. In this paper, we analyze the mathematical factors of SSIM and show that it can generate results, in both synthetic and realistic use cases, that are unexpected, sometimes undefined, and nonintuitive. As a consequence, assessing image quality based on SSIM can lead to incorrect conclusions and using SSIM as a loss function for deep learning can guide neural network training in the wrong direction.

Key facts. Cited by 75 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Jun 2020.

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Citing papers per year: 2021: 6; 2022: 14; 2023: 8; 2024: 13; 2025: 20; 2026: 14.

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  11. 2024Sanity Checks Revisited: An Exploration to Repair the Model Parameter Randomisation Testcited by 9
  12. 2022Attention-Based Generative Neural Image Compression on Solar Dynamics Observatorycited by 8
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