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Parameters optimization of the Structural Similarity Index
  DOI :  10.2352/issn.2694-118X.2020.LIM-13  Published OnlineSeptember 2020
Abstract

We exploit evolutionary computation to optimize the handcrafted Structural Similarity method (SSIM) through a datadriven approach. We estimate the best combination of luminance, contrast and structure components, as well as the sliding window size used for processing, with the objective of optimizing the similarity correlation with human-expressed mean opinion score on a standard dataset. We experimentally observe that better results can be obtained by penalizing the overall similarity only for very low levels of luminance similarity. Finally, we report a comparison of SSIM with the optimized parameters against other metrics for full reference quality assessment, showing superior performance on a different dataset.

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Illya Bakurov, Marco Buzzelli, Mauro Castelli, Raimondo Schettini, Leonardo Vanneschi, "Parameters optimization of the Structural Similarity Indexin Proc. IS&T London Imaging Meeting 2020: Future Colour Imaging,  2020,  pp 19 - 23,  https://doi.org/10.2352/issn.2694-118X.2020.LIM-13

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Copyright © Society for Imaging Science and Technology 2020
75011771
London Imaging Meeting
2694-118X
2694-118x
Society for Imaging Science and Technology