Parameters optimization of the Structural Similarity Index

Illya Bakurov, Marco Buzzelli, Mauro Castelli, Raimondo Schettini, Leonardo Vanneschi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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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.
Original languageEnglish
Title of host publicationLondon Imaging Meeting 2020
Subtitle of host publicationFuture Colour Imaging
Pages19-23
Volume2020
Edition1
DOIs
Publication statusPublished - 29 Sep 2020

Publication series

NameLondon Imaging Meeting
ISSN (Print)2694-118X

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