TL;DR
This paper critically examines the various implementations of the SSIM index, a key image quality metric, and provides recommendations to improve its efficiency and reliability in perceptual quality assessment.
Contribution
It offers a comprehensive comparison of SSIM implementations and proposes guidelines to enhance their effectiveness and computational efficiency.
Findings
Identified differences in SSIM implementation performance
Provided recommendations to improve SSIM's computational efficiency
Enhanced understanding of SSIM's design choices and their impact
Abstract
The Structural Similarity (SSIM) Index is a very widely used image/video quality model that continues to play an important role in the perceptual evaluation of compression algorithms, encoding recipes and numerous other image/video processing algorithms. Several public implementations of the SSIM and Multiscale-SSIM (MS-SSIM) algorithms have been developed, which differ in efficiency and performance. This "bendable ruler" makes the process of quality assessment of encoding algorithms unreliable. To address this situation, we studied and compared the functions and performances of popular and widely used implementations of SSIM, and we also considered a variety of design choices. Based on our studies and experiments, we have arrived at a collection of recommendations on how to use SSIM most effectively, including ways to reduce its computational burden.
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