GPU Based Image Quality Assessment using Structural Similarity (SSIM) Index

Abstract

This chapter deals with performance analysis of CUDA implementation of an image quality assessment tool based on structural similarity index (SSI). Since it had been initial created at the University of Texas in 2002, the Structural SIMilarity (SSIM) image assessment algorithm has become a valuable tool for still image and video processing analysis. SSIM provided a big giant over MSE (Mean Square Error) and PSNR (Peak Signal to Noise Ratio) techniques because it way more closely aligned with the results that would have been obtained with subjective testing. For objective image analysis, this new technique represents as significant advancement over SSIM as the advancement that SSIM provided over PSNR. The method is computationally intensive and this poses issues in places wherever real time quality assessment is desired. We tend to develop a CUDA implementation of this technique that offers a speedup of approximately 30 X on Nvidia GTX275 and 80 X on C2050 over Intel single core processor.

Underwater Image Translation via Multi-Scale Generative Adversarial Network

“…We utilize four metrics for quantitative evaluation, including peak signal-to-noise ratio (PSNR) [33], structure similarity index measure (SSIM) [34], information entropy (Entropy) [35], and single image Fréchet Inception distance (SIFID) [36].…”