index is based on SSIM and is a commercially available tool. It extends SSIM's capabilities, mainly to target video applications. It provides scores in the range of 0–100, linearly matched to human subjective ratings. It also allows adapting the scores to the intended viewing device, comparing video across different resolutions and contents. According to its authors, SSIMPLUS achieves higher accuracy and higher speed than other image and video quality metrics. However, no independent evaluation of SSIMPLUS has been performed, as the algorithm itself is not publicly available. cSSIM In order to further investigate the standard discrete SSIM from a theoretical perspective, the continuous SSIM (cSSIM) has been introduced and studied in the context of Radial basis function interpolation. Other simple modifications The r* cross-correlation metric is based on the variance metrics of SSIM. It's defined as when , when both standard deviations are zero, and when only one is zero. It has found use in analyzing human response to contrast-detail phantoms. SSIM has also been used on the gradient of images, making it "G-SSIM". G-SSIM is especially useful on blurred images. The modifications above can be combined. For example, 4-G-r* is a combination of 4-SSIM, G-SSIM, and r*. It is able to reflect radiologist preference for images much better than other SSIM variants tested. Application SSIM has applications in a variety of different problems. Some examples are: Image Compression: In lossy image compression, information is deliberately discarded to decrease the storage space of images and video. The MSE is typically used in such compression schemes. According to its authors, using SSIM instead of MSE is suggested to produce better results for the decompressed images. Image Restoration: Image restoration focuses on solving the problem where is the blurry image that should be restored, is the blur kernel, is the additive noise and is the original image we wish to recover. The traditional filter which is used to solve this problem is the Wiener Filter. However, the Wiener filter design is based on the MSE. Using an SSIM variant, specifically Stat-SSIM, is claimed to produce better visual results, according to the algorithm's authors. Pattern Recognition: Since SSIM mimics aspects of human perception, it could be used for recognizing patterns. When faced with issues like image scaling, translation and rotation, the algorithm's authors claim that it is better to use CW-SSIM, which is insensitive to these variations and may be directly applied by