Structural similarity

The structural similarity (SSIM) index is a method for predicting the perceived quality of digital television and cinematic pictures, as well as other kinds of digital images and videos. An early variant was developed in the Laboratory for Image and Video Engineering (LIVE) at The University of Texas at Austin and the full algorithm was developed jointly with the Laboratory for Computational Vision (LCV) at New York University.

SSIM is used for measuring the similarity between two images. The SSIM index is a full reference metric; in other words, the measurement or prediction of image quality is based on an initial uncompressed or distortion-free image as reference. SSIM is designed to improve on traditional methods such as peak signal-to-noise ratio (PSNR) and mean squared error (MSE), which have proven to be inconsistent with human visual perception.

The predecessor of SSIM was called Universal Quality Index (UQI), or Wang–Bovik Index, and was developed by Zhou Wang and Al Bovik in 2001. It was modified into the current version of SSIM (many variations now exist) along with Hamid Sheikh and Eero Simoncelli, and described in print in a paper entitled "Image quality assessment: From error visibility to structural similarity”, which was published in the IEEE Transactions on Image Processing in April 2004.

The 2004 SSIM paper has been cited more than 14,000 times according to Google Scholar, making it one of the highest cited papers in the image processing and video engineering fields, ever. It was accorded the IEEE Signal Processing Society Best Paper Award for 2009. It also received the IEEE Signal Processing Society Sustained Impact Award for 2017, indicative of a paper having an unusually high impact for at least 10 years following its publication. The inventors of SSIM were each accorded an individual Primetime Engineering Emmy Award by the Television Academy in 2015.

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