template matching without using any training sample. Since data-driven pattern recognition approaches may produce better performance when a large amount of data is available for training, the authors suggest using CW-SSIM in data-driven approaches. Performance comparison Due to its popularity, SSIM is often compared to other metrics, including more simple metrics such as MSE and PSNR, and other perceptual image and video quality metrics. SSIM has been repeatedly shown to significantly outperform MSE and its derivates in accuracy, including research by its own authors and others. A paper by Dosselmann and Yang claims that the performance of SSIM is "much closer to that of the MSE" than usually assumed. While they do not dispute the advantage of SSIM over MSE, they state an analytical and functional dependency between the two metrics. According to their research, SSIM has been found to correlate as well as MSE-based methods on subjective databases other than the databases from SSIM's creators. As an example, they cite Reibman and Poole, who found that MSE outperformed SSIM on a database containing packet-loss–impaired video. In another paper, an analytical link between PSNR and SSIM was identified. See also Mean squared error Peak signal-to-noise ratio Video quality References External links Home page Rust Implementation C/C++ Implementation DSSIM C++ Implementation Chris Lomont's C# Implementation qpsnr implementation (multi threaded C++) Implementation in VQMT software Implementation in Python "Mystery Behind Similarity Measures MSE and SSIM", Gintautas Palubinskas, 2014 Image processing