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The Structural Similarity Index (SSIM) is generally considered to be amilestone in the recent history of Image Quality Assessment (IQA). Alas, SSIM'saccepted development from the product of three heuristic factors continues toobscure it's real underlying simplicity. Starting instead from asymmetric-antisymmetric reformulation we first show SSIM to be a contrast orvisibility function in the classic sense. Furthermore, the previously enigmaticstructural covariance is revealed to be the difference of variances. The secondstep, eliminating the intrinsic quadratic nature of SSIM, allows a near linearcorrelation with human observer scores, and without invoking the usual, butarbitrary, sigmoid model fitting. We conclude that SSIM can be re-interpretedin terms of perceptual masking: it is essentially equivalent to a normalisederror or noise visibility function (NVF), and, furthermore, the NVF aloneexplains it success in modelling perceptual image quality. We use the termDissimilarity Quotient (DQ) for the specifically anti/symmetric SSIM derivedNVF. It seems that IQA researchers may now have two choices: 1) Continue to usethe complex SSIM formula, but noting that SSIM only works coincidentally sincethe covariance term is actually the mean square error (MSE) in disguise. 2) Usethe simplest of all perceptually-masked image quality metrics, namely NVF orDQ. On this choice Occam is clear: in the absence of differences in predictiveability, the fewer assumptions that are made, the better.

Structural Similarity Index SSIMplified: Is there really a simpler concept at the heart of image quality measurement?