An important aim of research on the blind image
quality assessment (IQA) problem is to devise perceptual models
that can predict the quality of distorted images with as little
prior knowledge of the images or their distortions as possible.
Current state-of-the-art “general purpose” no reference (NR) IQA
algorithms require knowledge about anticipated distortions in
the form of training examples and corresponding human opinion
scores. However we have recently derived a blind IQA model that
only makes use of measurable deviations from statistical regularities
observed in natural images, without training on human-rated
distorted images, and, indeed without any exposure to distorted
images. Thus, it is “completely blind.” The new IQA model, which
we call the Natural Image Quality Evaluator (NIQE) is based on
the construction of a “quality aware” collection of statistical features
based on a simple and successful space domain natural scene
statistic (NSS) model. These features are derived from a corpus of
natural, undistorted images. Experimental results show that the
new index delivers performance comparable to top performing
NR IQA models that require training on large databases of human
opinions of distorted images. A software release is available at
http://live.ece.utexas.edu/research/quality/niqe_release.zip.