Abstract-We propose a highly unsupervised, training free, no reference image quality assessment (IQA) model that is based on the hypothesis that distorted images have certain latent characteristics that differ from those of "natural" or "pristine" images. These latent characteristics are uncovered by applying a "topic model" to visual words extracted from an assortment of pristine and distorted images. For the latent characteristics to be discriminatory between pristine and distorted images, the choice of the visual words is important. We extract quality-aware visual words that are based on natural scene statistic features [1]. We show that the similarity between the probability of occurrence of the different topics in an unseen image and the distribution of latent topics averaged over a large number of pristine natural images yields a quality measure. This measure correlates well with human difference mean opinion scores on the LIVE IQA database [2].
For examples, in REF , Mittal et al. proposed a method by conducting probabilistic latent semantic analysis on the statistical features of a large collection of pristine and distorted image patches.
Blind Image Quality Assessment Without Human Training Using Latent Quality Factors
IEEE Signal Processing Letters
IEEE Signal Processing Letters
Computer Science