  Current top-performing blind perceptual image quality prediction models are
generally trained on legacy databases of human quality opinion scores on
synthetically distorted images. Therefore they learn image features that
effectively predict human visual quality judgments of inauthentic, and usually
isolated (single) distortions. However, real-world images usually contain
complex, composite mixtures of multiple distortions. We study the perceptually
relevant natural scene statistics of such authentically distorted images, in
different color spaces and transform domains. We propose a bag of feature-maps
approach which avoids assumptions about the type of distortion(s) contained in
an image, focusing instead on capturing consistencies, or departures therefrom,
of the statistics of real world images. Using a large database of authentically
distorted images, human opinions of them, and bags of features computed on
them, we train a regressor to conduct image quality prediction. We demonstrate
the competence of the features towards improving automatic perceptual quality
prediction by testing a learned algorithm using them on a benchmark legacy
database as well as on a newly introduced distortion-realistic resource called
the LIVE In the Wild Image Quality Challenge Database. We extensively evaluate
the perceptual quality prediction model and algorithm and show that it is able
to achieve good quality prediction power that is better than other leading
models.
