Purchase this article with an account.
Anish Mittal, Rajiv Soundararajan, Alan Bovik; Prediction of Image Naturalness and Quality. Journal of Vision 2013;13(9):1056. doi: https://doi.org/10.1167/13.9.1056.
Download citation file:
© ARVO (1962-2015); The Authors (2016-present)
This work studies connections between image naturalness and perceived image quality. Specifically we define an Image Naturalness Index that quantifies intrinsic naturalness of images using features learned from a representative database of natural images. These features derive from models of early visual processing that lead to statistically regular processed images. We have observed that image distortions disrupt statistical image naturalness and that humans are highly sensitive to these disruptions in distorted images they observe. The naturalness index is derived by selecting patches from natural images, then collecting relevant NSS features from these patches to construct a natural image model. A multivariate Gaussian (MVG) distribution is used to characterize them. The ‘naturalness’ of an arbitrary test image, whose quality needs to be evaluated is expressed as the distance of the MVG model obtained from natural images to the MVG fit on the set of the same NSS features extracted from the patches of the test image. When applied to distorted images, the index is able to achieve image quality assessment (IQA) performance (in terms of correlation with human subjective judgments) comparable to leading full reference IQA algorithms such the Structural Similarity (SSIM) Index and much better than the Mean Squared Error (MSE) which demonstrates its relevance with respect to perceptual distortion sensitivity. This method is different from prior IQA approaches, all of which required both similar NSS models as well as exposure to both distorted images and human judgments of them, replying instead only on a simple natural scene statistic (NSS) model. Conversely, the Image Naturalness Index performs well when constructed using perceptually relevant NSS features extracted from a corpus of naturalistic, undistorted features, but does not function well when using other types of features (such as SIFT features or edges) or if the features are extracted from distorted images.
Meeting abstract presented at VSS 2013
This PDF is available to Subscribers Only