No-reference image quality assessment based on internal generative mechanism

Abstract

No-reference (NR) image quality assessment (IQA) aims to measure the visual quality of a distorted image without access to its non- distorted reference image. Recent neuroscience research indicates that human visual system (HVS) perceives and understands perceptual sig- nals with an internal generative mechanism (IGM). Based on the IGM, we propose a novel and effective no-reference IQA framework in this paper. First, we decompose an image into an orderly part and a disor- derly one using a computational prediction model. Then we extract the joint statistics of two local contrast features from the orderly part and local binary pattern (LBP) based structural distributions from the other part, respectively. And finally, two groups of features extracted from the complementary parts are combined to train a regression model for image quality estimation. Extensive experiments on some standard databases validate that the proposed IQA method shows highly competitive performance to state-of-the-art NR-IQA ones. Moreover, the proposed metric also demonstrates its effectiveness on the multiply-distorted images.