Blind image quality assessment on authentically distorted images with perceptual features

Abstract

Almost all present successful general purpose blind image quality assessment (BIQA) models are designed only towards singly and synthetically distorted images. However, real world images generally contain multiply types of distortions other than single type of distortion. Even some state of the art BIQA methods cannot work well on authentically distorted images. In this paper we propose a novel effective BIQA method for authentically distorted images to remedy the shortage of the popular BIQA models. First, except for traditional natural scene statistics (NSS) based features, we introduce a series of quality aware features based on human visual system (HVS), such as image dynamic range, color information, blurriness. Then support vector regression (SVR) is utilized to learn the mapping between the combined effective features and human opinion scores. Experimental results demonstrate the promising performance of the proposed method.