Blind quality assessment of multiply-distorted images based on structural degradation

It is known that images available usually undergo some stages of processing (e.g., acquisition, compression, transmission and display), and each stage may introduce certain type of distortion. Hence, images distorted by multiple types of distortions are common in real applications. Research in human visual perception has evidenced that the human visual system (HVS) is sensitive to image structural information. This fact inspires us to design a new blind/no-reference (NR) image quality assessment (IQA) method to evaluate the visual quality of multiply-distorted images based on structural degradation. Specifically, quality-aware features are extracted from both the first- and high-order image structures by local binary pattern (LBP) operators. Experimental results on two well-known multiply-distorted image databases demonstrate the outstanding performance of the proposed method.