Referenceless quality metric of multiply-distorted images based on structural degradation

Multiply-distorted images, that is, distorted by different types of distortions simultaneously, are so common in real applications. This kind of images contain multiple overlaying stages (e.g., acquisition, compression and transmission stage). Each stage will introduce a certain type of distortion, for example, sensor noise in acquisition stage and compression artifacts in compression stage. However, most current blind/no-reference image quality assessment (NR-IQA) methods are specifically designed for singly-distorted images, thus resulting in their deficiency in handling multiply-distorted images. Motivated by the hypothesis that human visual system (HVS) is adapted to the structural information in images, we attempt to assess multiply-distorted images based on structural degradation. To this end, we use both first- and high-order image structures to design a novel referenceless quality metric for multiply-distorted images. Specifically, we leverage the quality-aware features extracted from both the gradient-magnitude map and contrast-normalized map, and further improve the performance by making use of redundancy of features with random subspace method. Experimental results on popular multiply-distorted image databases verify the outstanding performance of the proposed method.