Hybrid No-Reference Quality Metric for Singly and Multiply Distorted Images

In a typical image communication system, the visual signal presented to the end users may undergo the steps of acquisition, compression and transmission which cause the artifacts of blurring, quantization and noise. However, the researches of image quality assessment (IQA) with multiple distortion types are very limited. In this paper, we first introduce a new multiply distorted image database (MDID2013), which is composed of 324 images that are simultaneously corrupted by blurring, JPEG compression and noise injection. We then propose a new six-step blind metric (SISBLIM) for quality assessment of both singly and multiply distorted images. Inspired by the early human visual model and recently revealed free energy based brain theory, our method works to systematically combine the single quality prediction of each emerging distortion type and joint effects of different distortion sources. Comparative studies of the proposed SISBLIM with popular full-reference IQA approaches and start-of-the-art no-reference IQA metrics are conducted on five singly distorted image databases (LIVE, TID2008, CSIQ, IVC, Toyama) and two newly released multiply distorted image databases (LIVEMD, MDID2013). Experimental results confirm the effectiveness of our blind technique. MATLAB codes of the proposed SISBLIM algorithm and MDID2013 database will be available online at http://gvsp.sjtu.edu.cn/.