Image quality assessment based on local orientation distributions

Image quality assessment (IQA) is very important for many image and video processing applications, e.g. compression, archiving, restoration and enhancement. An ideal image quality metric should achieve consistency between image distortion prediction and psychological perception of human visual system (HVS). Inspired by that HVS is quite sensitive to image local orientation features, in this paper, we propose a new structural information based image quality metric, which evaluates image distortion by computing the distance of Histograms of Oriented Gradients (HOG) descriptors. Experimental results on LIVE database show that the proposed IQA metric is competitive with state-of-the-art IQA metrics, while keeping relatively low computing complexity.