No-reference image quality assessment using shearlet transform

Abstract

Image and video quality measurements are crucial for many applications, such as acquisition, compression, transmission, enhancement, and reproduction. Nowadays, no-reference (NR) image quality assessment (IQA) methods have been drawn extensive attention because it does not need any information of reference images. However, most proposed NR IQA methods are designed only for one or a set of predefined specific distortion types, which are unlikely to generalize for evaluating images distorted with other types of distortions. In order to estimate a wide range of image distortions, in this paper, a novel NR IQA method is proposed which is based on shearlet transform, a new multiscale directional transform with a strong ability to localize distributed discontinuities. The distorted image leads to significant variation in the distributed discontinuities in all directions. Thus, the statistical property of the distorted image is significantly different from that of natural images in shearlet domain. A new model is also proposed to measure this difference. Numerical experiments demonstrate that this new NR IQA method is consistent with subjective assessment, very effective for many well-known types of image distortions and superior to some existing prominent methods.