Objective image quality assessment (QA) is a fundamental and challenging job in image processing which evaluates the image quality consistently with human perception automatically. Generally, an image can be segmented into two kinds of areas: structure and texture. And structural information plays a much more important role between the two. The pixels, edges and shape with directional characteristic contribute much more to the structural information. In this paper, the structural information is modeled as the energy of directional projection, which is shown in a directional projection-based map that is built by Radon transform. On the assumption that any image's distortion could be modeled as the difference between the directional projection-based maps of reference and distortion images, we propose a new objective quality assessment method with Radon transform for full reference model, and we also try to explore the feasibility to develop the image QA metrics with scalable efficiency and computational cost. Experimental results show that the proposed metrics are well consistent with the subjective quality score.
['Jianxin Pang', 'Rong Zhang', 'Hui Zhang', 'Xuan Huang', 'Zhengkai Liu']
Image quality assessment metrics with Radon transform