No-reference image quality assessment with orientation selectivity mechanism
Jinjian Wu, Man Zhang, Guangming Shi, Xuemei Xie, Weisi Lin · 2017
No-reference (NR) image quality assessment (IQA) technology is greatly required in quality-orientated visual signal processing systems. However, without the guidance of the reference information, it is still a great challenge for NR IQA to perform consistent with the subjective perception. Researches on cognitive neuroscience state that the human visual system (HVS) presents substantially orientation selectivity mechanism, within which the visual structures are extracted in the local receptive fields for scene understanding. Inspired by this mechanism, a set of orientation selectivity based visual patterns are designed. By analyzing the quality degradation on those patterns, a novel visual pattern degradation based NR IQA method is proposed. Experimental results on large databases demonstrate that the proposed method outperforms the existing NR IQA methods.