Perceptual image quality assessment (IQA) adopts a computational model to assess the image quality in a fashion,\nwhich is consistent with human visual system (HVS). From the view of HVS, different image regions have different\nimportance. Based on this fact, we propose a simple and effective method based on the image decomposition for\nimage quality assessment. In our method, we first divide an image into two components: edge component and\ntexture component. To separate edge and texture components, we use the TV flow-based nonlinear diffusion method\nrather than the classic TV regularization methods, for highly effective computing. Different from the existing\ncontent-based IQA methods, we realize different methods on different components to compute image quality. More\nspecifically, the luminance and contrast similarity are computed in texture component, while the structural similarity is\ncomputed in edge component. After obtaining the local quality map, we use texture component again as a weight\nfunction to derive a single quality score. Experimental results on five datasets show that, compared with previous\napproaches in the literatures, the proposed method is more efficient and delivers higher prediction accuracy.
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