Full-reference image quality assessment based on image segmentation with edge feature

Full-reference image quality assessment is widely used in many applications, such as image compression, image transmission and image mosaic. The visual masking effect has a significant impact on the perception of the human visual system, which is ignored in previous image quality assessments. Combined with the visual masking effect, a full-reference image quality assessment method by edge-feature-based image segmentation (EFS) was proposed. First, the image is segmented into three parts: contour regions, edge-extension regions and slowly-varying regions. The pixels in different regions are then described by different low-level features in the light of the visual masking effect. Finally, the low-level features in each part are pooled by two complementary aspects: visual saliency and visual masking effect. Experimental results on four large-scale benchmark databases show that the proposed method has a better prediction accuracy in all distortion types than other state-of-the-art image quality assessment indices.