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

摘要

Highlights•An image quality assessment method based on visual masking effect is proposed.•According to edge features, the image is segmented into sub-regions for assessment.•The model achieves high accuracy for all distortion types in benchmark databases.AbstractFull-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.