Progressive Feature Fusion Network for Enhancing Image Quality Assessment

Image compression has been applied in the fields of image storage and video broadcasting. However, it's formidably tough to distinguish the subtle quality differences between those distorted images generated by different algorithms. In this paper, we propose a new image quality assessment framework called Progressive Feature Fusion Network to decide which image is better in an image group. The network can effectively select clearer images based on their features. Experimental results show that compared with the current mainstream image quality assessment methods, the proposed network can achieve more accurate image quality assessment and ranks second in the benchmark of Challenge on Learned Image Compression in the image perceptual model track.