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Image compression has been applied in the fields of image storage and videobroadcasting. However, it's formidably tough to distinguish the subtle qualitydifferences between those distorted images generated by different algorithms.In this paper, we propose a new image quality assessment framework to decidewhich image is better in an image group. To capture the subtle differences, afine-grained network is adopted to acquire multi-scale features. Subsequently,we design a cross subtract block for separating and gathering the informationwithin positive and negative image pairs. Enabling image comparison in featurespace. After that, a progressive feature fusion block is designed, which fusesmulti-scale features in a novel progressive way. Hierarchical spatial 2Dfeatures can thus be processed gradually. Experimental results show thatcompared with the current mainstream image quality assessment methods, theproposed network can achieve more accurate image quality assessment and rankssecond in the benchmark of CLIC in the image perceptual model track.

Progressive Feature Fusion Network for Enhancing Image Quality Assessment