Abstract
A key problem in blind image quality assessment (BIQA)
is how to effectively model the properties of human visual
system in a data-driven manner. In this paper, we propose
a simple and efficient BIQA model based on a novel framework which consists of a fully convolutional neural network
(FCNN) and a pooling network to solve this problem. In
principle, FCNN is capable of predicting a pixel-by-pixel
similar quality map only from a distorted image by using
the intermediate similarity maps derived from conventional
full-reference image quality assessment methods. The predicted pixel-by-pixel quality maps have good consistency
with the distortion correlations between the reference and
distorted images. Finally, a deep pooling network regresses the quality map into a score. Experiments have demonstrated that our predictions outperform many state-of-theart BIQA methods