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-the-art BIQA methods.
@article{arxiv.1805.08493,
title = {Blind Predicting Similar Quality Map for Image Quality Assessment},
author = {Da Pan and Ping Shi and Ming Hou and Zefeng Ying and Sizhe Fu and Yuan Zhang},
journal= {arXiv preprint arXiv:1805.08493},
year = {2019}
}