Blind Predicting Similar Quality mAP for Image Quality Assessment
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-the-art BIQA methods.
Cite
Text
Pan et al. "Blind Predicting Similar Quality mAP for Image Quality Assessment." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018. doi:10.1109/CVPR.2018.00667Markdown
[Pan et al. "Blind Predicting Similar Quality mAP for Image Quality Assessment." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018.](https://mlanthology.org/cvpr/2018/pan2018cvpr-blind/) doi:10.1109/CVPR.2018.00667BibTeX
@inproceedings{pan2018cvpr-blind,
title = {{Blind Predicting Similar Quality mAP for Image Quality Assessment}},
author = {Pan, Da and Shi, Ping and Hou, Ming and Ying, Zefeng and Fu, Sizhe and Zhang, Yuan},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2018},
doi = {10.1109/CVPR.2018.00667},
url = {https://mlanthology.org/cvpr/2018/pan2018cvpr-blind/}
}