A Soft-Ranked Index Fusion Framework with Saliency Weighting for Image Quality Assessment
Abstract
The compression technique is widely adopted for efficient data storage and transmission. Accurate image quality assessment (IQA) measures are urgently desired to evaluate the compression performance. To obtain a more robust evaluation, we propose a soft-ranked index fusion framework for the perceptual preference prediction task, with a combination of different quality measures. The derived soft-ranked indices are fully leveraged to provide the strong discriminability of ranking information. Furthermore, a saliency weighting approach is utilized to investigate the impact of visual attention on our framework. Experimental results indicate that our method achieves a promising prediction accuracy compared with the state-of-the-art quality measures.
Cite
Text
Yu et al. "A Soft-Ranked Index Fusion Framework with Saliency Weighting for Image Quality Assessment." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022. doi:10.1109/CVPRW56347.2022.00197Markdown
[Yu et al. "A Soft-Ranked Index Fusion Framework with Saliency Weighting for Image Quality Assessment." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022.](https://mlanthology.org/cvprw/2022/yu2022cvprw-softranked/) doi:10.1109/CVPRW56347.2022.00197BibTeX
@inproceedings{yu2022cvprw-softranked,
title = {{A Soft-Ranked Index Fusion Framework with Saliency Weighting for Image Quality Assessment}},
author = {Yu, Liangwei and Wang, Zhao and Ye, Yan and Zhu, Lingyu and Wang, Shiqi},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2022},
pages = {1809-1813},
doi = {10.1109/CVPRW56347.2022.00197},
url = {https://mlanthology.org/cvprw/2022/yu2022cvprw-softranked/}
}