A Soft-Ranked Index Fusion Framework With Saliency Weighting for Image Quality Assessment

Liangwei Yu, Zhao Wang, Yan Ye, Lingyu Zhu, Shiqi Wang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2022, pp. 1810-1814

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.

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[bibtex]
@InProceedings{Yu_2022_CVPR, author = {Yu, Liangwei and Wang, Zhao and Ye, Yan and Zhu, Lingyu and Wang, Shiqi}, title = {A Soft-Ranked Index Fusion Framework With Saliency Weighting for Image Quality Assessment}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2022}, pages = {1810-1814} }