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A Soft-ranked Index Fusion Framework with Saliency Weighting for Image Quality Assessment

  • Liangwei Yu
  • , Zhao Wang
  • , Yan Ye
  • , Lingyu Zhu
  • , Shiqi Wang
  • Alibaba Group Holding Ltd.
  • City University of Hong Kong

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

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.

源语言英语
主期刊名Proceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022
出版商IEEE Computer Society
1809-1813
页数5
ISBN(电子版)9781665487399
DOI
出版状态已出版 - 2022
已对外发布
活动2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022 - New Orleans, 美国
期限: 19 6月 202224 6月 2022

出版系列

姓名IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
2022-June
ISSN(印刷版)2160-7508
ISSN(电子版)2160-7516

会议

会议2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022
国家/地区美国
New Orleans
时期19/06/2224/06/22

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