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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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationProceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022
PublisherIEEE Computer Society
Pages1809-1813
Number of pages5
ISBN (Electronic)9781665487399
DOIs
StatePublished - 2022
Externally publishedYes
Event2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022 - New Orleans, United States
Duration: 19 Jun 202224 Jun 2022

Publication series

NameIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Volume2022-June
ISSN (Print)2160-7508
ISSN (Electronic)2160-7516

Conference

Conference2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022
Country/TerritoryUnited States
CityNew Orleans
Period19/06/2224/06/22

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