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

Liangwei Yu; Zhao Wang; Yan Ye; Lingyu Zhu; Shiqi Wang

doi:10.1109/CVPRW56347.2022.00197

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

Liangwei Yu, Zhao Wang, Yan Ye, Lingyu Zhu, Shiqi Wang

Department of Computer Science

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-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 language	English
Title of host publication	Proceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022
Publisher	IEEE Computer Society
Pages	1809-1813
ISBN (Electronic)	9781665487399
ISBN (Print)	9781665487405
DOIs	https://doi.org/10.1109/CVPRW56347.2022.00197
Publication status	Published - 2022
Event	2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW 2022) - Hybrid, New Orleans, United States Duration: 19 Jun 2022 → 24 Jun 2022 https://cvpr2022.thecvf.com/

Publication series

Name	IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Volume	2022-June
ISSN (Print)	2160-7508
ISSN (Electronic)	2160-7516

Conference

Conference	2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW 2022)
Place	United States
City	New Orleans
Period	19/06/22 → 24/06/22
Internet address	https://cvpr2022.thecvf.com/

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Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

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Yu, L., Wang, Z., Ye, Y., Zhu, L., & Wang, S. (2022). A Soft-ranked Index Fusion Framework with Saliency Weighting for Image Quality Assessment. In Proceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022 (pp. 1809-1813). (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops; Vol. 2022-June). IEEE Computer Society. https://doi.org/10.1109/CVPRW56347.2022.00197

Yu, Liangwei ; Wang, Zhao ; Ye, Yan et al. / A Soft-ranked Index Fusion Framework with Saliency Weighting for Image Quality Assessment. Proceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022. IEEE Computer Society, 2022. pp. 1809-1813 (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops).

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title = "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.",

author = "Liangwei Yu and Zhao Wang and Yan Ye and Lingyu Zhu and Shiqi Wang",

note = "Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).; 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW 2022) ; Conference date: 19-06-2022 Through 24-06-2022",

year = "2022",

doi = "10.1109/CVPRW56347.2022.00197",

language = "English",

isbn = "9781665487405",

series = "IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops",

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Yu, L, Wang, Z, Ye, Y, Zhu, L & Wang, S 2022, A Soft-ranked Index Fusion Framework with Saliency Weighting for Image Quality Assessment. in Proceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, vol. 2022-June, IEEE Computer Society, pp. 1809-1813, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW 2022), New Orleans, Louisiana, United States, 19/06/22. https://doi.org/10.1109/CVPRW56347.2022.00197

A Soft-ranked Index Fusion Framework with Saliency Weighting for Image Quality Assessment. / Yu, Liangwei; Wang, Zhao; Ye, Yan et al.
Proceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022. IEEE Computer Society, 2022. p. 1809-1813 (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops; Vol. 2022-June).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

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T1 - A Soft-ranked Index Fusion Framework with Saliency Weighting for Image Quality Assessment

AU - Yu, Liangwei

AU - Wang, Zhao

AU - Ye, Yan

AU - Zhu, Lingyu

AU - Wang, Shiqi

N1 - Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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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SN - 9781665487405

T3 - IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

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BT - Proceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022

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Yu L, Wang Z, Ye Y, Zhu L , Wang S. A Soft-ranked Index Fusion Framework with Saliency Weighting for Image Quality Assessment. In Proceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022. IEEE Computer Society. 2022. p. 1809-1813. (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops). Epub 2022 Aug 23. doi: 10.1109/CVPRW56347.2022.00197

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

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