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Reproducibility Companion Paper: Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality Assessment

Published: 17 October 2021 Publication History

Abstract

This companion paper supports the experimental replication of the paper "Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality Assessment'' presented at ACM Multimedia 2020. We provide the software package for replicating the implementation of the "Norm-in-Norm'' loss and the corresponding "LinearityIQA'' model used in the original paper. This paper contains the guidelines to reproduce all the experimental results of the original paper.

Supplementary Material

MP4 File (MM21-rcp0011.mp4)
The presentation video for "Reproducibility Companion Paper: Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality Assessment"

References

[1]
Deepti Ghadiyaram and Alan C. Bovik. 2016. Massive online crowdsourced study of subjective and objective picture quality. IEEE Transactions on Image Processing, Vol. 25, 1 (2016), 372--387.
[2]
Vlad Hosu, Hanhe Lin, Tamas Sziranyi, and Dietmar Saupe. 2020. KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment. IEEE Transactions on Image Processing, Vol. 29 (2020), 4041--4056.
[3]
Dingquan Li, Tingting Jiang, and Ming Jiang. 2020. Norm-in-norm loss with faster convergence and better performance for image quality assessment. In ACM International Conference on Multimedia. 789--797.
[4]
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019. PyTorch: An imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems. 8024--8035.

Cited By

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  • (2022)NTIRE 2022 Challenge on Perceptual Image Quality Assessment2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW56347.2022.00109(950-966)Online publication date: Jun-2022

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Published In

cover image ACM Conferences
MM '21: Proceedings of the 29th ACM International Conference on Multimedia
October 2021
5796 pages
ISBN:9781450386517
DOI:10.1145/3474085
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 October 2021

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Author Tags

  1. faster convergence
  2. iqa
  3. loss
  4. normalization
  5. reproducibility

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  • Short-paper

Funding Sources

  • National Science Founda- tion of China
  • Sino- German Center for Research Promotion

Conference

MM '21
Sponsor:
MM '21: ACM Multimedia Conference
October 20 - 24, 2021
Virtual Event, China

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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

View all
  • (2022)NTIRE 2022 Challenge on Perceptual Image Quality Assessment2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW56347.2022.00109(950-966)Online publication date: Jun-2022

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