Image Quality Assessment: Measuring Perceptual Degradation via Distribution Measures in Deep Feature Spaces-Reference-Cited by-同舟云学术

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Image Quality Assessment: Measuring Perceptual Degradation via Distribution Measures in Deep Feature Spaces

Published:2024 Issue: Volume:33 Page:4044-4059
ISSN:1057-7149
Container-title:IEEE Transactions on Image Processing
language:
Short-container-title:IEEE Trans. on Image Process.

Author:

Liao Xingran¹^ORCID,Wei Xuekai²^ORCID,Zhou Mingliang²^ORCID,Li Zhengguo³^ORCID,Kwong Sam⁴^ORCID

Affiliation:

1. Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong

2. School of Computer Science, Chongqing University, Chongqing, China

3. Institute for Infocomm Research, Agency for Science, Technology and Research, Fusionopolis, Singapore

4. Department of Computing and Decision Sciences, Computational Intelligence Program, Lingnan University, Tuen Mun, Hong Kong

Funder

National Natural Science Foundation of China

Hong Kong General Research Fund-Research Grant Council (GRFRGC) GRF

Chongqing Talent

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/83/10346232/10577432.pdf?arnumber=10577432

Reference67 articles.

1. Accurate and Fast Image Denoising via Attention Guided Scaling

2. BiN-Flow: Bidirectional Normalizing Flow for Robust Image Dehazing

3. Adversarial Multi-Path Residual Network for Image Super-Resolution

4. KADID-10k: A Large-scale Artificially Distorted IQA Database

5. Image Quality Assessment: Unifying Structure and Texture Similarity

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