Blind Image Quality Assessment via Deep Response Feature Decomposition and Aggregation-Reference-Cited by-同舟云学术

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Blind Image Quality Assessment via Deep Response Feature Decomposition and Aggregation

Published:2023-11 Issue:6 Volume:17 Page:1165-1177
ISSN:1932-4553
Container-title:IEEE Journal of Selected Topics in Signal Processing
language:
Short-container-title:IEEE J. Sel. Top. Signal Process.

Author:

Wang Hui¹,Wang Guangcheng²^ORCID,Xia Wenjun¹^ORCID,Yang Ziyuan¹^ORCID,Yu Hui¹,Fang Leyuan³^ORCID,Zhang Yi⁴^ORCID

Affiliation:

1. College of Computer Science, Sichuan University, Chengdu, China

2. School of Transportation and Civil Engineering, Nantong University, Nantong, China

3. College of Electrical and Information Engineering, Hunan University, Changsha, China

4. School of Cyber Science and Engineering, Sichuan University, Chengdu, China

Funder

National Natural Science Foundation of China

Sichuan Science and Technology

Sichuan University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/4200690/10378961/10123006.pdf?arnumber=10123006

Reference56 articles.

1. Deep Learning of Human Visual Sensitivity in Image Quality Assessment Framework

2. Reduced-Reference Quality Assessment Based on the Entropy of DWT Coefficients of Locally Weighted Gradient Magnitudes

3. Unsupervised feature learning framework for no-reference image quality assessment

4. Blind Image Quality Assessment Using Joint Statistics of Gradient Magnitude and Laplacian Features

5. Using Free Energy Principle For Blind Image Quality Assessment

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