Attention-Guided Neural Networks for Full-Reference and No-Reference Audio-Visual Quality Assessment-Reference-Cited by-同舟云学术

Attention-Guided Neural Networks for Full-Reference and No-Reference Audio-Visual Quality Assessment

Published:2023 Issue: Volume:32 Page:1882-1896
ISSN:1057-7149
Container-title:IEEE Transactions on Image Processing
language:
Short-container-title:IEEE Trans. on Image Process.

Author:

Cao Yuqin¹^ORCID,Min Xiongkuo¹^ORCID,Sun Wei¹^ORCID,Zhai Guangtao¹^ORCID

Affiliation:

1. Institute of Image Communication and Network Engineering, Shanghai Key Laboratory of Digital Media Processing and Transmissions, Shanghai Jiao Tong University, Shanghai, China

Funder

NSFC

Fundamental Research Funds for the Central Universities

National Key Research and Development Program of China

Shanghai Municipal Science and Technology Major Project

Shanghai Pujiang Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Graphics and Computer-Aided Design,Software

Link

http://xplorestaging.ieee.org/ielx7/83/9991910/10075375.pdf?arnumber=10075375

Reference69 articles.

1. A Perceptually Weighted Rank Correlation Indicator for Objective Image Quality Assessment

2. MC360IQA: A Multi-channel CNN for Blind 360-Degree Image Quality Assessment

3. ESIM: Edge Similarity for Screen Content Image Quality Assessment

4. Q-DNN: A quality-aware deep neural network for blind assessment of enhanced images

5. Blind Image Quality Estimation via Distortion Aggravation

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