An Effective Foveated 360° Image Assessment Based on Graph Convolution Network-Reference-Cited by-同舟云学术

An Effective Foveated 360° Image Assessment Based on Graph Convolution Network

Published:2022 Issue: Volume:10 Page:98165-98178
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Huong Truong Thu¹^ORCID,Ha Do Thu¹,Tran Huyen T. T.²^ORCID,Viet Ngo Duc¹,Tien Bui Duy¹,Thanh Nguyen Huu¹^ORCID,Thang Truong Cong²^ORCID,Nam Pham Ngoc³

Affiliation:

1. School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi, Vietnam

2. Department of Computer Science and Engineering, University of Aizu, Aizuwakamatsu, Japan

3. College of Engineering and Computer Science, Vin University, Hanoi, Vietnam

Funder

Vingroup Innovation Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09878309.pdf?arnumber=9878309

Reference44 articles.

1. Semi-supervised classification with graph convolutional networks;kipf;arXiv 1609 02907,2016

2. Analysis of Human Receptor Density

3. Quality Assessment of Stereoscopic 360-degree Images from Multi-viewports

4. Perceptually Unequal Packet Loss Protection by Weighting Saliency and Error Propagation

5. Comparison of Full-Reference Image Quality Models for Optimization of Image Processing Systems

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