Abstract
Original language | English |
---|---|
Article number | 9055066 |
Pages (from-to) | 7398-7410 |
Number of pages | 13 |
Journal | IEEE Transactions on Instrumentation and Measurement |
Volume | 69 |
Issue number | 10 |
Early online date | 2 Apr 2020 |
DOIs | |
Publication status | Published - Oct 2020 |
Externally published | Yes |
Bibliographical note
Publisher Copyright:© 1963-2012 IEEE.
Funding
This work was supported in part by the Natural Science Foundation of China under Grant 61901236, Grant 61622109, and Grant 61801303, in part by the Natural Science Foundation of Ningbo under Grant 2019A610097, in part by the Zhejiang Natural Science Foundation of China under Grant R18F010008, in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2019A1515012031, in part by the Shenzhen Science and Technology Plan Basic Research Project under Grant JCYJ20190808161805519, and in part by the National Science Foundation of Shenzhen University under Grant 860-000002110122. It was also sponsored by K. C. Wong Magna Fund in Ningbo University.
Keywords
- Blind image quality measurement
- convolutional neural network
- deep learning
- dictionary encoding
- end-to-end