Proceedings of the 2015 6th International Conference on Manufacturing Science and Engineering

No-reference blur image quality assessment based on Simulated Annealing and General Regression Neural Network

Authors
Zhongzhong Liu, Tao Cheng
Corresponding Author
Zhongzhong Liu
Available Online December 2015.
DOI
10.2991/icmse-15.2015.140How to use a DOI?
Keywords
General Regression Neural Network; no-reference; image quality assessment; Simulated Annealing
Abstract

In order to improve the accuracy and efficiency of no-reference blur image quality assessment based on General Regression Neural Network. We choose Simulated Annealing algorithm to optimize the method. Using LIVE (Laboratory for Image & Video Engineering) database as the initial study database. 174 images from LIVE database are assigned randomly to two groups. Phase-matched images generated by phase transformation. We can get Gray Level Co-occurrence Matrix form phase-matched images. Then, get the energy, Entropy, correlation, contrast and homogeneity of these five characteristics indexes. Using the above indicators as input data and using Difference Mean Opinion Score as output data. Training neural network model on this way. In order to improve the accuracy and efficiency, using the Simulated Annealing algorithm to find the optimal smoothing factor parameter. Finally, spearman correlation coefficient of objective and subjective data is 0.9319 . Pearson correlation coefficient of objective and subjective data is 0.9328. The results show that, this algorithm fits Difference Mean Opinion Score well. It predict better on image quality assessment.

Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

Volume Title
Proceedings of the 2015 6th International Conference on Manufacturing Science and Engineering
Series
Advances in Engineering Research
Publication Date
December 2015
ISBN
978-94-6252-137-7
ISSN
2352-5401
DOI
10.2991/icmse-15.2015.140How to use a DOI?
Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Zhongzhong Liu
AU  - Tao Cheng
PY  - 2015/12
DA  - 2015/12
TI  - No-reference blur image quality assessment based on Simulated Annealing and General Regression Neural Network
BT  - Proceedings of the 2015 6th International Conference on Manufacturing Science and Engineering
PB  - Atlantis Press
SP  - 779
EP  - 785
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmse-15.2015.140
DO  - 10.2991/icmse-15.2015.140
ID  - Liu2015/12
ER  -