No-reference image quality assessment based on regional mutual information

DOI

10.2991/icitmi-15.2015.138How to use a DOI?

Keywords

Image quality assessment (IQA), no reference (NR), regional mutual information, wavelet transform, Support Vector Machine (SVM).

Abstract

We study the regional mutual information to build a powerful and effective no-reference (NR) image quality assessment (IQA) approach. This approach does not need to compute distortion-specific features, but use the features which can distinguish the distortion in images across the distortion types. Such features are extracted from the image regional mutual information, which includes modified regional mutual information and a novel way to describe such information via wavelet transform. Operating within the 2-step framework, these features are tested on the LIVE database and show a nice correlation with human subjective opinions of image quality.

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)

TY  - CONF
AU  - Yanjie Wang
AU  - Jianjun Zhou
AU  - Li Jia
AU  - Yue Wang
PY  - 2015/10
DA  - 2015/10
TI  - No-reference image quality assessment based on regional mutual information
BT  - Proceedings of the 4th International Conference on Information Technology and Management Innovation
PB  - Atlantis Press
SP  - 824
EP  - 828
SN  - 2352-538X
UR  - https://doi.org/10.2991/icitmi-15.2015.138
DO  - 10.2991/icitmi-15.2015.138
ID  - Wang2015/10
ER  -