TY - GEN
T1 - A Novel Blind Image Quality Assessment Method Based on Refined Natural Scene Statistics
AU - OU, Fu-Zhao
AU - WANG, Yuan-Gen
AU - ZHU, Guopu
PY - 2019
Y1 - 2019
N2 - Natural scene statistics (NSS) model has received considerable attention in the image quality assessment (IQA) community due to its high sensitivity to image distortion. However, most existing NSS-based IQA methods extract features either from spatial domain or from transform domain. There is little work to simultaneously consider the features from these two domains. In this paper, a novel blind IQA method (NBIQA) based on refined NSS is proposed. The proposed NBIQA first investigates the performance of a large number of candidate features from both the spatial and transform domains. Based on the investigation, we construct a refined NSS model by selecting competitive features from existing NSS models and adding three new features. Then the refined NSS is fed into SVM tool to learn a simple regression model. Finally, the trained regression model is used to predict the scalar quality score of the image. Experimental results tested on both LIVE IQA and LIVE-C databases show that the proposed NBIQA performs better in terms of synthetic and authentic image distortion than current mainstream IQA methods. The source code is available at https://github.com/GZU-Image-Video-Lab/NBIQA. © 2019 IEEE
AB - Natural scene statistics (NSS) model has received considerable attention in the image quality assessment (IQA) community due to its high sensitivity to image distortion. However, most existing NSS-based IQA methods extract features either from spatial domain or from transform domain. There is little work to simultaneously consider the features from these two domains. In this paper, a novel blind IQA method (NBIQA) based on refined NSS is proposed. The proposed NBIQA first investigates the performance of a large number of candidate features from both the spatial and transform domains. Based on the investigation, we construct a refined NSS model by selecting competitive features from existing NSS models and adding three new features. Then the refined NSS is fed into SVM tool to learn a simple regression model. Finally, the trained regression model is used to predict the scalar quality score of the image. Experimental results tested on both LIVE IQA and LIVE-C databases show that the proposed NBIQA performs better in terms of synthetic and authentic image distortion than current mainstream IQA methods. The source code is available at https://github.com/GZU-Image-Video-Lab/NBIQA. © 2019 IEEE
KW - Image quality assessment
KW - natural scene statistics
KW - image distortion
UR - http://www.scopus.com/inward/record.url?scp=85076814894&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85076814894&origin=recordpage
U2 - 10.1109/ICIP.2019.8803047
DO - 10.1109/ICIP.2019.8803047
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 978-1-5386-6250-2
SP - 1004
EP - 1008
BT - 2019 IEEE International Conference on Image Processing
PB - IEEE
T2 - 26th IEEE International Conference on Image Processing (ICIP 2019)
Y2 - 22 September 2019 through 25 September 2019
ER -