TY - GEN
T1 - Blind Image Quality Assessment by Learning from Multiple Annotators
AU - Ma, Kede
AU - Liu, Xuelin
AU - Fang, Yuming
AU - Simoncelli, Eero P.
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/9
Y1 - 2019/9
N2 - Models for image quality assessment (IQA) are generally optimized and tested by comparing to human ratings, which are expensive to obtain. Here, we develop a blind IQA (BIQA) model, and a method of training it without human ratings. We first generate a large number of corrupted image pairs, and use a set of existing IQA models to identify which image of each pair has higher quality. We then train a convolutional neural network to estimate perceived image quality along with the uncertainty, optimizing for consistency with the binary labels. The reliability of each IQA annotator is also estimated during training. Experiments demonstrate that our model outperforms state-of-the-art BIQA models in terms of correlation with human ratings in existing databases, as well in group maximum differentiation (gMAD) competition.
AB - Models for image quality assessment (IQA) are generally optimized and tested by comparing to human ratings, which are expensive to obtain. Here, we develop a blind IQA (BIQA) model, and a method of training it without human ratings. We first generate a large number of corrupted image pairs, and use a set of existing IQA models to identify which image of each pair has higher quality. We then train a convolutional neural network to estimate perceived image quality along with the uncertainty, optimizing for consistency with the binary labels. The reliability of each IQA annotator is also estimated during training. Experiments demonstrate that our model outperforms state-of-the-art BIQA models in terms of correlation with human ratings in existing databases, as well in group maximum differentiation (gMAD) competition.
KW - Blind image quality assessment
KW - convolutional neural networks
KW - gMAD competition
UR - http://www.scopus.com/inward/record.url?scp=85076815140&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85076815140&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2019.8803390
DO - 10.1109/ICIP.2019.8803390
M3 - Conference contribution
AN - SCOPUS:85076815140
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 2344
EP - 2348
BT - 2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings
PB - IEEE Computer Society
T2 - 26th IEEE International Conference on Image Processing, ICIP 2019
Y2 - 22 September 2019 through 25 September 2019
ER -