  In this work we investigate the use of deep learning for distortion-generic
blind image quality assessment. We report on different design choices, ranging
from the use of features extracted from pre-trained Convolutional Neural
Networks (CNNs) as a generic image description, to the use of features
extracted from a CNN fine-tuned for the image quality task. Our best proposal,
named DeepBIQ, estimates the image quality by average pooling the scores
predicted on multiple sub-regions of the original image. The score of each
sub-region is computed using a Support Vector Regression (SVR) machine taking
as input features extracted using a CNN fine-tuned for category-based image
quality assessment. Experimental results on the LIVE In the Wild Image Quality
Challenge Database and on the LIVE Image Quality Assessment Database show that
DeepBIQ outperforms the state-of-the-art methods compared, having a Linear
Correlation Coefficient (LCC) with human subjective scores of almost 0.91 and
0.98 respectively. Furthermore, in most of the cases, the quality score
predictions of DeepBIQ are closer to the average observer than those of a
generic human observer.
