  We propose a deep bilinear model for blind image quality assessment (BIQA)
that handles both synthetic and authentic distortions. Our model consists of
two convolutional neural networks (CNN), each of which specializes in one
distortion scenario. For synthetic distortions, we pre-train a CNN to classify
image distortion type and level, where we enjoy large-scale training data. For
authentic distortions, we adopt a pre-trained CNN for image classification. The
features from the two CNNs are pooled bilinearly into a unified representation
for final quality prediction. We then fine-tune the entire model on target
subject-rated databases using a variant of stochastic gradient descent.
Extensive experiments demonstrate that the proposed model achieves superior
performance on both synthetic and authentic databases. Furthermore, we verify
the generalizability of our method on the Waterloo Exploration Database using
the group maximum differentiation competition.
