  The research in image quality assessment (IQA) has a long history, and
significant progress has been made by leveraging recent advances in deep neural
networks (DNNs). Despite high correlation numbers on existing IQA datasets,
DNN-based models may be easily falsified in the group maximum differentiation
(gMAD) competition with strong counterexamples being identified. Here we show
that gMAD examples can be used to improve blind IQA (BIQA) methods.
Specifically, we first pre-train a DNN-based BIQA model using multiple noisy
annotators, and fine-tune it on multiple subject-rated databases of
synthetically distorted images, resulting in a top-performing baseline model.
We then seek pairs of images by comparing the baseline model with a set of
full-reference IQA methods in gMAD. The resulting gMAD examples are most likely
to reveal the relative weaknesses of the baseline, and suggest potential ways
for refinement. We query ground truth quality annotations for the selected
images in a well controlled laboratory environment, and further fine-tune the
baseline on the combination of human-rated images from gMAD and existing
databases. This process may be iterated, enabling active and progressive
fine-tuning from gMAD examples for BIQA. We demonstrate the feasibility of our
active learning scheme on a large-scale unlabeled image set, and show that the
fine-tuned method achieves improved generalizability in gMAD, without
destroying performance on previously trained databases.
