  Recently, increasing interest has been drawn in exploiting deep convolutional
neural networks (DCNNs) for no-reference image quality assessment (NR-IQA).
Despite of the notable success achieved, there is a broad consensus that
training DCNNs heavily relies on massive annotated data. Unfortunately, IQA is
a typical small sample problem. Therefore, most of the existing DCNN-based IQA
metrics operate based on pre-trained networks. However, these pre-trained
networks are not designed for IQA task, leading to generalization problem when
evaluating different types of distortions. With this motivation, this paper
presents a no-reference IQA metric based on deep meta-learning. The underlying
idea is to learn the meta-knowledge shared by human when evaluating the quality
of images with various distortions, which can then be adapted to unknown
distortions easily. Specifically, we first collect a number of NR-IQA tasks for
different distortions. Then meta-learning is adopted to learn the prior
knowledge shared by diversified distortions. Finally, the quality prior model
is fine-tuned on a target NR-IQA task for quickly obtaining the quality model.
Extensive experiments demonstrate that the proposed metric outperforms the
state-of-the-arts by a large margin. Furthermore, the meta-model learned from
synthetic distortions can also be easily generalized to authentic distortions,
which is highly desired in real-world applications of IQA metrics.
