  The explosive growth of image data facilitates the fast development of image
processing and computer vision methods for emerging visual applications,
meanwhile introducing novel distortions to the processed images. This poses a
grand challenge to existing blind image quality assessment (BIQA) models,
failing to continually adapt to such subpopulation shift. Recent work suggests
training BIQA methods on the combination of all available human-rated IQA
datasets. However, this type of approach is not scalable to a large number of
datasets, and is cumbersome to incorporate a newly created dataset as well. In
this paper, we formulate continual learning for BIQA, where a model learns
continually from a stream of IQA datasets, building on what was learned from
previously seen data. We first identify five desiderata in the new setting with
a measure to quantify the plasticity-stability trade-off. We then propose a
simple yet effective method for learning BIQA models continually. Specifically,
based on a shared backbone network, we add a prediction head for a new dataset,
and enforce a regularizer to allow all prediction heads to evolve with new data
while being resistant to catastrophic forgetting of old data. We compute the
quality score by an adaptive weighted summation of estimates from all
prediction heads. Extensive experiments demonstrate the promise of the proposed
continual learning method in comparison to standard training techniques for
BIQA.
