  Existing blind image quality assessment (BIQA) methods are mostly designed in
a disposable way and cannot evolve with unseen distortions adaptively, which
greatly limits the deployment and application of BIQA models in real-world
scenarios. To address this problem, we propose a novel Lifelong blind Image
Quality Assessment (LIQA) approach, targeting to achieve the lifelong learning
of BIQA. Without accessing to previous training data, our proposed LIQA can not
only learn new distortions, but also mitigate the catastrophic forgetting of
seen distortions. Specifically, we adopt the Split-and-Merge distillation
strategy to train a single-head network that makes task-agnostic predictions.
In the split stage, we first employ a distortion-specific generator to obtain
the pseudo features of each seen distortion. Then, we use an auxiliary
multi-head regression network to generate the predicted quality of each seen
distortion. In the merge stage, we replay the pseudo features paired with
pseudo labels to distill the knowledge of multiple heads, which can build the
final regressed single head. Experimental results demonstrate that the proposed
LIQA method can handle the continuous shifts of different distortion types and
even datasets. More importantly, our LIQA model can achieve stable performance
even if the task sequence is long.
