  Blind Image Quality Assessment (BIQA) is susceptible to poor transferability
when the distribution shift occurs, e.g., from synthesis degradation to
authentic degradation. To mitigate this, some studies have attempted to design
unsupervised domain adaptation (UDA) based schemes for BIQA, which intends to
eliminate the domain shift through adversarial-based feature alignment.
However, the feature alignment is usually taken at the low-frequency space of
features since the global average pooling operation. This ignores the
transferable perception knowledge in other frequency components and causes the
sub-optimal solution for the UDA of BIQA. To overcome this, from a novel
frequency perspective, we propose an effective alignment strategy, i.e.,
Frequency Alignment (dubbed FreqAlign), to excavate the perception-oriented
transferability of BIQA in the frequency space.
  Concretely, we study what frequency components of features are more proper
for perception-oriented alignment. Based on this, we propose to improve the
perception-oriented transferability of BIQA by performing feature frequency
decomposition and selecting the frequency components that contained the most
transferable perception knowledge for alignment. To achieve a stable and
effective frequency selection, we further propose the frequency movement with a
sliding window to find the optimal frequencies for alignment, which is composed
of three strategies, i.e., warm up with pre-training, frequency movement-based
selection, and perturbation-based finetuning. Extensive experiments under
different domain adaptation settings of BIQA have validated the effectiveness
of our proposed method. The code will be released at
https://github.com/lixinustc/Openworld-IQA.
