  Existing learning-based methods for blind image quality assessment (BIQA) are
heavily dependent on large amounts of annotated training data, and usually
suffer from a severe performance degradation when encountering the
domain/distribution shift problem. Thanks to the development of unsupervised
domain adaptation (UDA), some works attempt to transfer the knowledge from a
label-sufficient source domain to a label-free target domain under domain shift
with UDA. However, it requires the coexistence of source and target data, which
might be impractical for source data due to the privacy or storage issues. In
this paper, we take the first step towards the source-free unsupervised domain
adaptation (SFUDA) in a simple yet efficient manner for BIQA to tackle the
domain shift without access to the source data. Specifically, we cast the
quality assessment task as a rating distribution prediction problem. Based on
the intrinsic properties of BIQA, we present a group of well-designed
self-supervised objectives to guide the adaptation of the BN affine parameters
towards the target domain. Among them, minimizing the prediction entropy and
maximizing the batch prediction diversity aim to encourage more confident
results while avoiding the trivial solution. Besides, based on the observation
that the IQA rating distribution of single image follows the Gaussian
distribution, we apply Gaussian regularization to the predicted rating
distribution to make it more consistent with the nature of human scoring.
Extensive experimental results under cross-domain scenarios demonstrated the
effectiveness of our proposed method to mitigate the domain shift.
