  Image quality assessment (IQA) has long been a fundamental challenge in image
understanding. In recent years, deep learning-based IQA methods have shown
promising performance. However, the lack of large amounts of labeled data in
the IQA field has hindered further advancements in these methods. This paper
introduces DSMix, a novel data augmentation technique specifically designed for
IQA tasks, aiming to overcome this limitation. DSMix leverages the
distortion-induced sensitivity map (DSM) of an image as prior knowledge. It
applies cut and mix operations to diverse categories of synthetic distorted
images, assigning confidence scores to class labels based on the aforementioned
prior knowledge. In the pre-training phase using DSMix-augmented data,
knowledge distillation is employed to enhance the model's ability to extract
semantic features. Experimental results on both synthetic and authentic IQA
datasets demonstrate the significant predictive and generalization performance
achieved by DSMix, without requiring fine-tuning of the full model. Code is
available at \url{https://github.com/I2-Multimedia-Lab/DSMix}.
