  Blind image quality assessment (BIQA) remains challenging due to the
diversity of distortion and image content variation, which complicate the
distortion patterns crossing different scales and aggravate the difficulty of
the regression problem for BIQA. However, existing BIQA methods often fail to
consider multi-scale distortion patterns and image content, and little research
has been done on learning strategies to make the regression model produce
better performance. In this paper, we propose a simple yet effective
Progressive Multi-Task Image Quality Assessment (PMT-IQA) model, which contains
a multi-scale feature extraction module (MS) and a progressive multi-task
learning module (PMT), to help the model learn complex distortion patterns and
better optimize the regression issue to align with the law of human learning
process from easy to hard. To verify the effectiveness of the proposed PMT-IQA
model, we conduct experiments on four widely used public datasets, and the
experimental results indicate that the performance of PMT-IQA is superior to
the comparison approaches, and both MS and PMT modules improve the model's
performance.
