  In recent years, Artificial Intelligence Generated Content (AIGC) has gained
widespread attention beyond the computer science community. Due to various
issues arising from continuous creation of AI-generated images (AIGI), AIGC
image quality assessment (AIGCIQA), which aims to evaluate the quality of AIGIs
from human perception perspectives, has emerged as a novel topic in the field
of computer vision. However, most existing AIGCIQA methods directly regress
predicted scores from a single generated image, overlooking the inherent
differences among AIGIs and scores. Additionally, operations like resizing and
cropping may cause global geometric distortions and information loss, thus
limiting the performance of models. To address these issues, we propose a
patches sampling-based contrastive regression (PSCR) framework. We suggest
introducing a contrastive regression framework to leverage differences among
various generated images for learning a better representation space. In this
space, differences and score rankings among images can be measured by their
relative scores. By selecting exemplar AIGIs as references, we also overcome
the limitations of previous models that could not utilize reference images on
the no-reference image databases. To avoid geometric distortions and
information loss in image inputs, we further propose a patches sampling
strategy. To demonstrate the effectiveness of our proposed PSCR framework, we
conduct extensive experiments on three mainstream AIGCIQA databases including
AGIQA-1K, AGIQA-3K and AIGCIQA2023. The results show significant improvements
in model performance with the introduction of our proposed PSCR framework. Code
will be available at \url{https://github.com/jiquan123/PSCR}.
