  Image compression has raised widespread interest recently due to its
significant importance for multimedia storage and transmission. Meanwhile, a
reliable image quality assessment (IQA) for compressed images can not only help
to verify the performance of various compression algorithms but also help to
guide the compression optimization in turn. In this paper, we design a
full-reference image quality assessment metric SwinIQA to measure the
perceptual quality of compressed images in a learned Swin distance space. It is
known that the compression artifacts are usually non-uniformly distributed with
diverse distortion types and degrees. To warp the compressed images into the
shared representation space while maintaining the complex distortion
information, we extract the hierarchical feature representations from each
stage of the Swin Transformer. Besides, we utilize cross attention operation to
map the extracted feature representations into a learned Swin distance space.
Experimental results show that the proposed metric achieves higher consistency
with human's perceptual judgment compared with both traditional methods and
learning-based methods on CLIC datasets.
