  Image quality assessment (IQA) aims to assess the perceptual quality of
images. The outputs of the IQA algorithms are expected to be consistent with
human subjective perception. In image restoration and enhancement tasks, images
generated by generative adversarial networks (GAN) can achieve better visual
performance than traditional CNN-generated images, although they have spatial
shift and texture noise. Unfortunately, the existing IQA methods have
unsatisfactory performance on the GAN-based distortion partially because of
their low tolerance to spatial misalignment. To this end, we propose the
reference-oriented deformable convolution, which can improve the performance of
an IQA network on GAN-based distortion by adaptively considering this
misalignment. We further propose a patch-level attention module to enhance the
interaction among different patch regions, which are processed independently in
previous patch-based methods. The modified residual block is also proposed by
applying modifications to the classic residual block to construct a
patch-region-based baseline called WResNet. Equipping this baseline with the
two proposed modules, we further propose Region-Adaptive Deformable Network
(RADN). The experiment results on the NTIRE 2021 Perceptual Image Quality
Assessment Challenge dataset show the superior performance of RADN, and the
ensemble approach won fourth place in the final testing phase of the challenge.
Code is available at https://github.com/IIGROUP/RADN.
