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.

提出了一种基于参考的可变卷积和补丁级别的注意力模块以及修改的残差块构成的Region-Adaptive Deformable Network(RADN)，用于在基于GAN的失真中提高IQA网络的性能，并在NTIRE 2021感知图像质量评估挑战数据集上获得了出色的性能表现并在测试阶段中获得第四名。

区域自适应变形网络图像质量评估