Image Quality Assessment with Transformers and Multi-Metric Fusion Modules

Image quality assessment is crucial for low-level vision tasks such as compression, super-resolution, denoising and etc. It guides researchers how to design networks, design loss functions, and decide the optimization direction of networks. A good quality assessment metric should comform to people's subjective feelings as much as possible. Traditional PSNR and MS-SSIM have more and more obvious shortcomings in quality evaluation with the popularity of GANs. Inspired by metrics such as LPIPS, IQT, etc., we decided to design a metric that is learned by the network itself. In this paper, we use a ConvNeXt-Tiny network to extract features and calculate nonlinear residuals between reference images and distorted images. We feed residuals into transformers to compare the degree of distortion. In addition, we use multi-metric fusion to improve the performance of our network. Our model achieves 0.780 accuracy on CLIC validation set. Our code is available at https://github.com/JiangWeibeta/IQA-TMFM.