No-reference super-resolution image quality assessment based on upscaling-factor aware contrastive learning

The quality of super-resolution images is not only affected by the reconstruction algorithm, but also there are some differences in the quality degradation levels of the reconstructed images under different upscaling-factors. However, the existing no-reference super-resolution image quality assessment (NR-SRIQA) methods mainly focus on the visual features of super-resolution images, ignoring the available upscaling-factor information. An upscaling-factor aware contrastive learning (UFACL) method is proposed. The network structure is divided into a upscaling-factor recognition branch and a quality score branch. The upscaling-factor recognition branch starts from the dataset, and takes the super-resolution images of different upscaling-factors as positive and negative samples of each other. Contrastive learning is introduced to complete the classification task, so as to improve the expression ability of effective features. In the quality score branch, a frequency domain attention module (FDAM) is designed, which considers both global information and channel information. At the same time, this branch uses inverted residuals blocks (IRB) to reduce the calculation amount of the model, which ensures the accuracy of quality score prediction and improves the training efficiency of the model in the training process. Experimental results show that the proposed UFACL can achieve better consistency with subjective perceived quality.