C$^{2}$MT: A Credible and Class-Aware Multi-Task Transformer for SR-IQA
Hui Li, Kaibing Zhang, Zhenxing Niu, Hongyu Shi · IEEE Signal Processing Letters · 2022
In this letter a novel credible and class-aware multi-task transformer abbreviated as C$^{2}$MT for SRIQA, is proposed. In the proposed C$^{2}$MT, a quality-aware task for the quality prediction and the other class-aware task for the classification of SR algorithms are jointly framed to mine mutual information between the quality of SR images and the class of SR algorithms for more discriminative perceptual representation. In the class-aware task, we develop a supervised contrastive learning strategy to learn embedding perceived features related to a class-specific SR algorithm. While in the other quality-aware task, we employ a novel credible pseudo quality label generation strategy to actively adjust the quality labels by ranking the pair-wise consistency between the predicted quality scores and subjective perceptual scores but keep the image-level quality labels unchanged. The developed supervised contrastive learning and the variant of active learning strategies benefit learning a more consistent quality predictor for SR images. Experiment results indicate that our proposed C$^{2}$MT achieves state-of-the-art results on five popular SRIQA benchmark databases.