Transformer-based No-Reference Image Quality Assessment via Supervised
  Contrastive Learning
Jinsong Shi Pan Gao Jie Qin
Image Quality Assessment (IQA) has long been a research hotspot in the field
of image processing, especially No-Reference Image Quality Assessment (NR-IQA).
Due to the powerful feature extraction ability, existing Convolution Neural
Network (CNN) and Transformers based NR-IQA methods have achieved considerable
progress. However, they still exhibit limited capability when facing unknown
authentic distortion datasets. To further improve NR-IQA performance, in this
paper, a novel supervised contrastive learning (SCL) and Transformer-based
NR-IQA model SaTQA is proposed. We first train a model on a large-scale
synthetic dataset by SCL (no image subjective score is required) to extract
degradation features of images with various distortion types and levels. To
further extract distortion information from images, we propose a backbone
network incorporating the Multi-Stream Block (MSB) by combining the CNN
inductive bias and Transformer long-term dependence modeling capability.
Finally, we propose the Patch Attention Block (PAB) to obtain the final
distorted image quality score by fusing the degradation features learned from
contrastive learning with the perceptual distortion information extracted by
the backbone network. Experimental results on seven standard IQA datasets show
that SaTQA outperforms the state-of-the-art methods for both synthetic and
authentic datasets. Code is available at
https://github.com/I2-Multimedia-Lab/SaTQA