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Found 2 papers, 2 papers with code
Transformer-based No-Reference Image Quality Assessment via Supervised Contrastive Learning
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.
Blind Image Quality Assessment via Transformer Predicted Error Map and Perceptual Quality Token
Specifically, we firstly generate the predicted error map by pre-training one model consisting of a Transformer encoder and decoder, in which the objective difference between the distorted and the reference images is used as supervision.
Blind Image Quality Assessment
No-Reference Image Quality Assessment
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