Re-IQA: Unsupervised Learning for Image Quality Assessment in the Wild
Re-IQA: Unsupervised Learning for Image Quality Assessment in the Wild
Automatic Perceptual Image Quality Assessment is a challenging problem that impacts billions of internet, and social media users daily. To advance research in this field, we propose a Mixture of Experts approach to train two separate encoders to learn high-level content and low-level image quality features in an unsupervised setting. …