Reference Image Quality Assessment

Reference image quality assessment (IQA) aims to automatically evaluate the perceptual quality of images, either with (full-reference) or without (no-reference) a pristine reference image. Current research heavily emphasizes no-reference IQA, focusing on developing lightweight, efficient deep learning models (often employing transformers and convolutional neural networks) that accurately predict human judgments of image quality, even on high-resolution images and mobile devices. These advancements are crucial for applications ranging from automated image selection and enhancement to optimizing image compression and improving the user experience in various image-based technologies.