Image Quality Assessment

Image Quality Assessment (IQA) aims to objectively measure the perceived quality of images, often by correlating automated metrics with human judgments. Current research focuses on developing robust, training-efficient methods, particularly for no-reference IQA (NR-IQA), employing architectures like transformers and convolutional neural networks, often incorporating techniques like contrastive learning and vision-language models. These advancements are crucial for various applications, including image processing, medical imaging, and the evaluation of AI-generated content, improving the reliability and efficiency of computer vision systems.