Image Quality Score

Image quality assessment (IQA) aims to automatically score the perceptual quality of images, particularly in the absence of a reference image (blind IQA). Current research heavily focuses on leveraging powerful deep learning architectures, including transformers and diffusion models, often incorporating multimodal approaches that combine image and text information to improve accuracy and explainability. These advancements are crucial for various applications, from optimizing image processing algorithms to enhancing user experience in image-based systems, and are driving improvements in both the accuracy and robustness of IQA models. The development of robust and generalizable IQA methods remains a significant challenge, with ongoing efforts to address vulnerabilities and improve performance on diverse, real-world image datasets.