Image Assessment

Image assessment aims to automatically evaluate image quality and aesthetics, mirroring human perception. Current research focuses on developing unified models that handle both quality and aesthetic assessment simultaneously, often leveraging multimodal learning with vision-language models and incorporating multi-scale features and attention mechanisms for improved accuracy. These advancements are significant for various applications, including improving image processing, detecting image manipulations, and enhancing the efficiency of medical image analysis by reducing the need for extensive manual annotation.