Image Complexity

Image complexity, encompassing the inherent intricacy and information density within visual data, is a crucial area of research aiming to quantify and leverage this characteristic for improved computer vision performance. Current efforts focus on developing robust metrics for assessing image complexity, often incorporating deep learning models and techniques like contrastive learning and hierarchical clustering, to better understand how complexity impacts tasks such as segmentation, super-resolution, and object detection. These advancements are significant because accurate complexity assessment can guide model design, optimize resource allocation (e.g., storage and computation), and ultimately enhance the accuracy and efficiency of various computer vision applications across diverse fields, including medical imaging and e-commerce.