Visual Complexity

Visual complexity, the inherent difficulty in processing visual information, is a multifaceted research area aiming to define, quantify, and understand how humans and machines perceive and interpret complex scenes. Current research focuses on developing computational measures of complexity, often leveraging multi-scale analysis and deep learning models (including large vision-language models and self-attention mechanisms) to analyze image structure and predict subjective complexity ratings. These efforts are crucial for improving the performance of AI systems in tasks requiring visual understanding, such as medical image analysis, video comprehension, and robotic navigation, and for advancing our understanding of human visual perception and cognition.