This research paper presents an ability decomposition framework and a difficulty quantification methodology for visual tasks, aiming to advance systematic evaluation approaches for artificial general intelligence (AGI). By analyzing the intrinsic structure of visual task capabilities, the study seeks to provide reliable measurement tools for assessing AGI systems. The framework is validated across multiple visual tasks and highlights significant implications for the development and evaluation of next-generation intelligent systems.