Image Aesthetic Assessment (IAA) has attracted increasing attention recently but it is still challenging due to its high abstraction and complexity. In this paper, recent advancements in IAA are explored, emphasizing the goal, complexity, and critical role this task plays in improving visual content. Insights from our recent studies are combined to present a unified perspective on the state of IAA, focusing on methods relying on the use of genetic algorithms, language-based understanding, and composition-attribute guidance. These methods are examined for their potential in practical applications like content selection and quality enhancement, such as autocropping. The discussion concludes with an overview of the challenges and future directions in this field.