Image aesthetic attribute assessment provides explainable outputs for Image Aesthetic Quality Assessment (IAQA), evaluating attributes such as rule of thirds, symmetry, and lighting. These attributes are context-dependent, as their importance varies across different photography scenes. However, most attribute-based IAQA methods remain scene-agnostic, limiting their ability to model scene–attribute dependencies. We propose a scene-aware IAQA model based on a vision transformer that extracts multi-level features and integrates learned scene embeddings within a two-tower module to capture both general and scene-specific patterns, with adaptive gating for context-aware fusion. Experimental results show improved correlation for both overall score and attribute prediction, outperforming state-of-the-art attribute-based IAQA methods.

Image Aesthetic Quality Assessment (IAQA) spans applications such as the fashion industry, AI-generated content, product design, and e-commerce. Recent deep learning advancements have been employed to evaluate image aesthetic quality. A few surveys have been conducted on IAQA models; however, details of recent deep learning models and challenges have not been fully mentioned. This article aims to fill these gaps by providing a review of deep learning IAQA over the past decade, based on input, process, and output phases. Methodologies for deep learning-based IAQA can be categorized into general and task-specific approaches, depending on the type and diversity of input images. The processing phase involves considerations related to network architecture, learning structures, and feature extraction methods. The output phase generates results such as scoring, distribution, attributes, and description. Despite achieving a maximum accuracy of 91.5%, further improvements in deep learning models are still required. Our study highlights several challenges, including adapting models for task-specific methodology, accounting for environmental factors influencing aesthetics, the lack of substantial datasets with appropriate labels, imbalanced data, preserving image aspect ratio and integrity in network architecture design, and the need for explainable AI to understand the causative factors behind aesthetic judgments.