Multi-Dimensional Aesthetic Evaluation Model for AI-Generated Visual Images and Its Optimization Strategies
Main Article Content
Abstract
The rapid development of AI image generation technology has created an urgent need for systematic aesthetic evaluation of generated visual content. Existing computer vision assessment methods are often limited to technical image parameters and insufficiently consider multidimensional artistic judgment, texture details, and style consistency. This study develops a multidimensional aesthetic evaluation framework that decomposes image assessment into five core dimensions: composition balance, color harmony, theme expression, detail completeness, and style consistency. An attention-enhanced convolutional neural network is combined with transfer learning and multi-task learning to improve cross-domain generalization under limited labeled data conditions. To address the subjectivity of aesthetic preference and cultural differences, the optimization strategy introduces user-feedback-based online learning and interpretability analysis, enabling evaluation results to combine objective quantitative foundations with adaptability to specific application scenarios. Experimental results show that the proposed multidimensional fusion model achieves a Pearson correlation coefficient of 0.847 with human expert scores, outperforming single-dimension evaluation methods. The model provides technical support for AI-generated image quality control and offers methodological references for visual feature extraction, texture analysis, and electromagnetic imaging evaluation in engineering applications.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
References
Y. Wang, J. Zhang, C. Shen, H. Yu, and S. Luo, “Generative AI aids personalized product aesthetic generation and evaluation based on style themes,” Advanced Engineering Informatics, vol. 68, Art. no. 103756, 2025, doi: 10.1016/J.AEI.2025.103756.
I. Bianchi, E. Branchini, T. Uricchio, and R. Bongelli, “Creativity and aesthetic evaluation of AI-generated artworks: bridging problems and methods from psychology to AI,” Frontiers in Psychology, vol. 16, Art. no. 1648480, 2025, doi: 10.3389/FPSYG.2025.1648480.
J. Hagiwara, Y. Ueda, W. Yoo, and M. Nomura, “Does human–AI collaboration lead to more creative art? Aesthetic evaluation of human-made and AI-generated haiku poetry,” Computers in Human Behavior, vol. 139, Art. no. 107502, 2023, doi: 10.1016/J.CHB.2022.107502.
X. Wu, “Automatic control method of underground gas extraction in coal mine based on simple recurrent neural network,” Computer Informatization and Mechanical System, vol. 7, no. 1, pp. 73–76, 2024, doi: 10.12250/JPCIAMS2024090216.
Z. Meng, J. Shi, H. Dong, T. Liang, and F. Li, “Simulation Study on Risk Warning and Control Mechanism and Transmission Path of Major Disasters and Accidents in Coal Mine Based on Method of Structural Equation,” Academic Journal of Engineering and Technology Science, vol. 6, no. 12, 2023, doi: 10.25236/AJETS.2023.061204.
D. Wang, R. Li, J. Cheng, W. Zheng, Y. Shen, S. Zhao, et al., “Research on the Calculation Model and Control Method of Initial Supporting Force for Temporary Support in the Underground Excavation Roadway of Coal Mine,” Axiomss, vol. 12, no. 10, pp. 948, 2023, doi: 10.3390/axioms12100948.
H. Wen, M. Liu, D. Zhang, Z. Wang, J. Deng, and W. Wang, “HCl formation mechanism in the pyrolysis process of coal mine conveyor belt,” Fuel, vol. 384, Art. no. 133899, 2025, doi: 10.1016/J.FUEL.2024.133899.
Z. Zheng, D. Yang, L. Zeng, and N. Mughees, “A deep learning framework for objective aesthetic evaluation of indoor landscapes using CNN-GNN model,” Scientific reports, vol. 15, Art. no. 40810, 2025, doi: 10.1038/S41598-025-24548-W.
S. M. Kendir and M. Zuhurlu, “Evaluation Large Language Models’ Time Dependent Consistency in Aesthetic Surgery Consultations and Comparison of Their Performance Across Different Clinical Domains,” Aesthetic Plastic Surgery, pp. 1-12, 2025, doi: 10.1007/S00266-025-05308-7.
H. Chen, Y. Wang, W. Li, and B. Xiao, “A multi-granularity facial aesthetic evaluation model based on image-text modality,” Knowledge-Based Systems, vol. 330, no. PA, Art. no. 114502, 2025, doi: 10.1016/J.KNOSYS.2025.114502.
J. Wang, “Evaluation of Aesthetic Quality of Digital Artworks Using Software-assisted and Deep Learning Models,” International Journal of High Speed Electronics and Systems, (prepublish), 2025, doi: 10.1142/S0129156425405753.
M. Li, S. Guo, and J. Chen, “Construction of aesthetic education evaluation model based on K-means clustering analysis in the context of artificial intelligence,” Journal of Computational Methods in Sciences and Engineering, vol. 25, no. 1, pp. 728–743, 2025, doi: 10.1177/14727978251321946.
C. C. Kuo, S. J. Jiang, and M. M. Lin, “Exploring Taiwan’s Landscape Painting Aesthetic Preferences Through Evaluation Grid Method and the Continuous Fuzzy Kano Model,” Journal of Contemporary Educational Research, vol. 7, no. 12, pp. 268–276, 2023, doi: 10.26689/JCER.V7I12.5821.
Y. Yan, X. Yu, J. Liao, L. Liu, J. Gong, and Y. Yang, “Combined navigation system for substation inspection robot based on DGPS/DR multi-sensor fusion,” Journal of Physics: Conference Series, vol. 2728, no. 1, Art. no. 012039, 2024, doi: 10.1088/1742-6596/2728/1/012039.
M. Yang and E. Yang, “Two-stage multi-sensor fusion positioning system with seamless switching for cooperative mobile robot and manipulator system,” International Journal of Intelligent Robotics and Applications, vol. 7, no. 2, pp. 275–290, 2023, doi: 10.1007/S41315-023-00276-0.
Z. Yang, Y. Zhang, H. Li, H. Hao, W. Chen, and W. Zhan, “The Navigation System of a Logistics Inspection Robot Based on Multi-Sensor Fusion in a Complex Storage Environment,” Sensors, vol. 22, no. 20, pp. 7794, 2022, doi: 10.3390/S22207794.
F. L. Cardona, G. A. J. Luna, and R. A. J. Carmona, “Bibliometric Analysis of Intelligent Systems for Early Anomaly Detection in Oil and Gas Contracts: Exploring Recent Progress and Challenges,”Sustainability, vol. 16, no. 11, 2024, doi: 10.3390/SU16114669.
Z. Ren, H. Yang, S. Wang, J. Li, S. X. Huang, and Y. Guo, “Research on multi-functional intelligent ventilator based on UC/OS-III operating system for gas concentration detection,” Journal of Physics: Conference Series, vol. 2246, no. 1, Art. no. 012028, 2022, doi: 10.1088/1742-6596/2246/1/012028.
J. Zhang and S. Cao, “Research on modern art creation trend and its visual expression based on deep learning technology,” Applied Mathematics and Nonlinear Sciences, vol. 9, no. 1, 2024, doi: 10.2478/AMNS-2024-3176.
B. La Rosa, G. Blasilli, R. Bourqui, D. Auber, G. Santucci, and R. Capobianco, “State of the Art of Visual Analytics for eXplainable Deep Learning,” Computer Graphics Forum, vol. 42, no. 1, pp. 319-355, 2023, doi: 10.1111/CGF.14733.