Journal of Engineering, Project, and Production Management, 2026, 16(7), 2026-320
Generation and Perception: A Computational Evaluation Method for Visual Quality and Emotional Impact in AI Artworks
Lecturer, Shanghai
Publishing and Printing College, Shanghai, 200093, China,
E-mail: hengjugang01@163.com
Project Management
Received March 10, 2026; revised April 13, 2026; June 1, 2026; August 5, 2026; accepted August 17, 2026
Available online August 30, 2026
Abstract: The rapid advancement of generative artificial intelligence has enabled significant breakthroughs in the visual realism and artistic expressiveness of AI-generated artworks. However, challenges persist in objectively evaluating their visual quality and emotional impact. Existing evaluation methods either focus on underlying image quality or rely on subjective user surveys, lacking a unified computational framework. This paper proposes a bimodal, multi-dimensional, and interpretable computational evaluation method. The Visual Quality and Emotion (VQ-Emo) framework is designed to jointly optimize visual quality scores and multidimensional emotional predictions. The framework comprises three core modules: a visual quality assessment module (a multi-branch convolutional neural network incorporating style perception, quantifying composition, color, texture, and lighting); an emotional impact computation module (a 20-dimensional multi-label emotion classifier based on the VAWE emotion model), and a visual-emotion association module (using attention mechanisms to identify emotion-driven visual regions). This model was trained and validated on the AGIQA-1K, ArtEmis, and self-constructed VAWE-Art datasets. Experimental results demonstrate that VQ-Emo outperforms existing methods in both visual quality assessment and emotion recognition, achieving an SRCC of 0.912 and a mAP of 0.678. Key contributions include proposing a multi-task learning framework for jointly optimizing visual quality and emotion, establishing the inaugural VAWE-Art dataset comprising 5,000 AI-generated images with 20-dimensional emotional annotations. This reveals quantifiable correlations between visual features and emotional responses across diverse artistic styles and provides computational foundations for emotion-controllable generative art systems.
Keywords: AI-generated art, image quality assessment, affective computing, multimodal learning, explainable artificial intelligence.
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Citation: Gang, H. (2026). Generation and Perception: A Computational Evaluation Method for Visual Quality and Emotional Impact in AI Artworks. Journal of Engineering, Project, and Production Management, 16(7), 2026-320.
