Aesthetic Evaluation of Visual Perception in AI-Generated Short Films via Integrated Support Vector Machine and Attention Mechanism

With the rise of AI-generated content in creative fields like film and animation, assessing its visual perception quality has become a major challenge. Traditional evaluation methods often fall short. This study presents ZCASNet, a new hybrid model for automated evaluation of AI-generated short films based on several significant indicators like visual coherence, scene rationality, detail reality, temporal consistency. In ZCASNet framework, Convolutional Neural Networks (CNN), ResNet50, is applied to extract spatial features from video frames. Multi-Head Attention (MHA) captures semantic and temporal relationships across frames. The Zebra Optimization Algorithm (ZOA) enhances the CNN-MHA architecture by refining hyperparameters to improve its performance. Subsequently, a Support Vector Machine (SVM) is employed to classify the optimized features. Our experiments show that ZCASNet outperforms baseline models, including CNN-only, CNN with MHA, Transformer models, and standard SVM. ZCASNet achieves an accuracy of 92.4%, demonstrating notable performance. Ablation studies confirm the importance of the RBF kernel function and each module in the model architecture. Overall, ZCASNet offers an effective and interpretable solution for evaluation in AI-generated video. It has potential uses in content recommendation, AI-assisted filmmaking, and quality control in multimedia applications.