Emotion matching prediction between music and video segments is essential for intelligent mobile sensing systems, where multimodal affective cues collected from smart devices must be jointly analyzed for context-aware media understanding. However, traditional approaches relying on single-modality feature extraction struggle to capture complex cross-modal dependencies, resulting in a gap between low-level audiovisual signals and high-level affective semantics. To address these challenges, a dual-driven framework that integrates perceptual characteristics with objective feature representations is proposed for audiovisual affective matching prediction. The framework incorporates fine-grained affective states of audiovisual data to better characterize cross-modal correlations from an emotional distribution perspective. Moreover, a decoupled Deep Canonical Correlation Analysis approach is developed, incorporating discriminative sample-pairing criteria (matched/mismatched data discrimination) and separate modality-specific component extractors, which dynamically refine the feature projection space. To further enhance multimodal feature interaction, an Attention and Feature-Consistency-Loss-Based Multi-Task Learning Network is proposed. In addition, a feature-consistency loss function is introduced to impose joint constraints across dual semantic embeddings, ensuring both affective consistency and matching accuracy. Experiments on a self-collected benchmark dataset demonstrate that the proposed method achieves a mean absolute error of 0.109 in music–video matching score prediction, significantly outperforming existing approaches.

This article investigates the impact of visual color perception on fine-grained emotion prediction in videos, analyzing the contribution of color perception features in fine-grained emotion prediction. A total of 20 subjects were involved in this experiment. First, 10 subjects conducted a fine-grained emotional subjective evaluation experiment on 50 video clips. Then, another 10 subjects conducted a subjective evaluation experiment of color perception annotation for these 50 video clips. On this basis, the correlation and mechanism between color perceptual features and fine-grained emotions were analyzed. Finally, a fine-grained emotion prediction model was established based on the combination of objective features and color perceptual features. It was also observed that, compared to using only objective features, incorporating perceptual features enhanced the model’s accuracy. This article also compared the importance of different perceptual features for each emotion and explained the mechanism between color perception and fine-grained emotions. By selecting the top 24 important features to predict emotions, the association between perceptual features and emotions was more effectively captured.