Accurately assessing image complexity (IC) is essential for many vision tasks, yet existing approaches rely almost exclusively on visual features and therefore fail to capture the high-level semantics that humans often use when judging complexity. We introduce a multimodal perspective for IC modeling by integrating visual representations with caption-derived textual semantics. This integration enriches the representational space and provides complementary structural cues that are difficult to infer from vision alone. From an information theoretic and representation viewpoint, we offer an idealized analysis suggesting how semantic guidance can regularize the hypothesis space and support more stable generalization. We propose D2S (Describe-to-Score), a text-guided framework that uses caption-derived semantics only during training to regularize visual complexity modeling, while preserving a vision-only inference pipeline with no additional multimodal overhead at inference. Concretely, D2S transfers semantic structure into the visual branch through feature alignment and entropy distribution alignment, encouraging the visual encoder to internalize complexity-relevant semantic regularities. Experiments show that D2S achieves state-of-the-art performance on the IC9600 benchmark and remains competitive on no-reference image quality assessment (NR-IQA) tasks. Additional analyses further clarify the sample-imbalance issue in the small samples training setting and the distribution-shift limitations observed in cross-dataset transfer. Code is available at: https://github.com/xauat-liushipeng/D2S.

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