Towards Robust Text-Guided Image Compression Under Modality Missing

Text-guided image compression aims to enhance the perceptual quality of reconstructed images by leveraging textual semantic information. However, existing methods struggle to effectively integrate text information and suffer from significant performance degradation when text is unavailable. To address these issues, we propose a Robust Text-Guided Image Compression (RobustTGIC) network that fully utilizes text semantics when available and mitigates performance loss when absent. Specifically, we introduce a Dual-Dimensional Text Modulation (DDTM) module to enhance perceptual quality by accurately fusing textual information. Building on this, we further propose an Intermediate Feature Modulation (IFM) module, which compensates for missing semantics through lightweight adaptation, improving robustness. Experimental results indicate that, at low bitrates (e.g., 0.07 bpp), our method achieves superior perceptual quality reconstruction while significantly reducing bitrates (e.g., 0.5× HiFiC and 0.4× Bpg). Moreover, our method effectively mitigates performance degradation caused by missing text with a parameter increase of less than 1% of the total parameters.