Modality Compression

Modality compression aims to reduce the size of multimodal data (e.g., images, text, video) while preserving essential information. Current research focuses on developing efficient compression techniques, often leveraging large multimodal models (like CLIP) and exploring joint coding of different modalities or sequential conditional coding to improve compression ratios and reconstruction quality. This field is significant because it addresses the growing need for efficient storage and transmission of massive multimodal datasets, impacting applications ranging from autonomous driving to ultra-low bitrate image compression and enabling deployment on resource-constrained devices.