Distributed Image Compression with Multimodal Side Information at Extremely Low Bitrates
Researchers have developed a new Multimodal Distributed Image Compression (MDIC) framework designed to improve image reconstruction quality at extremely low bitrates. This novel approach uniquely utilizes side information in a multimodal fashion, incorporating both textual and visual data to preserve fine-grained local details and enhance global perceptual quality. The framework employs a text-to-image diffusion-based decoder conditioned on textual side information and a feature-mask generator to better exploit visual side information, leading to state-of-the-art results on benchmark datasets. AI
IMPACT This research could enable higher quality image transmission in bandwidth-constrained environments, potentially impacting applications like remote sensing and multi-view video conferencing.