With the evolution of storage and communication protocols, ultra-low bitrate image compression has become a highly demanding topic. However, existing compression algorithms must sacrifice either consistency with the ground truth or perceptual quality at ultra-low bitrate. In recent years, the rapid development of the Large Multimodal Model (LMM) has made it possible to balance these two goals. To solve this problem, this paper proposes a method called Multimodal Image Semantic Compression (MISC), which consists of an LMM encoder for extracting the semantic information of the image, a map encoder to locate the region corresponding to the semantic, an image encoder generates an extremely compressed bitstream, and a decoder reconstructs the image based on the above information. Experimental results show that our proposed MISC is suitable for compressing both traditional Natural Sense Images (NSIs) and emerging AI-Generated Images (AIGIs) content. It can achieve optimal consistency and perception results while saving 50% bitrate, which has strong potential applications in the next generation of storage and communication. The code will be released on https://github.com/lcysyzxdxc/MISC.
@article{arxiv.2402.16749,
title = {MISC: Ultra-low Bitrate Image Semantic Compression Driven by Large Multimodal Model},
author = {Chunyi Li and Guo Lu and Donghui Feng and Haoning Wu and Zicheng Zhang and Xiaohong Liu and Guangtao Zhai and Weisi Lin and Wenjun Zhang},
journal= {arXiv preprint arXiv:2402.16749},
year = {2024}
}