LMM-driven Semantic Image-Text Coding for Ultra Low-bitrate Learned Image Compression

Autor:	Murai, Shimon, Sun, Heming, Katto, Jiro
Rok vydání:	2024
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	Supported by powerful generative models, low-bitrate learned image compression (LIC) models utilizing perceptual metrics have become feasible. Some of the most advanced models achieve high compression rates and superior perceptual quality by using image captions as sub-information. This paper demonstrates that using a large multi-modal model (LMM), it is possible to generate captions and compress them within a single model. We also propose a novel semantic-perceptual-oriented fine-tuning method applicable to any LIC network, resulting in a 41.58\% improvement in LPIPS BD-rate compared to existing methods. Our implementation and pre-trained weights are available at https://github.com/tokkiwa/ImageTextCoding. Comment: IEEE VCIP 2024 poster
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2411.13033 Zobrazit plný text záznamu View this record from Arxiv