LMM-driven Semantic Image-Text Coding for Ultra Low-bitrate Learned Image Compression
This addresses compression efficiency for applications requiring minimal bandwidth, though it is incremental as it builds on existing learned image compression frameworks.
The paper tackles ultra low-bitrate learned image compression by using a large multi-modal model to generate and compress captions within a single model, achieving a 41.58% improvement in LPIPS BD-rate compared to existing methods.
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.