Efficient Image Compression through Extreme Image Rescaling
Jiancong Chen, Yixuan Li, Peilin Chen, Shiqi Wang, Zhu Li · 2025
In this paper, we propose a generative image compression scheme for extremely low bitrate representation and high visual quality reconstruction. This method decomposes images into ultra-low-resolution thumbnails and text descriptions, achieving high compression rates while maintaining human-perceptible thumbnails for better previewing and understanding. To this end, we integrate an arbitrary-scale image rescaling model with a pre-trained conditional diffusion model, enhancing both rescaling flexibility and visual quality. Specifically, the high-resolution image is downscaled into a thumbnail for transmission or storage, then decoded by upscaling it to its original resolution, followed by a diffusion-based generative process for quality enhancement. To better utilize the generative priors of the pretrained diffusion model, the upscaled images are aligned with the original input in the latent space of the diffusion model. Leveraging these generative priors, thumbnails at extreme scales can be reconstructed to their original resolution with high fidelity and perceptual quality. Additionally, text descriptions extracted from the original image are used to condition the diffusion model, improving semantic consistency in the reconstruction. Extensive experimental results demonstrate that our method can achieve notable compression efficiency and visually pleasing reconstruction results at extremely low bitrates.