  Transferring large amount of high resolution images over limited bandwidth is
an important but very challenging task. Compressing images using extremely low
bitrates (<0.1 bpp) has been studied but it often results in low quality images
of heavy artifacts due to the strong constraint in the number of bits available
for the compressed data. It is often said that a picture is worth a thousand
words but on the other hand, language is very powerful in capturing the essence
of an image using short descriptions. With the recent success of diffusion
models for text-to-image generation, we propose a generative image compression
method that demonstrates the potential of saving an image as a short text
embedding which in turn can be used to generate high-fidelity images which is
equivalent to the original one perceptually. For a given image, its
corresponding text embedding is learned using the same optimization process as
the text-to-image diffusion model itself, using a learnable text embedding as
input after bypassing the original transformer. The optimization is applied
together with a learning compression model to achieve extreme compression of
low bitrates <0.1 bpp. Based on our experiments measured by a comprehensive set
of image quality metrics, our method outperforms the other state-of-the-art
deep learning methods in terms of both perceptual quality and diversity.
