  Diffusion models dominate the field of image generation, however they have
yet to make major breakthroughs in the field of image compression. Indeed,
while pre-trained diffusion models have been successfully adapted to a wide
variety of downstream tasks, existing work in diffusion-based image compression
require task specific model training, which can be both cumbersome and
limiting. This work addresses this gap by harnessing the image prior learned by
existing pre-trained diffusion models for solving the task of lossy image
compression. This enables the use of the wide variety of publicly-available
models, and avoids the need for training or fine-tuning. Our method, PSC
(Posterior Sampling-based Compression), utilizes zero-shot diffusion-based
posterior samplers. It does so through a novel sequential process inspired by
the active acquisition technique "Adasense" to accumulate informative
measurements of the image. This strategy minimizes uncertainty in the
reconstructed image and allows for construction of an image-adaptive transform
coordinated between both the encoder and decoder. PSC offers a progressive
compression scheme that is both practical and simple to implement. Despite
minimal tuning, and a simple quantization and entropy coding, PSC achieves
competitive results compared to established methods, paving the way for further
exploration of pre-trained diffusion models and posterior samplers for image
compression.
