  Image codecs are typically optimized to trade-off bitrate \vs distortion
metrics. At low bitrates, this leads to compression artefacts which are easily
perceptible, even when training with perceptual or adversarial losses. To
improve image quality and remove dependency on the bitrate, we propose to
decode with iterative diffusion models. We condition the decoding process on a
vector-quantized image representation, as well as a global image description to
provide additional context. We dub our model PerCo for 'perceptual
compression', and compare it to state-of-the-art codecs at rates from 0.1 down
to 0.003 bits per pixel. The latter rate is more than an order of magnitude
smaller than those considered in most prior work, compressing a 512x768 Kodak
image with less than 153 bytes. Despite this ultra-low bitrate, our approach
maintains the ability to reconstruct realistic images. We find that our model
leads to reconstructions with state-of-the-art visual quality as measured by
FID and KID. As predicted by rate-distortion-perception theory, visual quality
is less dependent on the bitrate than previous methods.
