Enhancing the Rate-Distortion-Perception Flexibility of Learned Image
  Codecs with Conditional Diffusion Decoders
Daniele Mari Simone Milani
Learned image compression codecs have recently achieved impressive
compression performances surpassing the most efficient image coding
architectures. However, most approaches are trained to minimize rate and
distortion which often leads to unsatisfactory visual results at low bitrates
since perceptual metrics are not taken into account. In this paper, we show
that conditional diffusion models can lead to promising results in the
generative compression task when used as a decoder, and that, given a
compressed representation, they allow creating new tradeoff points between
distortion and perception at the decoder side based on the sampling method.