Correcting Diffusion-Based Perceptual Image Compression with Privileged
  End-to-End Decoder
Yiyang Ma Wenhan Yang Jiaying Liu
The images produced by diffusion models can attain excellent perceptual
quality. However, it is challenging for diffusion models to guarantee
distortion, hence the integration of diffusion models and image compression
models still needs more comprehensive explorations. This paper presents a
diffusion-based image compression method that employs a privileged end-to-end
decoder model as correction, which achieves better perceptual quality while
guaranteeing the distortion to an extent. We build a diffusion model and design
a novel paradigm that combines the diffusion model and an end-to-end decoder,
and the latter is responsible for transmitting the privileged information
extracted at the encoder side. Specifically, we theoretically analyze the
reconstruction process of the diffusion models at the encoder side with the
original images being visible. Based on the analysis, we introduce an
end-to-end convolutional decoder to provide a better approximation of the score
function $\nabla_{\mathbf{x}_t}\log p(\mathbf{x}_t)$ at the encoder side and
effectively transmit the combination. Experiments demonstrate the superiority
of our method in both distortion and perception compared with previous
perceptual compression methods.