Steering One-Step Diffusion Model with Fidelity-Rich Decoder for Fast
  Image Compression
Authors: name: Zheng Chen, name: Mingde Zhou, name: Jinpei Guo, name: Jiale Yuan, name: Yifei Ji, name: Yulun Zhang

Diffusion-based image compression has demonstrated impressive perceptual
performance. However, it suffers from two critical drawbacks: (1) excessive
decoding latency due to multi-step sampling, and (2) poor fidelity resulting
from over-reliance on generative priors. To address these issues, we propose
SODEC, a novel single-step diffusion image compression model. We argue that in
image compression, a sufficiently informative latent renders multi-step
refinement unnecessary. Based on this insight, we leverage a pre-trained
VAE-based model to produce latents with rich information, and replace the
iterative denoising process with a single-step decoding. Meanwhile, to improve
fidelity, we introduce the fidelity guidance module, encouraging output that is
faithful to the original image. Furthermore, we design the rate annealing
training strategy to enable effective training under extremely low bitrates.
Extensive experiments show that SODEC significantly outperforms existing
methods, achieving superior rate-distortion-perception performance. Moreover,
compared to previous diffusion-based compression models, SODEC improves
decoding speed by more than 20times. Code is released at:
https://github.com/zhengchen1999/SODEC.