Steering One-Step Diffusion Model with Fidelity-Rich Decoder for Fast
  Image Compression
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.
6895e84ae78b624607aa96a0
Zheng Chen
6895e84ae78b624607aa96a1
Mingde Zhou
6895e84ae78b624607aa96a2
Jinpei Guo
6895e84ae78b624607aa96a3
Jiale Yuan
6895e84ae78b624607aa96a4
Yifei Ji
6895e84ae78b624607aa96a5
Yulun Zhang
Steering One-Step Diffusion Model with Fidelity-Rich Decoder for Fast
  Image Compression
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.
SODEC, a single-step diffusion image compression model, enhances decoding speed and fidelity by using a pre-trained VAE and a fidelity guidance module.
diffusion-based image compression decoding latency generative priors single-step diffusion VAE-based model iterative denoising single-step decoding fidelity guidance module rate annealing rate-distortion-perception performance