  Diffusion probabilistic models have recently achieved remarkable success in
generating high-quality images. However, balancing high perceptual quality and
low distortion remains challenging in image compression applications. To
address this issue, we propose an efficient Uncertainty-Guided image
compression approach with wavelet Diffusion (UGDiff). Our approach focuses on
high frequency compression via the wavelet transform, since high frequency
components are crucial for reconstructing image details. We introduce a wavelet
conditional diffusion model for high frequency prediction, followed by a
residual codec that compresses and transmits prediction residuals to the
decoder. This diffusion prediction-then-residual compression paradigm
effectively addresses the low fidelity issue common in direct reconstructions
by existing diffusion models. Considering the uncertainty from the random
sampling of the diffusion model, we further design an uncertainty-weighted
rate-distortion (R-D) loss tailored for residual compression, providing a more
rational trade-off between rate and distortion. Comprehensive experiments on
two benchmark datasets validate the effectiveness of UGDiff, surpassing
state-of-the-art image compression methods in R-D performance, perceptual
quality, subjective quality, and inference time. Our code is available at:
https://github.com/hejiaxiang1/Wavelet-Diffusion/tree/main
