Diffusion-OAMP for Joint Image Compression and Wireless Transmission
This work provides a training-free framework for joint compression and transmission, which is important for practical communication systems but remains underexplored.
Diffusion-OAMP addresses joint image compression and wireless transmission by embedding a pre-trained diffusion model into the OAMP algorithm, achieving favorable performance against classic methods across varying compression ratios and noise levels.
Joint image compression and wireless transmission remain relatively underexplored compared to generic image restoration, despite its importance in practical communication systems. We formulate this problem under an equivalent linear model, and propose Diffusion-OAMP, a training-free reconstruction framework that embeds a pre-trained diffusion model into the OAMP algorithm. In Diffusion-OAMP, the OAMP linear estimator produces pseudo-AWGN observations, while the diffusion model serves as a nonlinear estimator under an SNR-matching rule. This framework offers a way to incorporate multiple generative priors into OAMP. Experiments with varying compression ratios and noise levels show that Diffusion-OAMP performs favorably against classic methods in the evaluated settings.