# Diffusion-OAMP for Joint Image Compression and Wireless Transmission Source: [Diffusion-OAMP for Joint Image Compression and Wireless Transmission]() Collection index: [llms.txt]() Data updated: 2026-05-02T05:00:10.851Z Authors: Wentao Hou, Yimin Bai, Zelei Luo, Jiadong Hong, Lei Liu Published: 2026-04-30T14:49:31.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Joint image compression and wireless transmission on CelebA TDL-A channels](): Joint image compression and wireless transmission; CelebA TDL-A channels; PSNR: 33.15. - [Image Compression on CelebA (test)](): Image Compression; CelebA (test); PSNR: 33.34. ## Original sources and implementation [Original paper]() [PDF]()