Diffusion-OAMP for Joint Image Compression and Wireless Transmission

Popularized by their strong image generation performance, diffusion and related methods for generative modeling have found widespread success in visual media applications. In particular, diffusion methods have enabled new approaches to data compression, where realistic reconstructions can be generated at extremely low bit-rates. This article provides a unifying review of recent diffusion-based methods for generative lossy compression, with a focus on image compression. These methods generally encode the source into an embedding and use a diffusion model to iteratively refine it during decoding, so that the reconstruction approximately follows the true data distribution. The embedding can take various forms and is typically transmitted via an auxiliary entropy model, and recent methods also explore the use of diffusion models themselves for information transmission via channel simulation. We review representative approaches through the lens of rate-distortion-perception theory, highlighting the role of common randomness and connections to inverse problems, and identify open challenges.

Diffusion-based image compression methods, leveraging powerful generative priors, have demonstrated remarkable perceptual quality at ultra-low bitrates. However, adapting modern generative models to image compression often relies on carefully engineered conditioning or auxiliary branches, together with substantial retraining, and these costs grow as the models scale. This motivates an open question: Can stronger generative priors be integrated into compression through a simpler, more extensible design? To answer this, we propose FlowCodec, a streamlined framework that plugs pretrained large-scale text-to-image priors (e.g., Qwen-image-2512 and FLUX.1-dev) into ultra-low-bitrate codecs. FlowCodec decomposes the pipeline into two decoupled stages: (1) Latent Compression, which maps clean latents to bitrate-constrained noisy latents; and (2) Latent Transport, which leverages the pretrained prior to refine the noisy latents toward the clean ones in a single step. Notably, FlowCodec requires neither additional conditioning signals nor auxiliary networks. Furthermore, with lightweight adaptation, it can flexibly support multiple bitrates while keeping the number of trainable parameters below 0.54% of the generative backbone. Experiments show that FlowCodec preserves high visual quality at bitrates below 0.05 bits per pixel. The Qwen-image variant significantly outperforms existing methods in terms of LPIPS and DISTS, while both variants deliver higher PSNR and clearly faster encoding than existing one-step diffusion-based methods, with the FLUX variant also maintaining competitive decoding speed.

DiffC provides a principled way to reuse pre-trained diffusion models for lossy compression, but its encoding and decoding procedures remain slow because they require many discretized forward and reverse steps. We study whether few-step generative models -- Rectified Flow, Consistency Trajectory Models (CTM), and MeanFlow -- can be cast as codecs within the same reverse channel coding (RCC) framework. The main challenge is that RCC requires posterior and shared distribution parameters, whereas these models do not explicitly parameterize intermediate conditional distributions. For Rectified Flow and MeanFlow, we use the equivalence between velocity parameterization and diffusion-style denoising parameterization to derive the quantities required by RCC. For CTM, which is distilled from EDM, we adopt the EDM noise parameterization together with local Gaussian approximations of the sender and shared distributions at intermediate states. This yields a proof-of-concept probabilistic formulation that enables compression with pre-trained few-step generative models without retraining. On low-resolution benchmarks, the resulting codecs reduce encoding and decoding time and improve realism in the low-bit-rate regime.