Adapting Diffusion Language Models for Lossless Pixel-Level Image Transmission

• URL: http://arxiv.org/abs/2606.06273v1
• Date: Thu, 04 Jun 2026 15:14:31 GMT
• Title: Adapting Diffusion Language Models for Lossless Pixel-Level Image Transmission
• Abstract summary: Lossless pixel-level image transmission is a fundamental regime beyond semantic communications.<n>This paper proposes a discrete symbol-diffusion-model-based separate source-channel coding framework for image transmission.
• Score: 13.755711145996592
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Lossless pixel-level image transmission is a fundamental regime beyond semantic communications, because exact recovery requires both accurate symbol probability modeling and reliable delivery over noisy channels. This paper proposes DDM-SSCC, a discrete-diffusion-model-based separate source-channel coding framework for lossless image transmission. Different from raster-order autoregressive coding, the proposed source codec adapts a diffusion language model to pixel-token restoration and performs synchronized reverse arithmetic coding under bidirectional attention, allowing multiple masked tokens to be coded within one reverse denoising step. This progressive restoration process also yields a more favorable source representation for noisy transmission, since newly restored tokens can serve as bidirectional context in subsequent denoising steps. To bridge the gap between generation-oriented masked denoising and lossless arithmetic coding, we further introduce a Halton-guided denoising order, a mask-ratio-aware cosine schedule, and a lightweight temperature calibration module. These designs respectively improve spatial coverage, adapt the denoising pace to context reliability, and calibrate the probability tables used by arithmetic coding. Experiments on CIFAR10, DIV2K-LR-X4, and Kodak over additive white Gaussian noise and Rayleigh fading channels show that DDM-SSCC achieves better exact-recovery performance than representative lossless and semantic communication baselines, while ablation studies verify the effectiveness of the proposed denoising order, schedule, and calibration modules.

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