arXiv:2606.06273v1 Announce Type: cross 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 bidi
This research builds on recent advances in diffusion models and language models, demonstrating their applicability to complex engineering challenges like lossless image transmission, which is becoming increasingly critical with data growth.
This development could significantly improve the efficiency and reliability of image data transfer, impacting sectors from telecommunications to scientific research by ensuring perfect fidelity without massive overhead.
The proposed DDM-SSCC framework introduces a novel method for lossless image transmission, potentially surpassing traditional raster-order autoregressive coding in efficiency and robustness.
- · Telecommunications providers
- · Cloud storage companies
- · AI/ML research institutions
- · Data-intensive industries
- · Inefficient image compression algorithms
More efficient and reliable transmission of high-resolution images across networks.
Reduced bandwidth requirements and storage costs for visual data will enable new applications reliant on pixel-perfect imagery.
This could accelerate the development of systems requiring real-time, high-fidelity visual data feeds, leading to advancements in areas like remote sensing and autonomous systems.
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Read at arXiv cs.AI