DiffCom: Channel Received Signal is a Natural Condition to Guide Diffusion Posterior Sampling [pdf]
Here is the implementation of the paper "DiffCom: Channel Received Signal is a Natural Condition to Guide Diffusion Posterior Sampling".
Project website: https://semcomm.github.io/DiffCom/
End-to-end visual communication systems typically optimize a trade-off between channel bandwidth costs and signal-level distortion metrics. However, under challenging physical conditions, this traditional coding and transmission paradigm often results in unrealistic reconstructions with perceptible blurring and aliasing artifacts, despite the inclusion of perceptual or adversarial losses for optimizing. This issue primarily stems from the receiver’s limited knowledge about the underlying data manifold and the use of deterministic decoding mechanisms. To address these limitations, this paper introduces DiffCom, a novel end-to-end generative communication paradigm that utilizes off-the-shelf generative priors and probabilistic diffusion models for decoding, thereby improving perceptual quality without heavily relying on bandwidth costs and received signal quality. Unlike traditional systems that rely on deterministic decoders optimized solely for distortion metrics, our DiffCom leverages raw channel-received signal as a fine-grained condition to guide stochastic posterior sampling. Our approach ensures that reconstructions remain on the manifold of real data with a novel confirming constraint, enhancing the robustness and reliability of the generated outcomes.
RDP curves on FFHQ testset
Clone the repo and create a conda environment (we use PyTorch 1.9, CUDA 11.1).
TODO: check the dependencies
We provide 3 pre-trained ADJSCC models in this link, please download them and put them in the _djscc/ckpt folder.
The pre-trained Diffusion models are available at the following links, please download them and put them in the model_zoo folder.
| Model | Download link |
|---|---|
| 256x256_diffusion_uncond.pt(ILSVRC 2012 subset of ImageNet) | download link |
| ffhq_10m.pt | download link |
TODO: provide implementations of DiffCom based on `SwinJSCC` and `NTSCC+`.
python main_diffcom.py --opt ./configs/diffcom.yamlPlease check the diffcom.yaml file for more details.
The codebase now supports diffcom, hifi_diffcom, and blind_diffcom.
You can change the hyperparameters in the YAML file to run the corresponding method.
The implementation is based on DPS, PSLD.
We thank the authors for sharing their code.
If you find this code useful for your research, please cite our paper
@article{wang2024diffcom,
title={DiffCom: Channel Received Signal is a Natural Condition to Guide Diffusion Posterior Sampling},
author={Wang, Sixian and Dai, Jincheng and Tan, Kailin and Qin, Xiaoqi and Niu, Kai and Zhang, Ping},
journal={arXiv preprint arXiv:2406.07390},
year={2024}
}