Pytorch Implementation of JSAC 2022 Paper "Nonlinear Transform Source-Channel Coding for Semantic Communications"
Arxiv Link: https://arxiv.org/abs/2112.10961
Project Page: https://semcomm.github.io/ntscc/
Pretrained models (optimized for MSE) trained from scratch using randomly chose 300k images from the OpenImages dataset.
Other pretrained models will be released successively.
Note: We reorganize code and the performances are slightly different from the paper's.
If you find the code helpful in your resarch or work, please cite:
@ARTICLE{9791398,
author={Dai, Jincheng and Wang, Sixian and Tan, Kailin and Si, Zhongwei and Qin, Xiaoqi and Niu, Kai and Zhang, Ping},
journal={IEEE Journal on Selected Areas in Communications},
title={Nonlinear Transform Source-Channel Coding for Semantic Communications},
year={2022},
volume={40},
number={8},
pages={2300-2316},
doi={10.1109/JSAC.2022.3180802}
}
The NTSCC model is partially built upon the Swin Transformer and CompressAI. We thank the authors for sharing their code.