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LossResilientLIC

An implementation of "Towards Loss-Resilient Image Coding for Unstable Satellite Networks" (AAAI 2025 Oral).


Abstract

Our method builds on the channel-wise progressive coding framework, incorporating Spatial-Channel Rearrangement (SCR) on the encoder side and Mask Conditional Aggregation (MCA) on the decoder side to improve reconstruction quality with unpredictable errors. By integrating the Gilbert-Elliot model into the training process, we enhance the model’s ability to generalize in real-world network conditions.


Citation

If you use this code in your research, please cite our paper:

@inproceedings{sha2025towards,
  title={Towards Loss-Resilient Image Coding for Unstable Satellite Networks},
  author={Sha, Hongwei and Dong, Muchen and Luo, Quanyou and Lu, Ming and Chen, Hao and Ma, Zhan},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={39},
  number={12},
  pages={12506--12514},
  year={2025}
}

Installation

  1. Clone the repo

    git clone https://github.com/NJUVISION/LossResilientLIC.git
    cd LossResilientLIC
  2. Install dependencies

    pip install compressai
    pip install timm

Training

python train.py train_config.yaml

Evaluation

python eval_channel_packet.py

Progressive Inference

python progressive_test.py 

Directory Structure

├── bin/                # Compressed bit stream
├── exp/                # Organized network data
├── losses/                # RD loss
├── models/                 # Model definitions
├── rec_img/                # Decompressed images
├── sim2net/                 # Simple Network Simulator
├── train.py                # Training entrypoint
├── eval_channel_packet.py             # Evaluation entrypoint with packet
├── codec.py            # Real inference Codec
├── train_config.yaml            # Training config
├── codec_config.yaml            # Real inference Codec config
├── progressive_test.py        # Progressive inference entrypoint
└── README.md

License

BSD 3-Clause Clear License


Acknowledgements

This work builds upon the CompressAI, sim2net and ProgDTD.

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[AAAI 2025 (Oral)] Towards Loss-Resilient Image Coding for Unstable Satellite Networks

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