ResiComp: Loss-Resilient Image Compression via Dual-Functional Masked Visual Token Modeling

This is the repository of the paper "ResiComp: Loss-Resilient Image Compression via Dual-Functional Masked Visual Token Modeling".

Overview of ResiComp

Requirements

Clone the repo and create a conda environment (we use PyTorch 1.9, CUDA 11.1).

The dependencies includes CompressAI.

Pre-trained Models

Download the pre-trained models from Google Drive.

Train and Evaluate

python main.py --config './config/resicom.yaml' 

Acknowledgement

Codebase from CompressAI and Swin Transformer

Citation

If you find this code useful for your research, please cite our paper

@ARTICLE{10877904,
  author={Wang, Sixian and Dai, Jincheng and Qin, Xiaoqi and Yang, Ke and Niu, Kai and Zhang, Ping},
  journal={IEEE Transactions on Circuits and Systems for Video Technology}, 
  title={ResiComp: Loss-Resilient Image Compression via Dual-Functional Masked Visual Token Modeling}, 
  year={2025},
  volume={},
  number={},
  pages={1-1},
  keywords={Context modeling;Packet loss;Resilience;Entropy;Image coding;Codecs;Transforms;Transformers;Adaptation models;Forward error correction},
  doi={10.1109/TCSVT.2025.3539747}}