HFLIC

This section contains the official code for Human Friendly Perceptual Learned Image Compression with Reinforced Transform. Additionally, it includes an unofficial implementation of the paper titled "PO-ELIC: Perception-Oriented Efficient Learned Image Coding." The code implementation is based on CompressAI. We share our enhance transform elic ckpt in Enh-ELIC-ckpt, and modify the config_5group.py in ./config, you can train HFLIC and Enh-POELIC with different lambda. We further share some of our code and lamda setting for "EnhPO:ELIC" and "HFLIC"

PO:ELIC

This section consists of an unofficial implementation of the paper titled "PO-ELIC: Perception-Oriented Efficient Learned Image Coding," which was presented at CVPR22W as the 1st place winner of CLIC22.

How to train po:elic

To utilize the code effectively, follow these steps: Open the file modules/layers/res_blk.py and modify the ResidualBottleneck class. Set N * 2 to N / 2. Open the file config/config_5group.py and locate the line "lambda_face": 0. Modify this line to set the value of "lambda_face" as 0.

ELIC

We have incorporated the ELIC code from the GitHub repository maintained by JiangWeibeta. You can find the code and related resources at the following link: https://github.com/JiangWeibeta/ELIC.

Cite

HFLIC

@misc{ning2023hflic,
      title={HFLIC: Human Friendly Perceptual Learned Image Compression with Reinforced Transform}, 
      author={Peirong Ning and Wei Jiang and Ronggang Wang},
      year={2023},
      eprint={2305.07519},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

PO:ELIC

@inproceedings{he2022po,
  title={PO-ELIC: Perception-Oriented Efficient Learned Image Coding},
  author={He, Dailan and Yang, Ziming and Yu, Hongjiu and Xu, Tongda and Luo, Jixiang and Chen, Yuan and Gao, Chenjian and Shi, Xinjie and Qin, Hongwei and Wang, Yan},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={1764--1769},
  year={2022}
}

ELIC

@misc{jiang2022unofficialelic,
    author={Jiang, Wei},
    title={Unofficial ELIC},
    howpublished={\url{https://github.com/JiangWeibeta/ELIC}},
    year={2022}
}

@inproceedings{he2022elic,
  title={Elic: Efficient learned image compression with unevenly grouped space-channel contextual adaptive coding},
  author={He, Dailan and Yang, Ziming and Peng, Weikun and Ma, Rui and Qin, Hongwei and Wang, Yan},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={5718--5727},
  year={2022}
}