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[ICLR2022] Entroformer: A Transformer-based Entropy Model for Learned Image Compression [pdf]

The official repository for Entroformer: A Transformer-based Entropy Model for Learned Image Compression.

Pipeline

Evaluation on Kodak Dataset

Requirements

Prerequisites

Clone the repo and create a conda environment as follows:

conda create --name entroformer python=3.7
conda activate entroformer
conda install pytorch=1.7 torchvision cudatoolkit=10.1
pip install torchac

(We use PyTorch 1.7, CUDA 10.1. We use torchac for arithmetic coding.)

Test Dataset

Kodak Dataset

kodak
├── image1.jpg 
├── image2.jpg
└── ...

Train & Evaluate & Comress & Decompress

Train:

sh scripts/pretrain.sh 0.3
sh scripts/train.sh [tradeoff_lambda(e.g. 0.02)]
(You may use your own dataset by modifying the train/test data path.)

Evaluate:

# Kodak
sh scripts/test.sh [/path/to/kodak] [model_path]
(sh test_parallel.sh [/path/to/kodak] [model_path])

Compress:

sh scripts/compress.sh [original.png] [model_path]
(sh compress_parallel.sh [original.png] [model_path])

Decompress:

sh scripts/decompress.sh [original.bin] [model_path]
(sh decompress_parallel.sh [original.bin] [model_path])

Trained Models

Download the pre-trained models optimized by MSE.

Note: We reorganize code and the performances are slightly different from the paper's.

Acknowledgement

Codebase from L3C-image-compression , torchac

Citation

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

@InProceedings{Yichen_2022_ICLR,
    author    = {Qian, Yichen and Lin, Ming and Sun, Xiuyu and Tan, Zhiyu and Jin, Rong},
    title     = {Entroformer: A Transformer-based Entropy Model for Learned Image Compression},
    booktitle = {International Conference on Learning Representations},
    month     = {May},
    year      = {2022},
}