The Devil Is in the Details: Window-based Attention for Image Compression

Pytorch implementation of the paper "The Devil Is in the Details: Window-based Attention for Image Compression". CVPR2022. This repository is based on CompressAI. We kept scripts for training and evaluation, and removed other components. The major changes are provided in compressai/models. For the official code release, see the CompressAI.

About

This repo defines the CNN-based models and Transformer-based models for learned image compression in "The Devil Is in the Details: Window-based Attention for Image Compression".

The architecture of CNN-based model.

The architecture of Transformer-based model (STF).

Installation

Install CompressAI and the packages required for development.

conda create -n compress python=3.7
conda activate compress
pip install compressai
pip install pybind11
git clone https://github.com/Googolxx/STF stf
cd stf
pip install -e .
pip install -e '.[dev]'

Note: wheels are available for Linux and MacOS.

Usage

Training

An examplary training script with a rate-distortion loss is provided in train.py.

Training a CNN-based model:

CUDA_VISIBLE_DEVICES=0,1 python train.py -d /path/to/image/dataset/ -e 1000 --batch-size 16 --save --save_path /path/to/save/ -m cnn --cuda --lambda 0.0035
e.g., CUDA_VISIBLE_DEVICES=0,1 python train.py -d openimages -e 1000 --batch-size 16 --save --save_path ckpt/cnn_0035.pth.tar -m cnn --cuda --lambda 0.0035

Training a Transformer-based model(STF):

CUDA_VISIBLE_DEVICES=0,1 python train.py -d /path/to/image/dataset/ -e 1000 --batch-size 16 --save --save_path /path/to/save/ -m stf --cuda --lambda 0.0035

Evaluation

To evaluate a trained model on your own dataset, the evaluation script is:

CUDA_VISIBLE_DEVICES=0 python -m compressai.utils.eval_model -d /path/to/image/folder/ -r /path/to/reconstruction/folder/ -a stf -p /path/to/checkpoint/ --cuda

CUDA_VISIBLE_DEVICES=0 python -m compressai.utils.eval_model -d /path/to/image/folder/ -r /path/to/reconstruction/folder/ -a cnn -p /path/to/checkpoint/ --cuda

Dataset

The script for downloading OpenImages is provided in downloader_openimages.py. Please install fiftyone first.

Results

Visualization

Visualization of the reconstructed image kodim01.png.

Visualization of the reconstructed image kodim07.png.

RD curves

RD curves on Kodak.

RD curves on CLIC Professional Validation dataset.

Codec Efficiency on Kodak

Method	Enc(s)	Dec(s)	PSNR	bpp
CNN	0.12	0.12	35.91	0.650
STF	0.15	0.15	35.82	0.651

Pretrained Models

Pretrained models (optimized for MSE) trained from scratch using randomly chose 300k images from the OpenImages dataset.

Method	Lambda	Link
CNN	0.0018	cnn_0018
CNN	0.0035	cnn_0035
CNN	0.0067	cnn_0067
CNN	0.025	cnn_025
STF	0.0018	stf_0018
STF	0.0035	stf_0035
STF	0.0067	stf_0067
STF	0.013	stf_013
STF	0.025	stf_025
STF	0.0483	stf_0483

Other pretrained models will be released successively.

Citation

@inproceedings{zou2022the,
  title={The Devil Is in the Details: Window-based Attention for Image Compression},
  author={Zou, Renjie and Song, Chunfeng and Zhang, Zhaoxiang},
  booktitle={CVPR},
  year={2022}
}

Related links

• CompressAI: https://github.com/InterDigitalInc/CompressAI
• Swin-Transformer: https://github.com/microsoft/Swin-Transformer
• Tensorflow compression library by Ballé et al.: https://github.com/tensorflow/compression
• Range Asymmetric Numeral System code from Fabian 'ryg' Giesen: https://github.com/rygorous/ryg_rans
• Kodak Images Dataset: http://r0k.us/graphics/kodak/
• Open Images Dataset: https://github.com/openimages
• fiftyone: https://github.com/voxel51/fiftyone
• CLIC: https://www.compression.cc/