RPNSIC

This repository contains the code for reproducing the results with the training file, in the following paper:

Exploring Resolution Fields for Scalable Image Compression with Uncertainty Guidance

1. Preparation

Dataset

1. Download CLIC2020 Dateset and place them in 'Dataset'.
2. You can generate the patches using the patch generation file from Link.
3. Downsample the training images to size HxW, 2Hx2W and 4Hx4W.
4. Generate the tfrecord for spatial scalable.

python create_tfrecords.py --train_tfrecords ./xxx.tfrecords --input_image ./your_4Hx4W_image_folder, --input_image_half ./your_2Hx2W_image_folder, --input_image_quater ./your_4Hx4W_image_folder

Environment

• Python==3.6.13

• Tensorflow==1.15.0

• Tensorflow-Compression==1.3

2. Train

If your machine has multiple GPUs, you can select which GPU you want to run on by setting the environment variable before the Python operation

CUDA_VISIBLE_DEVICES=0 (0, 1, 2, 3,...)

Before training, modify the arguments like train_dataset, checkpoint_dir, num_filter, and lambda.

Spatial Scalable

python train_spaital.py

Quality Scalable

python train_quality.py

3. Test

• When running the evaluate(), the input image will be compressed into several bitstreams like: stream_B.tfci and stream_e1.tfci, it will also evaluate the bpp and PSNR (or MS-SSIM) of each layer.

• When running the decompress() the bitstreams can be decoded to images.

Before testing, modify the arguments like input_image, output_folder, checkpoint_dir, and num_filters.

Spatial Scalable

python eval_spaital.py

Quality Scalable

python eval_quality.py

Citations

If you think it is useful for your reseach, please cite our paper.

@misc{zhang2023exploring,
      title={Exploring Resolution Fields for Scalable Image Compression with Uncertainty Guidance}, 
      author={Dongyi Zhang and Feng Li and Man Liu and Runmin Cong and Huihui Bai and Meng Wang and Yao Zhao},
      year={2023},
      eprint={2306.08941},
      archivePrefix={arXiv},
      primaryClass={eess.IV}
}

Contact

If you find any problem in the code and want to ask any questions, please send us an email dyzhang@bjtu.edu.cn