Variable-Rate Deep Image Compression through Spatially-Adaptive Feature Transform

This repository is the implementation of "Variable-Rate Deep Image Compression through Spatially-Adaptive Feature Transform" (ICCV 2021). Our code is based on CompressAI.

Abstract: We propose a versatile deep image compression network based on Spatial Feature Transform (SFT), which takes a source image and a corresponding quality map as inputs and produce a compressed image with variable rates. Our model covers a wide range of compression rates using a single model, which is controlled by arbitrary pixel-wise quality maps. In addition, the proposed framework allows us to perform task-aware image compressions for various tasks, e.g., classification, by efficiently estimating optimized quality maps specific to target tasks for our encoding network. This is even possible with a pretrained network without learning separate models for individual tasks. Our algorithm achieves outstanding rate-distortion trade-off compared to the approaches based on multiple models that are optimized separately for several different target rates. At the same level of compression, the proposed approach successfully improves performance on image classification and text region quality preservation via task-aware quality map estimation without additional model training.

Installation

We tested our code in ubuntu 16.04, g++ 8.4.0, cuda 10.1, python 3.8.8, pytorch 1.7.1. A C++ 17 compiler is required to use the Range Asymmetric Numeral System implementation.

1. Check your g++ version >= 7. If not, please update it first and make sure to use the updated version.

   • $ g++ --version

2. Set up the python environment (Python 3.8).

3. Install needed packages.

   • $ pip install torch==1.7.1+cu101 torchvision==0.8.2+cu101 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html
   • $ pip install -r requirements.txt
   • If some errors occur in installing CompressAI, please install it yourself. It is for the entropy coder.
     • We used CompressAI 1.0.9 version.

Dataset

• Training set: COCO dataset
• Test set: Kodak dataset

1. (Training set) Download the following files and decompress them.

   • 2014 Train images [83K/13GB]
   • 2014 Train/Val annotations [241MB]
     • instances_train2014.json
   • 2017 Train images [118K/18GB]
   • 2017 Train/Val annotations [241MB]
     • instances_train2017.json

2. (Test set) Download Kodak dataset.

3. Make a directory of structure as follows for the datasets.

   ├── your_dataset_root
   ├── coco
       |── annotations
           ├── instances_train2014.json
           └── instances_train2017.json
       ├── train2014
       └── train2017
   └── kodak
           ├── 1.png
           ├── ...

4. Run following command in scripts directory.

   • $ ./prepare.sh your_dataset_root/coco your_dataset_root/kodak
   • trainset_coco.csv and kodak.csv will be created in data directory.

Training

Configuration

We used the same configuration as ./configs/config.yaml to train our model. You can change it as you want. We expect that larger number of training iteration will lead to the better performance.

Train

$ python train.py --config=./configs/config.yaml --name=your_instance_name \ The checkpoints of the model will be saved in ./results/your_instance_name/snapshots. \ Training for 2M iterations will take about 2-3 weeks on a single GPU like Titan Xp. At least 12GB GPU memory is needed for the default training setting.

Resume from a checkpoint

$ python train.py --resume=./results/your_instance_name/snapshots/your_snapshot_name.pt \ By default, the original configuration of the checkpoint ./results/your_instance_name/config.yaml will be used.

Evaluation

$ python eval.py --snapshot=./results/your_instance_name/snapshots/your_snapshot_name.pt --testset=./data/kodak.csv

Pretrained model

We release the pretrained model. Unzip the file and put it in results directory. You can use it like following: \ $ python eval.py --snapshot=./results/pretrained_dist/snapshots/2M_itrs.pt --testset=./data/kodak.csv

Final evaluation results

[ Test-1 ] Total: 0.5104 | Real BPP: 0.2362 | BPP: 0.2348 | PSNR: 29.5285 | MS-SSIM: 0.9360 | Aux: 93 | Enc Time: 0.2403s | Dec Time: 0.0356s
[ Test 0 ] Total: 0.2326 | Real BPP: 0.0912 | BPP: 0.0902 | PSNR: 27.1140 | MS-SSIM: 0.8976 | Aux: 93 | Enc Time: 0.2399s | Dec Time: 0.0345s
[ Test 1 ] Total: 0.2971 | Real BPP: 0.1187 | BPP: 0.1176 | PSNR: 27.9824 | MS-SSIM: 0.9159 | Aux: 93 | Enc Time: 0.2460s | Dec Time: 0.0347s
[ Test 2 ] Total: 0.3779 | Real BPP: 0.1559 | BPP: 0.1547 | PSNR: 28.8982 | MS-SSIM: 0.9323 | Aux: 93 | Enc Time: 0.2564s | Dec Time: 0.0370s
[ Test 3 ] Total: 0.4763 | Real BPP: 0.2058 | BPP: 0.2045 | PSNR: 29.9052 | MS-SSIM: 0.9464 | Aux: 93 | Enc Time: 0.2553s | Dec Time: 0.0359s
[ Test 4 ] Total: 0.5956 | Real BPP: 0.2712 | BPP: 0.2697 | PSNR: 30.9739 | MS-SSIM: 0.9582 | Aux: 93 | Enc Time: 0.2548s | Dec Time: 0.0354s
[ Test 5 ] Total: 0.7380 | Real BPP: 0.3558 | BPP: 0.3541 | PSNR: 32.1140 | MS-SSIM: 0.9678 | Aux: 93 | Enc Time: 0.2598s | Dec Time: 0.0358s
[ Test 6 ] Total: 0.9059 | Real BPP: 0.4567 | BPP: 0.4548 | PSNR: 33.2801 | MS-SSIM: 0.9752 | Aux: 93 | Enc Time: 0.2596s | Dec Time: 0.0361s
[ Test 7 ] Total: 1.1050 | Real BPP: 0.5802 | BPP: 0.5780 | PSNR: 34.4822 | MS-SSIM: 0.9811 | Aux: 93 | Enc Time: 0.2590s | Dec Time: 0.0364s
[ Test 8 ] Total: 1.3457 | Real BPP: 0.7121 | BPP: 0.7095 | PSNR: 35.5609 | MS-SSIM: 0.9852 | Aux: 93 | Enc Time: 0.2569s | Dec Time: 0.0367s
[ Test 9 ] Total: 1.6392 | Real BPP: 0.8620 | BPP: 0.8590 | PSNR: 36.5931 | MS-SSIM: 0.9884 | Aux: 93 | Enc Time: 0.2553s | Dec Time: 0.0371s
[ Test10 ] Total: 2.0116 | Real BPP: 1.0179 | BPP: 1.0145 | PSNR: 37.4660 | MS-SSIM: 0.9907 | Aux: 93 | Enc Time: 0.2644s | Dec Time: 0.0376s
[ Test ] Total mean: 0.8841 | Enc Time: 0.2540s | Dec Time: 0.0361s

• [ TestN ] means to use a uniform quality map of (N/10) value for evaluation.
  • For example, in the case of [ Test8 ], a uniform quality map of 0.8 is used.
• [ Test-1 ] means to use pre-defined non-uniform quality maps for evaluation.
• Bpp is the theoretical average bpp calculated by the trained probability model.
• Real Bpp is the real average bpp for the saved file including quantized latent representations and metadata.
  • All bpps reported in the paper are Real Bpp.
• Total is the average loss value.

Classification-aware compression

Dataset

We made a test set of ImageNet dataset by sampling 102 categories and choosing 5 images per a category randomly.

1. Prepare the original ImageNet validation set ILSVRC2012_img_val.
2. Run following command in scripts directory.
   • $ ./prepare_imagenet.sh your_dataset_root/ILSVRC2012_img_val
   • imagenet_subset.csv will be created in data directory.

Running

$ python classification_aware.py --snapshot=./results/your_instance_name/snapshots/your_snapshot_name.pt \ A result plot ./classificatoin_result.png will be generated.

Citation

@inproceedings{song2021variable,
  title={Variable-Rate Deep Image Compression through Spatially-Adaptive Feature Transform},
  author={Song, Myungseo and Choi, Jinyoung and Han, Bohyung},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={2380--2389},
  year={2021}
}