Awesome

Universal Deep Image Compression via Content-Adaptive Optimization with Adapters

Official implementation of "Universal Deep Image Compression via Content-Adaptive Optimization with Adapters" presented at WACV 23.

Environment

Recommend to use Miniconda.

# python>=3.6 should be fine.
conda create -n aof python=3.9
conda activate aof
# install 7z for [Lam+, ACM MM 20]
conda install -c bioconda p7zip
pip install -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cu116

Preparation

Dataset

Prepare a dataset that consists of four domains: natural images, line drawings, comics, and vector arts.

python scripts/download_dataset.py
# In 2022/10/21, four files have missing links.
# the four files: (`156526161`, `99117125`, `15642096`, `158633139`)
python scripts/replace_empty_image.py

Pre-trained Weights

Prepare the weights of WACNN pre-trained on natural images.

bash scripts/download_pretrained_models.bash

Usage

Training of the Proposed Method

Apply the demo code (main.py) to a dataset by running a script. Please specify ["vector", "natural", "line", "comic"] as a dataset and [1, 2, 3, 4, 5, 6] as quality.

python run_main.py vector --out results/ours --quality 1

The refined latent representation is encoded and saved in cache/. The adapter parameters are encoded and saved in results/ours/wacnn/q1/<image file name>/weights.pt.

Evaluation of the Proposed Method

Perform evaluation using the compressed data obtained in training with the command below. Please specify --domain from ["vector", "natural", "line", "comic"]. The default is "vector".

# Without adaptation
python decode.py --stage 0th # --domain vector
# Without adapters (= only refining the latent representation = [Yang+, NeurIPS 20])
python decode.py --stage 1st # --domain vector
# Ours
python decode.py --stage 2nd # --domain vector

You can finally obtain the results in the csv format in results/ours/wacnn/q{1,2,3,4,5,6}/<dataset name>_{0th,1st,2nd}.csv.

Comparison with Other Adaptive Methods

Run other adaptive methods in our framework.

# [Lam+, ACM MM 20]
python run_main.py vector --out results/lam-mm20 --regex "'g_s\..*\.bias'" --n-dim 0 --width 0.0 --data_type float32 --quality 1
python decode.py --weight_root results/lam-mm20 --n-dim-2 0 --width 0.0 --regex "g_s\..*\.bias" --data_type float64+7z

# [Rozendaal+, ICLR 21]
python run_main.py vector --out results/rozendaal-iclr21 --regex "'.*'" --n-dim 0 --width 0.005 --alpha 1000 --sigma 0.05 --distrib spike-and-slab --lr 3e-5 --opt-enc --pipeline end2end --quality 1
python decode.py --weight_root results/rozendaal-iclr21 --rozendaal

# [Zou+, ISM 21]
python run_main.py vector --out results/zou-ism21 --n-dim -1 --groups 192 --width 0.0 --quality 1
python decode.py --weight_root results/zou-ism21 --n-dim-2 -1 --groups 192 --width 0.0

You can compare these methods with the baseline and proposed methods using the command below.

python plot_rdcurve.py

Ablation Studies

(Optional) Perform ablation studies.

• Optimization only in terms of distortion

# Distortion opt.
python run_main.py vector --out results-abl/ours-dopt --width 0.0 --quality 1
python decode.py --weight_root results-abl/ours-dopt --width 0.0

• Optimization of other parameters

# Biases
python run_main.py vector --out results-abl/ours-bias --regex "'g_s\..*\.bias'" --n-dim 0 --lr_2 1e-5 --quality 1
python decode.py --weight_root results-abl/ours-bias --regex "'g_s\..*\.bias'" --n-dim-2 0
# OMPs
python run_main.py vector --out results-abl/ours-omp --distrib logistic --n-dim -1 --groups 192 --quality 1
python decode.py --weight_root results-abl/ours-omp --n-dim-2 -1 --groups 192

• Optimization Order

python run_main.py vector --out results-abl/ours-swap --pipeline swap --quality 1
python decode.py --weight_root results-abl/ours-swap --pipeline swap

• Another Base Network Architecture

# Cheng20
python run_main.py vector --out results/ours --model cheng2020-attn --regex "'g_s\.[8-8]\.adapter_1.*'" --quality 1
python decode.py --model cheng2020-attn --n-dim-1 2 --n-dim-2 0

Contact

Feel free to contact me if there is any question: tsubota (a) hal.t.u-tokyo.ac.jp

License

This code is licensed under MIT (if not specified in the code).

Some part of this code are modified and copied open-source code. For the part, I describe the original license. Please let me know if there is a license issue with code redistribution. If so, I will remove the code and provide the instructions to reproduce the work.

As for the dataset, I do not have any rights and provide only URLs. Please refer to the original datasets (Kodak and BAM) for the detailed license of the images. When the link of some URLs is missing, I will redistribute the corresponding images if they are licensed under Creative Commons.