Lossy Image Compression using Hierarchical VAEs

This repository contains the authors' implementation of several deep learning-based methods related to lossy image compression.

• Models
• Results
• Install
• Usage (compress, decompress, train, evaluation)
• Licenses

Models

Implemented Methods (Pre-Trained Models Available)

• Lossy Image Compression with Quantized Hierarchical VAEs [arxiv] [cvf] [ieee]
  • Published at WACV 2023, Best Algorithms Paper Award
  • Abstract: a 12-layer VAE model named QRes-VAE. Good compression performance.
  • [Code & pre-trained models]: lossy-vae/lvae/models/qres
• QARV: Quantization-Aware ResNet VAE for Lossy Image Compression [arxiv] [ieee]
  • Published at TPAMI 2023
  • Abstract: an improved version of the previous model; Variable rate, faster decoding, better performance.
  • [Code & pre-trained models]: lossy-vae/lvae/models/qarv
• An Improved Upper Bound on the Rate-Distortion Function of Images [arxiv] [ieee]
  • Published at ICIP 2023
  • Abstract: a 15-layer VAE model used to estimate the information R(D) function. This model proves that -30% BD-rate w.r.t. VTM is theoretically achievable.
  • [Code & pre-trained models]: lossy-vae/lvae/models/rd

Features

Progressive coding: our models learn a deep hierarchy of latent variables and compress/decompress images in a coarse-to-fine fashion. This feature comes from the hierarchical nature of ResNet VAEs.

Compression performance: our models are powerful in terms of both rate-distortion and decoding speed. Please see the results section below.

Results

Bpp-PSNR results in JSON format

• Kodak images: lossy-vae/results/kodak
• Tecknick TESTIMAGES RGB 1200x1200: lossy-vae/results/tecnick-rgb-1200
• CLIC 2022 test set: lossy-vae/results/clic2022-test

Notes on metric computation:

• Bpp and PSNR are first computed for each image and then averaged over all images in a dataset.
• Bpp is the saved file size (in bits) divided by # of image pixels.
• PSNR is computed in RGB space (not YUV).

Encoding/decoding latency on CPU/GPU, and BD-rate

Model Name	CPU* Enc.	CPU* Dec.	3080 ti Enc.	3080 ti Dec.	BD-rate* (lower is better)
qres34m	0.899s	0.441s	0.116s	0.083s	-3.95 %
qarv_base	0.757s	0.295s	0.096s	0.063s	-7.26 %

*Time is the latency to encode/decode a 512x768 image, averaged over 24 Kodak images. Tested in plain PyTorch (v1.13 + CUDA 11.7) code, ie, no mixed-precision, torchscript, ONNX/TensorRT, etc.
*CPU is Intel 10700k.
*BD-rate is w.r.t. VTM 18.0, averaged on three common test sets (Kodak, Tecnick TESTIMAGES, and CLIC 2022 test set).

Install

Requirements:

• Python
• PyTorch >= 1.9 : https://pytorch.org/get-started/locally
• tqdm : conda install tqdm
• CompressAI : https://github.com/InterDigitalInc/CompressAI
• timm >= 0.8.0 : https://github.com/huggingface/pytorch-image-models

Download and Install:

1. Download the repository;
2. Modify the dataset paths in lossy-vae/lvae/paths.py.
3. [Optional] pip install the repository in development mode:

cd /pasth/to/lossy-vae
python -m pip install -e .

Usage

Get pre-trained weights

from lvae import get_model
model = get_model('qarv_base', pretrained=True) # weights are downloaded automatically
model.eval()
model.compress_mode(True) # initialize entropy coding

Compress images

Encode an image:

model.compress_file('/path/to/image.png', '/path/to/compressed.bits')

Decode an image:

im = model.decompress_file('/path/to/compressed.bits')
# im is a torch.Tensor of shape (1, 3, H, W). RGB. pixel values in [0, 1].

Datasets

COCO

1. Download the COCO dataset "2017 Train images [118K/18GB]" from https://cocodataset.org/#download
2. Unzip the images anywhere, e.g., at /path/to/datasets/coco/train2017
3. Edit lossy-vae/lvae/paths.py such that

known_datasets['coco-train2017'] = '/path/to/datasets/coco/train2017'

Kodak (link), Tecnick TESTIMAGES (link), and CLIC (link)

python scripts/download-dataset.py --name kodak         --datasets_root /path/to/datasets
                                          clic2022-test
                                          tecnick

Then, edit lossy-vae/lvae/paths.py such that known_datasets['kodak'] = '/path/to/datasets/kodak', and similarly for other datasets.

Custom Dataset

1. Prepare a folder containing images. The folder should contain only images (may contain subfolders).
2. Edit lossy-vae/lvae/paths.py such that known_datasets['custom-name'] = '/path/to/my-dataset', where custom-name is the name of your dataset, and /path/to/my-dataset is the path to the folder containing images.
3. Then, you can use custom-name as the dataset name in the training/evaluation scripts.

Training and evaluation scripts

Training and evaluation scripts vary from model to model. For example, qres34m uses fixed-rate train/eval scheme, while qarv_base uses variable-rate train/eval scheme.
Detailed training/evaluation instructions are provided in each model's subfolder (see the section Models).

License

Code in this repository is freely available for non-commercial use.