COOL-CHIC

This repository contains an implementation of COOL-CHIC: Coordinate-based Low Complexity Hierarchical Image Codec.

COOL-CHIC is a neural image codec based on overfitting. It offers the following features:

Decode an image with less than 700 multiplications / decoded pixel
Competitive with Ballé's hyperprior autoencoder (https://openreview.net/forum?id=rkcQFMZRb)
Reach performance close to HEVC (HM), with +20 % BD-rate.

More information available in the paper: COOL-CHIC: Coordinate-based Low Complexity Hierarchical Image Codec, Ladune et al.

Usage

Installation

>>> python3 -m pip install virtualenv               # Install virtual env if needed
>>> virtualenv venv && source venv/bin/activate     # Create and activate a virtual env named "venv"
>>> python3 -m pip install -r requirements.txt      # Install the required packages

Example

The scripts samples/code_image_gpu.sh and samples/code_image_cpu.sh provide examples for COOL-CHIC usage.

>>> ./samples/code_image_gpu.sh         # Use the cpu script if you don't have a GPU (it can be quite long!)
... Many logs are printed to the terminal
>>> cat samples/encoder_results.txt
psnr,rate_latent_bpp,rate_mlp_bpp,rate_all_bpp,training_time_second,n_iteration,decoder_rate_bpp,decoder_psnr_db
33.428055,0.7880071004231771,0.010424812324345112,0.7984318733215332,    5.7,  999.0,0.82692,33.42805

At the end of the 1000 iterations the entropy coding takes place and a bitstream is written. The encode.py script also includes decoding (which can be found at decode.py). The final results can be found at samples/encoder_results.txt. The different fields of the result file are:

Name	Meaning	Unit
psnr	Encoder-side (i.e. omitting entropy coding) PSNR	dB
rate_latent_bpp	Encoder-side (i.e. omitting entropy coding) rate for the latent variables	bit per pixel
rate_mlp_bpp	Encoder-side (i.e. omitting entropy coding) rate synthesis and ARM MLPs	bit per pixel
rate_all_bpp	Encoder-side (i.e. omitting entropy coding) total rate	bit per pixel
training_time_second	Total training time	second
n_iteration	Total number of training iterations	/
decoder_rate_bpp	Total rate (size of the bitstream)	bit per pixel
decoder_psnr_db	PSNR of the image decoded from the bitstream	dB

Due to a naïve implementation of the entropy coding step, decoder-side results are slightly worse than the encoder-side one (see disclaimer below). This should be solved by a better implementation.

Results from the paper are obtained using the psnr and rate_all_bpp rows.

The results/ folder provides the detailed results from the paper as well as on the samples/biville.png image.

Arguments

The different arguments accepted by the encoding script are explained here.

python3 src/encode.py                               \
    --device=cuda:0                                 \   # Run on GPU
    -i samples/biville.png                          \   # Image to compress
    -o samples/bitstream.bin                        \   # Where to write the bitstream
    --decoded_img_path=samples/decoded.png          \   # Save the decoded image here
    --model_save_path=samples/model.pt              \   # Save the resulting .pt model here
    --enc_results_path=samples/encoder_results.txt  \   # Encoder-side results (prior to bitstream)
    --lmbda=0.001                                   \   # Rate constraint: J = Dist + lmbda Rate
    --start_lr=1e-2                                 \   # Initial learning rate
    --n_itr=1000                                    \   # Maximum number of iterations (should be set to 1e6)
    --layers_synthesis=12,12                        \   # 2 hidden layers of width 12 for the synthesis, as in the paper
    --layers_arm=12,12                              \   # 2 hidden layers of width 12 for the synthesis, as in the paper
    --n_ctx_rowcol=2                                \   # Use 2 rows and columns of context pixels for the ARM (C = 12) as in the paper
    --latent_n_grids=7                                  # Use 7 latent grids (from 1:1 to 1:64), as in the paper

⚠ ⚠ ⚠ Disclaimer ⚠ ⚠ ⚠

This repository is only a proof of concept illustrating how the ideas of our COOL-CHIC paper could be translated into an actual codec. As such it suffers a number of limitations, which mainly revolves around the entropy coding step.

All operations are done using floating point arithmetic, including entropy coding. Due to floating point drift:
- The bitstream produced by the encoder are not cross-platform and can only be decoded on the same hardware that produced them.
- Even on the same hardware, floating point arithmetic is not guaranteed to be bit-exact. T his implementation circumvents this issue by quantizing the probability distribution parameters (expectation $\mu$ and standard deviation $\sigma$). This has a noticeable impact on performance (around +4 % of rate according to our empirical measurements).
The encoding process (i.e. learning the MLPs and latent representation) might suffer from failures. In may help to:
- Do successive encoding and keep the best one
- Tweak the learning rate

Thanks

We used the constriction package from Robert Bamler as our entropy coder. Many thanks for the great work!

R. Bamler, Understanding Entropy Coding With Asymmetric Numeral Systems (ANS): a Statistician's Perspective, arXiv preprint arXiv:2201.01741.

baoyu2020/Cool-Chic