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Informal disclaimer: Craystack is experimental software for prototyping and research in lossless compression. We will change (and probably break) parts of Craystack, and do not make any guarantees about API stability.

Craystack

The basic building blocks for doing lossless compression are

  1. Data to compress.
  2. A (probabilistic, generative) model for the data. You'll need to be able to evaluate the likelihood (or ELBO) of the data under this model so no GANs allowed!
  3. Software to do the compression, given data and a model.

Craystack is software to do compression, given data and model.

Craystack is based on codecs. In Craystack a codec is a pair of functions, one for mapping data to a compressed representation (encoding) and one for mapping back again (decoding). Simple codecs are provided, along with codec builders for various kinds of model.

The codecs in Craystack are

  • Composable, meaning you can combine and mix them to code different combinations of data types, according to sophisticated models, in parallel or in series.
  • Stacky, meaning last-in-first-out (LIFO). The last item that an encoder compresses will be the first item that a decoder decompresses.

Installation

To install craystack:

git clone [email protected]:j-towns/craystack.git
cd craystack
pip install -e .

Then to run the tests do

pytest

you may have to install pytest with

pip install pytest

Example: compress the MNIST test set in a few seconds using BB-ANS

Note: this requires pytorch to be installed.

In the examples directory, run python binary_mnist_vae.py. This compresses the MNIST test set using a small VAE model, using the BB-ANS algorithm. This uses the VAE codec, and runs in a few seconds thanks to the vectorization of craystack.

Authors

Craystack was written by Jamie Townsend, Tom Bird and Julius Kunze. If you use Craystack in your research, please cite this paper. Bibtex:

@inproceedings{
townsend2020hilloc,
title={Hi{\{}LL{\}}oC: lossless image compression with hierarchical latent variable models},
author={James Townsend and Thomas Bird and Julius Kunze and David Barber},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://openreview.net/forum?id=r1lZgyBYwS}
}