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A Deep Learning Approach to Data Compression

We introduce Bit-Swap, a scalable and effective lossless data compression
technique based on deep learning. It extends previous work on practical
compression with latent variable models, based on bits-back coding and
asymmetric numeral systems. In our experiments Bit-Swap is able to beat
benchmark compressors on a highly diverse collection of images. We’re releasing
code for the method and optimized models such that people can explore and
advance this line of modern compression ideas. We also release a demo and
a pre-trained model for Bit-Swap image compression and decompression on your
own image. See the end of the post for a talk that covers how bits-back coding
and Bit-Swap works.

Lossless compression for high-dimensional data

The goal is to design an effective lossless compression scheme that is scalable
to high-dimensional data, like images. This is a matter of concurrently solving
two problems:

  1. choosing a statistical model that closely captures the underlying
    distribution of the input data and

  2. developing a scalable compression algorithm that exploits this model’s
    theoretical compression potential.

The compression ratio of the resulting compression scheme heavily relies on the
first problem: the model capacity. Recent advances in deep learning allow us to
optimize probabilistic models of complex high-dimensional data efficiently.
These developments have opened up many opportunities regarding lossless
compression. A powerful technique is to pair autoregressive models with
entropy coders, like arithmetic coding or asymmetric numeral systems
(ANS), resulting in excellent compression ratios. However, the autoregressive
structure typically makes decompression several orders of magnitude slower than

Fortunately, ANS is known to be amenable to parallelization. To exploit this
property, we have to narrow our focus to models that encompass fully factorized
distributions. This constraint forces us to be extra innovative and choose our
model and coding scheme accordingly.

Recent work

The recent Bits-Back with Asymmetric Numeral Systems (BB-ANS) method tries to
mitigate this issue by combining latent variable models with ANS. Latent
variable models define unobserved random variables whose values help govern the
distribution of the data. For example, if the observed data consists of images,
the composition of the images may be dependent on the locations of edges and
textures, which are latent variables. (In practice, we typically define an
uninformative prior over the latent variables, like a standard gaussian.) This
type of model may use fully factorized distributions and can be efficiently
optimized using the VAE framework.

The critical component that enables BB-ANS to compress with latent variable
models is a principle called bits-back coding that turned out to be a natural
fit with ANS. Bits-back coding ensures compression that closely matches the
negative ELBO on average, in addition to an overhead that only occurs at
initialization. This overhead becomes insignificant when compressing long
sequences of datapoints at once.

Our contribution

While latent variable models can be designed to be complex density estimators,
restricting the model to fully factorized distributions, however, can
significantly limit model flexibility. Therefore, we propose employing
hierarchical latent variable models, which typically have greater modelling
capacity than models with a single latent layer. We extend the latent variable
model recursively, by substituting its fully factorized prior distribution by a
second latent variable model, substituting its prior by a third latent variable
model, and so on.

For example, if the observed data are images, the composition of the images may
be dependent on the locations of edges and textures, which may be dependent on
the locations of objects, which may be dependent on the scene composition, etc.
Note that if we let every layer be solely dependent on the layer on top of
that, this model design can be interpreted as multiple nested latent variable
models: the observed data distribution being governed by latent layer 1, latent
layer 1 distribution being governed by latent layer 2, up until the top latent
layer, which has an unconditional prior distribution.

Using that insight, we developed a novel coding technique called recursive
bits-back coding. As the name suggests, we apply bits-back coding on every
layer recursively, processing the nested latent variable models from bottom to
top. We coined the joint composition of recursive bits-back coding and the
specified hierarchical latent variable model Bit-Swap. The merits of Bit-Swap

  1. Applying bits-back coding in a recursive manner resulting in an overhead
    that is bounded and in practice does not grow with the depth of the model
    hierarchy. This stands in contrast with naively applying BB-ANS on a
    hierarchical latent variable model, which would ignore the latent variable
    topology and would treat all latent layers as one single vector, resulting in
    an overhead that linearly grows with the depth of the hierarchy. The bounded
    overhead makes Bit-Swap particularly interesting if we want to employ a
    powerful model with a deep latent hierarchy, while we do not wish to compress
    long sequences of datapoints at once.

  2. Bit-Swap is still able to compress close to the negative ELBO on average, in
    addition to a smaller overhead.

  3. Nesting the latent variable models through the prior distribution of every
    layer enables more complex distributions for every latent layer, except for the
    top one. The nested structure prompts a tighter ELBO, which in turn results in
    lower compression ratios.

  4. We maintain fully factorized distributions throughout the model, which makes
    the entire coding process is parallelizable. Using a GPU implementation of
    , together with model-parallelism, should result in high-speed
    compression and decompression. The major bottleneck in our implementation is
    the ANS operation, but we are optimistic this can be resolved due to its
    inherent parallelizability. We leave speed optimization of Bit-Swap to future


  Compression ratio of 100 images from ImageNet (unscaled & cropped)
Uncompressed 100.00 %
GNU Gzip 74.50 %
bzip2 63.38 %
LZMA 63.63 %
PNG 58.88 %
WebP 45.75 %
BB-ANS 45.25 %
Bit-Swap 43.88 %

We trained the hierarchical latent variable model on random 32 by 32 pixel
patches of the training set of ImageNet. For testing, we took 100 images
independently from the test set. The 100 images were cropped to multiples of 32
pixels on each side, such that we could fit a grid 32 by 32 pixel blocks. The
grid is treated as a dataset that has to be processed in sequence in order to
be compressed with Bit-Swap and BB-ANS. We then apply Bit-Swap and BB-ANS to a
single sequence at the time, corresponding to compressing one image at the
time. We used the same cropped images for the baseline schemes, without first
breaking the images up in 32 x 32 pixel blocks. The results are shown above. We
believe results can be further improved by using bigger pixel patches and more
sophisticated model optimization. All other results can be found in the paper.


Compress your own image using Bit-Swap. Clone the GitHub repository on and
run the script and The script will compress using Bit-Swap and compare it against GNU
Gzip, bzip2, LZMA, PNG and WebP compression. The script
will decompress a Bit-Swap compressed file.

Note: if the input file is already compressed (JPEG, PNG etc.), the script
first has to decompress that file export it to RGB pixel data. Thereafter, the
RGB pixel values are the input and that data gets compressed by Bit-Swap and
the other schemes, resulting in size reduction compared to the RGB pixel data.
One might notice that the raw RGB pixel data contains (much) more information
than the input file. The discrepancy between the file sizes is especially
prominent when converting JPEG files to RGB data. This is largely because JPEG,
a lossy compressor, consists of a quantization step, in which the original
image loses a (large) portion of the information. Quantization creates
predictable patterns, which are in turn compressed using lossless compression
techniques for the purpose of storage. When decompressing the JPEG file and
converting to RGB, however, we store every single pixel value explicitly, thus
disregarding the patterns. This could result in a large increase of


See the video below for a talk about this paper.

You can find the slides here.

This work was done while the author was at UC Berkeley. We refer the reader to
the following paper for details:

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