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Image Compression with Stochastic Winner-Take-All
Auto-Encoder
Thierry Dumas, Aline Roumy, Christine Guillemot
To cite this version:
Thierry Dumas, Aline Roumy, Christine Guillemot. Image Compression with Stochastic Winner-Take-
All Auto-Encoder. 2017 IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2017), Mar 2017, New Orleans, United States. �hal-01493137�
IMAGE COMPRESSION WITH STOCHASTIC WINNER-TAKE-ALL AUTO-ENCODER
Thierry Dumas, Aline Roumy, Christine Guillemot
INRIA Rennes Bretagne-Atlantique
thierry.dumas@inria.fr, aline.roumy@inria.fr, christine.guillemot@inria.fr
ABSTRACT
This paper addresses the problem of image compression us-
ing sparse representations. We propose a variant of auto-
encoder called Stochastic Winner-Take-All Auto-Encoder
(SWTA AE). “Winner-Take-All” means that image patches
compete with one another when computing their sparse rep-
resentation and “Stochastic” indicates that a stochastic hy-
perparameter rules this competition during training. Unlike
auto-encoders, SWTA AE performs variable rate image com-
pression for images of any size after a single training, which
is fundamental for compression. For comparison, we also
propose a variant of Orthogonal Matching Pursuit (OMP)
called Winner-Take-All Orthogonal Matching Pursuit (WTA
OMP). In terms of rate-distortion trade-off, SWTA AE out-
performs auto-encoders but it is worse than WTA OMP. Be-
sides, SWTA AE can compete with JPEG in terms of rate-
distortion.
Index Terms— Image compression, sparse representa-
tions, auto-encoders, Orthogonal Matching Pursuit.
1. INTRODUCTION
Auto-encoders are powerful tools for reducing the dimension-
ality of data. Deep fully-connected auto-encoders [1] are tra-
ditionally used for this task. However, two issues have so far
prevented them from becoming efficient image compression
algorithms: they can only be trained for one image size and
one compression rate [2, 3].
[4] attempts to solve both issues. The authors train an
auto-encoder on image patches so that images of various sizes
can be compressed. Their auto-encoder is a recurrent [5]
residual auto-encoder that performs variable rate image com-
pression after a single training. But all image patches have the
same rate and therefore different distortions due to the texture
complexity variety in image patches. In addition, recurrence,
which is equivalent to scalability in image compression, is not
optimal in terms of rate-distortion trade-off [6, 7].
Instead, we propose to perform learning on whole images
under a global rate-distortion constraint. This is done through
This work has been supported by the French Defense Procurement
Agency (DGA).
Winner-Take-All (WTA), which can be viewed as a competi-
tion between image patches when computing their represen-
tation. Furthermore, auto-encoders architecture must adapt
to different rates. Therefore, during training, the WTA pa-
rameter that controls the rate is stochastically driven. These
contributions give rise to Stochastic Winner-Take-All Auto-
Encoder (SWTA AE).
1.1. Notation
Vectors are denoted by bold lower case letters and matrices
by upper case ones. X
j
denotes the j
th
column of a matrix
X. kXk
F
is the Frobenius norm of X. kXk
0
counts the
number of non-zero elements in X. The support of a vector x
is supp (x) = {i | x
i
6= 0}.
2. STOCHASTIC WINNER-TAKE-ALL
AUTO-ENCODER (SWTA AE)
We now present our Stochastic Winner-Take-All Auto-
Encoder (SWTA AE) whose architecture is shown in Figure 1.
SWTA AE is a type of auto-encoder. An auto-encoder is a
neural network that takes an input and provides a reconstruc-
tion of this input. We justify below two of the most critical
choices for the SWTA AE architecture.
2.1. Strided convolution
A compression algorithm must process images of various
sizes. However, the most efficient neural networks [8, 9] re-
quire that all images have the same size. Indeed, they include
both convolutional layers and fully-connected layers, and the
number of parameters of the latters directly depends on the
image size. This imposes to train one architecture per im-
age size. That is why our proposed SWTA AE only contains
convolutional layers. Its encoder has two convolutional lay-
ers and its decoder has two deconvolutional layers [10]. Each
layer i ∈ J1, 4K consists in convolving the layer input with
the bank of filters W
(i)
, adding the biases b
(i)
and applying
a mapping g
(i)
, producing the layer output. For the borders of
the layer input, zero-padding of width p
(i)
is used.
Max-pooling is a core component of neural networks [11]
that downsamples its input representation by appling a max
Fig. 1: SWTA AE architecture.
filter to non-overlapping sub-regions. But max-pooling in-
creases the rate. Indeed, if the encoder contains a max-
pooling layer, the locations of maximum activations selected
during pooling operations must be recorded and transmitted
to the corresponding unpooling layer in the decoder [12, 13].
Instead, for i ∈ J1, 2K, we downsample using a fixed stride
s
(i)
> 1 for convolution, which does not need any signaling.
2.2. Semi-sparse bottleneck
The bottleneck is the stack of feature maps denoted Z ∈
R
h×w×65
in Figure 1. Z is the representation of the input
image that is processed in Section 2.3 to give the bitstream.
We propose to apply a global sparse constraint that po-
vides control over the coding cost of Z. This is called
Winner-Take-All (WTA). Let us define WTA via a mapping
g
α
: R
h×w×64
→ R
h×w×64
, where α ∈]0, 1[ is the WTA
parameter. g
α
keeps the α × h × w × 64 most representa-
tive coefficients in its input tensor, i.e. those whose absolute
values are the largest, and sets the rest to 0. g
α
only applies
to the output of the convolution in the second layer involving
the first 64 filters in W
(2)
, producing the first 64 sparse fea-
ture maps in Z. Figure 1 displays these sparse feature maps
in orange. Varying α leads to various coding costs of Z. Note
that [14] uses WTA, but our WTA rule is different and g
α
does not apply to specific dimensions of its input tensor as
this constraint is not relevant for image compression.
A patch of the input image might be represented by a por-
tion of the first 64 sparse feature maps in Z that only con-
tains zeros. We want to ensure that each image patch has a
minimum code in Z to guarantee a sufficient quality of recon-
struction per patch. That is why the last feature map in Z is
not sparse. Figure 1 displays it in red. We have noticed that,
during the training in Section 4.2, SWTA AE learns to store in
the last feature map a subsampled version of its input image.
2.3. Bitstream generation
The coefficients of the non-sparse feature map in Z are uni-
formly quantized over 8-bits and coded with a Huffman code.
The non-zero coefficients of the 64 sparse feature maps in Z
are uniformly quantized over 8-bits and coded with a Huff-
man code while their position is coded as explained here-
after. Figure 1 defines a coordinate system (x, y, z) for Z.
The non-zero coefficients in Z are scanned along (x, y, z)
where z changes the fastest. The position along z is coded
with a fixed-length code and, for each pair (x, y), the num-
ber of non-zero coefficients along z is coded with a Huffman
code. This unequivocally characterizes the position of each
non-zero coefficient in Z. We have observed that this pro-
cessing is effective in encoding the position of the non-zero
coefficients.
3. WINNER-TAKE-ALL ORTHOGONAL
MATCHING PURSUIT (WTA OMP)
SWTA AE is similar to Orthogonal Matching Pursuit (OMP)
[15], a common algorithm for image compression using
sparse representations [16]. The difference is that SWTA AE
computes the sparse representation of an image by alternat-
ing convolutions and mappings whereas OMP runs an iter-
ative decomposition of the image patches over a dictionary.
More precisely, let x ∈ R
m
be an image patch. Given x and
a dictionary D ∈ R
m×n
, OMP finds a vector of coefficients
y ∈ R
n
with k < m non-zero coefficients so that Dy equals
to x approximatively.
For the sake of comparison, we build a variant of OMP
called Winner-Take-All Orthogonal Matching Pursuit (WTA
OMP). More precisely, let X ∈ R
m×p
be a matrix whose
columns are formed by p image patches of dimension m and
Y ∈ R
n×p
be a matrix whose columns are formed by p vec-
tors of coefficients of dimension n. WTA OMP first decom-
poses each image patch over D, see (1). Then, it keeps the
γ × n × p coefficients with largest absolute value for the n-
length sparse representation of the p patches and sets the rest
to 0, see (2). The support of the sparse representation of each
patch has therefore been changed. Hence the need for a final
least-square minimization, see (3).
Algorithm 1 : WTA OMP
Inputs: X ∈ R
m×p
, D ∈ R
m×n
, k < m and γ ∈]0, 1[.
For each j ∈ J1, pK, Y
j
= OMP (X
j
, D, k) (1)
I = f
γ
(Y) (2)
For each j ∈ J1, pK, Z
j
= min
z∈R
n
kX
j
− Dzk
2
2
st.
supp (z) = supp (I
j
)
(3)
Output: Z ∈ R
n×p
.
4. TRAINING
Before moving on to the image compression experiment in
Section 5, SWTA AE needs training. Similarly, a dictionary
D ∈ R
m×n
must be learned for WTA OMP.
4.1. Training data extraction
We extract 1.0 × 10
5
RGB images from the ILSVRC2012
ImageNet dataset [17]. The RGB color space is transformed
into YCbCr and we only keep the luminance channel.
For SWTA AE, the luminance images are resized to
321 ×321. M ∈ R
321×321
denotes the mean of all luminance
images. σ ∈ R
∗
+
is the mean of the standard deviation over
all luminance images. Each luminance image is subtracted by
M and divided by σ. These images are concatenated into a
training set ∆ ∈ R
321×321×
(
1.0×10
5
)
.
For WTA OMP, η = 1.2 × 10
6
image patches of size
√
m ×
√
m are randomly sampled from the luminance im-
ages. We remove the DC component from each patch. These
patches are concatenated into a training set Γ ∈ R
m×η
.
4.2. SWTA AE training
As explained in Section 2.2, α tunes the coding cost of Z. If α
is fixed during training, all the filters and the biases of SWTA
AE are learned for one rate. That is why we turn α into a
stochastic hyperparameter during training. This justifies the
prefix “Stochastic” in SWTA AE. Since there is no reason to
favor some rates during training, we sample α according to
the uniform distribution U[µ −, µ + ], where µ − > 0 and
µ + < 1. We select µ = 1.8 × 10
−1
and = 1.7 × 10
−1
to make the support of α large. At each training epoch, α is
drawn for each training image of ∆.
As shown in Section 2.1, SWTA AE can process images
of various sizes. During training, we feed SWTA AE with
random crops of size 49×49 of the training images of ∆. This
accelerates training considerably. The training objective is to
minimize the mean squared error between these cropped im-
ages and their reconstruction plus l
2
-norm weights decay. We
use stochastic gradient descent. The gradient descent learn-
ing rate is fixed to 2.0 × 10
−5
, the momentum is 0.9 and the
size of mini-batches is 5. The weights decay coefficient is
5.0 × 10
−4
. Our implementation is based on Caffe [18]. It
adds to Caffe the tools introduced in Sections 2.2 and 2.3.
4.3. Dictionary learning for WTA OMP
Given Γ, k < m and γ ∈ ]0, 1[, the dictionary learning prob-
lem is formulated as (4).
min
D,Z
1
,...,Z
η
1
η
η
X
j=1
kΓ
j
− DZ
j
k
2
2
st. ∀j ∈ J1, ηK, kZ
j
k
0
≤ k
st.
η
X
i=j
kZ
j
k
0
≤ γ × n × η
(4)
(4) is solved by Algorithm 2 which alternates between sparse
coding steps that involve WTA OMP and dictionary updates
that use stochastic gradient descent. Given Γ and p ∈ N
∗
+
,
let φ be a function that randomly partitions Γ into η
p
=
η / p mini-batches
X
(1)
, ..., X
(η
p
)
, where, for i ∈ J1, η
p
K,
X
(i)
∈ R
m×p
. Mini-batches make learning very fast [19].
Algorithm 2 : dictionary learning for WTA OMP.
Inputs: Γ ∈ R
m×η
, k < m, γ ∈]0, 1[, p ∈ N
∗
+
and ε ∈ R
∗
+
.
D ∈ R
m×n
is randomly initialized.
∀j ∈ [|1, n|], D
j
← D
j
/ kD
j
k
2
For several epochs do:
h
X
(1)
, ..., X
(η
p
)
i
= φ (Γ, p)
∀i ∈ [|1, η
p
|], Z
(i)
= WTA OMP
X
(i)
, D, k, γ
D ← D − ε
∂
X
(i)
− DZ
(i)
2
F
∂D
∀j ∈ [|1, n|], D
j
← D
j
/ kD
j
k
2
Output: D ∈ R
m×n
.
For OMP, given Γ, a dictionary D
0
∈ R
m×n
is learned
using K-SVD [16]
1
, and the parameters m and n are opti-
mized with an exhaustive search. This leads to m = 64 and
n = 1024. For SWTA AE, the same values for m and n
are used for training D via Algorithm 2. Moreover, k = 15,
γ = 4.5 × 10
−3
, p = 10 and ε = 2.0 × 10
−2
.
1
K-SVD code: http://www.cs.technion.ac.il/ elad/software/
Fig. 2: Evolution of PNSR with the rate.
(a) LENA luminance 512 × 512. (b) BARBARA luminance 480 × 384.
5. IMAGE COMPRESSION EXPERIMENT
After training in Section 4, we compare the rate-distortion
curves of OMP, WTA OMP, SWTA AE, JPEG and JPEG2000
on test luminance images.
5.1. Image CODEC for SWTA AE
Each input test luminance image is pre-processed similarly to
the training in Section 4.1. The mean learned image M is
interpolated to match the size of the input image. Then, the
input image is subtracted by this interpolated mean image and
divided by the learned σ. The encoder of SWTA AE computes
Z. The bitstream is obtained by processing Z as detailed in
Section 2.3.
5.2. Image CODEC for OMP and WTA OMP
A luminance image is split into 8×8 non-overlapping patches.
The DC component is removed from each patch. The DC
components are uniformly quantized over 8-bits and coded
with a fixed-length code. OMP (or WTA OMP) finds the co-
efficients of the sparse decompositions of the image patches
over D
0
(or D). The non-zero coefficients are uniformly
quantized over 8-bits and coded with a Huffman code while
their position is coded with a fixed-length code.
Then, for WTA OMP only, the number of non-zero coef-
ficients of the sparse decomposition of each patch over D is
coded with a Huffman code.
5.3. Comparison of rate-distortion curves
In the literature, there is no reference rate-distortion curve
for auto-encoders. We compare SWTA AE with JPEG and
JPEG2000
2
even though the image CODEC of SWTA AE is
less optimized. Furthermore, we compare SWTA AE with its
2
JPEG and JPEG2000 code: http://www.imagemagick.org/script/index.php
non-sparse Auto-Encoder counterpart (AE). AE has the same
architecture as SWTA AE but its Z only contains non-sparse
feature maps. Note that, to draw a new point in the AE rate-
distortion curve, AE must be first re-trained with a different
number of feature maps in Z.
Figure 2 shows the rate-distortion curves of OMP, WTA
OMP, AE, SWTA AE, JPEG and JPEG2000 for two of the
most common images: LENA and BARBARA. In terms
of rate-distortion trade-off, SWTA AE outperforms AE and
WTA OMP is better than OMP. This highlights the value
of WTA for image compression. When we compare SWTA
AE with WTA OMP, we see that iterative decomposition
is more efficient for image compression using sparse repre-
sentations. Moreover, SWTA AE can compete with JPEG.
We also ran this image compression experiment on several
crops of LENA and BARBARA and observed that the rel-
ative position of the six rate-distortion curves was compa-
rable to the relative positioning in figure 2. The size of
the test image does not affect the performance of SWTA
AE. More simulation results and a complexity analysis for
OMP, WTA OMP and SWTA AE can be found on the
web page https://www.irisa.fr/temics/demos/
NeuralNets/AutoEncoders/swtaAE.htm.
6. CONCLUSIONS AND FUTURE WORK
We have shown that, SWTA AE is more adaptated to image
compression than auto-encoders as it performs variable rate
image compression for any size of image after a single train-
ing and provides better rate-distortion trade-offs.
So far, our work has focused on the layer of auto-encoders
which is dedicated to coding. Yet, many avenues of research
are still to be explored to improve auto-encoders for image
compression. For instance, [20] proves that removing a max-
pooling layer and increasing the stride of the previous con-
volution, as we do, harms neural networks. This has to be
addressed.
