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Multiple Description Convolutional Neural
Networks for Image Compression

arXiv:1801.06611v1 [cs.MM] 20 Jan 2018

Lijun Zhao, Huihui Bai, Member, IEEE, Anhong Wang, Member, IEEE, and Yao Zhao, Senior Member, IEEE

Abstract—Multiple description coding (MDC) is able to
stably transmit the signal in the un-reliable and non-prioritized
networks, which has been broadly studied for several decades.
However, the traditional MDC doesn’t well leverage image’s
context features to generate multiple descriptions. In this paper,
we propose a novel standard-compliant convolutional neural
network-based MDC framework in term of image’s context
features. Firstly, multiple description generator network
(MDGN) is designed to produce appearance-similar yet
feature-different multiple descriptions automatically according
to image’s content, which are compressed by standard codec.
Secondly, we present multiple description reconstruction
network (MDRN) including side reconstruction networks (SRN)
and central reconstruction network (CRN). When any one of
two lossy descriptions is received at the decoder, SRN network
is used to improve the quality of this decoded lossy description
by removing the compression artifact and up-sampling
simultaneously. Meanwhile, we utilize CRN network with two
decoded descriptions as inputs for better reconstruction, if both
of lossy descriptions are available. Thirdly, multiple description
virtual codec network (MDVCN) is proposed to bridge the gap
between MDGN network and MDRN network in order to train
an end-to-end MDC framework. Here, two learning algorithms
are provided to train our whole framework. In addition to
structural similarity loss function, the produced descriptions
are used as opposing labels with multiple description distance
loss function to regularize the training of MDGN network.
These losses guarantee that the generated description images
are structurally similar yet finely diverse. Experimental results
show a great deal of objective and subjective quality
measurements to validate the efficiency of the proposed
method.
Index Terms—Multiple description network, distance loss,
virtual codec, learning, coding artifacts.

I. I NTRODUCTION

L

ARGE amounts of attentions have been paid to various
techniques of Internet service and multimedia signal
transmission for many years, which not only provide us a
convenient manner of communication but also give us many
choices for our life style. Meanwhile, the bandwidth of
Internet has been accelerated and more stable transmission
service is guaranteed by these developments. But there are
still some risks of transmission failures, when the Internet
congestion occurs in the overloaded case or signal packets
are conveyed in the unpredictable yet unreliable channels
L. Zhao, H. Bai, Y. Zhao are with the Beijing Key Laboratory of Advanced
Information Science and Network Technology, Institute Information Science,
Beijing Jiaotong University, Beijing, 100044, P. R. China, e-mail: 15112084,
hhbai, yzhao@bjtu.edu.cn.
A. Wang is with Institute of Digital Media & Communication, Taiyuan
University of Science and Technology, Taiyuan, 030024, P. R. China, e-mail:
wah ty@163.com

[1, 2]. Multiple description coding has been studied as a
promising technique of source coding to relieve these
problems by decomposing the signal into multiple redundant
subsets, which are transmitted in different channels. Thus, a
degraded but acceptable signals reconstruction can be
produced after decoding, even though only one description is
received at the clients. If more descriptions are available for
users, better quality of signal reconstruction can be achieved.
Multiple description coding has been widely explored in the
field of image and video coding [3–21].
As one of the main techniques in multiple description
image coding, multiple description scalar quantization could
overcome impairments of transmission channel [6]. For
example, in [7], multiple description scalar quantizers have
been combined with efficient wavelet coders to generate
independent multiple packets for error resilience. In [5],
two-stage multiple description scalar quantization is
presented to create central and side decoders, whose
distortions are closer to the rate-distortion bound of multiple
description coding under the condition of the high-resolution
assumption. To cope with the L-description problem[9], two
novel coding schemes are proposed, when the symmetric
rates and symmetric distortion is constrained. In [3], a new
achievable rate-distortion region with combinatorial message
sharing is presented by introducing shared codebooks and
the refinement codebook to generate L-channel multiple
descriptions.
Compared with multiple description scalar quantization,
lattice vector quantization characterizes in good symmetric
structure of lattices and avoiding complex nearest neighbor
searching. In [10], the main problem of designing lattice
vector quantizer is formulated as a labeling problem for
two-channel multiple description. In [11], non-lattice
codebook with symmetries of the coarse lattice is used to get
objective quality gains for multiple description coding but
without a great increase of complexity. In [12], multiple
description lattice vector quantization is operated in an
optimized way in terms of appropriate construction of
wavelet coefficient vectors, choosing sub-lattice index values
and different subbands quantization step on the wavelet
domain. In [8], the index assignment of multiple description
lattice vector quantization is designed to be translated into a
transportation problem and greedy algorithm as well as
general algorithms is developed to pursue optimality of the
index assignment.
Except multiple descriptions directly produced by
quantization, there are many alternative strategies for
multiple description coding. To generate two descriptions in

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transform based coding framework, correlation between pairs
of transform coefficients is introduced by a pairwise
correlating transform [13]. This correlation facilities to
reduce the distortion when only a single description is
received. Later, both domain-based multiple description
coding and forward error correction are used for
concatenated multiple description coding of frame-rate
scalable video [14]. Meanwhile, both prioritized discrete
cosine transform in video compression and multiple
description codes based on forward error correction are
combined together to provide a wireless channels video
transmission scheme [15].
From literatures [14, 15], it can be observed that multiple
description video coding using forward error correction has
been widely explored. There are several other kinds of
multiple description video coding. In [16], a video is coded
into multiple independently streams so that each stream has
its own prediction and dependent state to defeat against bit
error or packet loss. In multiple description motion coding
algorithm, motion vector is encoded into two descriptions,
which are transmitted over distinct channels to the decoder
so that motion vector field is robust against transmission
errors [17]. In the scalable wavelet video codec, each packet
is encoded with a separate channel code, so that the integrity
of the packets is protected and it allows to detect
packet-decoding failures cases, after breaking wavelet
transformation into several spatial-temporal tree blocks [18].
In [19], two architectures of multiple description video
coding are built up based on motion compensation prediction
loop and a poly-phase down-sampling technique is chosen to
generate multiple descriptions and introduce cross
redundancy among the descriptions.
Although the aforementioned approaches can well
alleviate the congestion of Internet and satisfy the
demanding of real-time application, these approaches are not
compatible to standard codec, such as JPEG, and JPEG2000.
To resolve this problem, some previous works have provided
some feasible solutions, such as [4, 8, 20, 21]. In [21],
through grouping the codeblock to generate two balanced
set, these two set are compressed by JPEG-2000 with two
different quantization parameter to get four subsets, which
are interlacedly merged together to create two descriptions.
In [20], the rate-allocation strategy embedded in the
JPEG2000 encoder is introduced for the rate-distortion
optimization of multiple descriptions of images, in which
single description decoding is able to compatible with
JPEG2000 Part 1 decoder. In view of human eyes’ always
sensitivity to the changes above just noticeable difference
(JND) threshold, only the significant visual information,
which contributes to the JND tolerance, is encoded as the
redundant information during H.264/AVC based multiple
description video coding [4]. In [8], frame-level
rate-distortion optimized description generation scheme takes
account of temporal coding dependency to minimize the
end-to-end distortion, which is built on standard H.264/AVC.
Because the proposed approach is high related about the
issue of compression artifact removal [22–30], we will next
review several state-of-the-art works about compression

2

artifact removal. In [22], pointwise shape-adaptive discrete
cosine transform is leveraged for both denoising and
deblocking after image compression. In [23], dictionary
learning is introduced to reducing JPEG-compressed artifacts
in view of image’s sparse and redundant representations. In
[24], collaborative filtering is designed to uncover the finest
details and maintain each individual block’s unique features
in the sparse 3-D transform-domain, which is not restrict to
the denoising of compressed image, so this approach is a
general denoising method. Lately, the deblocking problem is
formulated as an optimization problem, where non-convex
low-rank model constrained is considered to reduce blocking
artifacts [25]. Meanwhile, the popular techniques of
convolutional neural network and generative adversarial have
been tried to remove artifacts [27–29].
Following the work of [19], we form multiple description
coding baselines with a poly-phase down-sampling technique
to
generate
multiple
descriptions
by
combining
state-of-the-art
artifact
removal
technique
with
super-resolution based on very deep convolutional neural
network. Specifically, the input image is down-sampled with
a poly-phase down-sampling technique along the main
diagonal for each 2 × 2 non-overlapped window to form two
descriptions for coding with standard codec. After decoding,
several state-of-the-art artifact removal techniques, such as
[22–25] are used to enhance image quality, which is
followed by super-resolution to restore image from
low-resolution to high-resolution with very deep
convolutional neural network, such as novel super-resolution
methods of [31] and [32]. The combinatorial methods with
artifact removal [22–25] and super-resolution [31] are
respectively referred to as multiple description coding
baselines1-4, namely ”MDB1a”, ”MDB2a”, ”MDB3a”,
”MDB4a”. In this similar way, when artifact removal
methods of [22–25] are combined together with [32], they
are respectively denoted as ”MDB1b”, ”MDB2b”,
”MDB3b”, ”MDB4b”.
In this paper, we introduce a novel standard-compatible
multiple description coding framework, in which multiple
descriptions are produced by deep convolutional neural
network. Our contributions are listed as follows:
• Multiple description generator network (MDGN) is
introduced to adaptively generate multiple descriptions
according to image’s content, which are compressed by
standard codec to reduce transmission bits.
• We present multiple description reconstruction network
(MDRN), which consists of side reconstruction
networks (SRN) and central reconstruction network
(CRN). When either one of two compressed description
is received at the decoder, side reconstruction
network-A network (SRNA) or side reconstruction
network-B (SRNB) is used to reconstruct the lossy
description and enlarge this description simultaneously
by removing compression artifact and up-sampling.
Meanwhile, we utilize CRN network with two received
descriptions as inputs to achieve high-quality image
reconstruction, if all the multiple description images are
available.

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Fig. 1. The framework of multiple description coding based on deep convolutional neural networks

We train the aforementioned two neural networks:
MDGN network and MDRN network together by
learning multiple description virtual codec network
(MDVCN). It means that the learned MDVCN network
is leveraged to further supervise the MDGN network’s
training. Besides, we provide two kinds of learning
algorithms for training our convolutional neural
networks.
• Distance loss for MDGN network is introduced as well
as structural similarity loss to guarantee that the
generated description images are structurally similar yet
finely different.
The rest of this paper is given as follow. We first introduce the
proposed methodology in Section 2. After that, we conduct a
series of the experimental results to validate the efficiency in
the Section 3. At last, we give a conclusion in the Section 4.
•

II. T HE METHODOLOGY
In this paper, multiple description coding framework based
on deep convolutional neural network is introduced to
efficiently compress images, when facing an unpredictable
and non-prioritized channel. Our main works are put on how
to generate multiple descriptions in terms of redundancy
between each description and description’s diversity for
better central reconstruction. Meanwhile, we design the
neural network for description’s generation and
reconstruction and introduce how to train our convolutional
neural networks together used in the proposed method. To
the best of our knowledge, this is the first work using
convolutional neural network for multiple description coding.

A. Framework
Our multiple description coding framework has three
components: MDGN network, standard codec of JPEG,
MDRN network, as depicted in the Fig. 1. The MDGN
network g(I, ω) is responsible to generate diverse
descriptions IA and IB from the ground-truth image I with
size of M × N . Here, ω is the parameter set of MDGN
network and other networks’ parameter set can be defined in
this similar way. Due to the widely usage of standard codec,
such as JPEG, the standard-compatible coding framework
becomes significant for practical applications. Thus, we use
the JPEG codec to compress these descriptions so that image
redundancy can be further reduced to get the lossy
descriptions IˆA and IˆB . The JPEG compressions of IA and
IB are respectively represented as IˆA = c(IA , φ), and
IˆB = c(IB , φ), where c(·) is the compression function of
codec. The compressed description streams are separately
transmitted over different channels. However, image
compression with standard codec often incurs coding
artifacts. Thus, MDRN network, denoted as reconstruction
function R(·), is leveraged to remove these artifacts for
image enhancement and enlarge the lossy description so that
the final reconstruction image is guaranteed to have the same
size with the ground-truth image I. Finally, the receiver can
still decode the received packet to get a description for
acceptable quality reconstruction I˜A with SRNA network or
I˜B with SRNB network, even though any one description is
missing, as displayed in the Fig. 1. If both descriptions are
received, high quality reconstruction I˜ can be built by CRN
network.

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As we all know, it’s not easy to jointly train the MDGN
network and MDRN network, because the quantization
function in the codec of lossy compression is
non-differentiable. Thus, the reconstruction error from the
MDRN network can’t be directly back-propagated to the
MDGN network. Following our previous work [33], we learn
the MDVCN network to imitate the two consecutive
procedures of codec’s compression and description’s
reconstruction with MDRN network. As a result, we can
train our whole framework in an end-to-end fashion.
B. Objective function
The objective function for our multiple description coding
framework is written as follows:
arg min LM DGN (IA , IB , I, ω)
ω,α,θ

+ LM DRN (IA , IB , I, α) + LM DV CN (IA , IB , θ),

(1)

α = [α1 , α2 , α3 ],
θ = [θ1 , θ2 , θ3 ],
[IA , IB ] = g(I, ω),

(2)

where three losses for training are respectively the loss of
MDGN network, the loss of MDRN network, and MDVCN
network’s loss.
LM DGN (IA , IB , I, ω) = LSSIM (u(IA ), I)

i

+ LSSIM (u(IB ), I) + βLdis (IA , IB ),
β = clip(0.2/QF, κ1, κ2)

(3)

The loss of LM DGN (IA , IB , I, ω) is used to supervise
the learning of the parameters ω of the MDGN network in
Eq. (3), where u(·) is the linear up-sampling function and β
balances the contributions between descriptor’s SSIM loss
[34] and distance loss, which are in effect contradictory to a
certain extent. In addition, QF is the quality factor for JPEG
compression and clip(·) is the clip function to restrict value
between κ1 and κ2 (e.g., κ1 = 5 ∗ 10−3 and κ1 = 5 ∗ 10−2 ).
Hence, the parameter of β plays a significant role on
generating valid multiple descriptions. Note that the better
quality is encoded, when the larger QF is set for JPEG.
On one hand, we hope that the two produced descriptions
structurally similar to the input image so that the decoded
descriptions can be watched directly for receiver, even without
the processing of MDRN network. Consequently, SSIM loss
function is used to supervise each description’s learning. For
example, the SSIM for description IA is defined as follows:
1 X
LSSIM (u(IA )i , Ii ), (4)
LSSIM (u(IA ), I) = −
M ·N
i

LSSIM (u(IA )i , Ii ) =
(2µu(IA )i · µIi + c1)(2σu(IA )i Ii + c2)
,
2
(µ2u(IA )i + µ2Ii + c1)(σu(I
+ σI2i + c2)
A )i

by pixel i in the image u(IA ). Similarly, µIi as well as σI2i
is denoted in this way. σu(IA )i Ii is the covariance between
neighbourhood windows centered by pixel i in the image
u(IA ) and in the image I. Meanwhile, c1 and c2 are two
constant values (e.g., c1 = 1 × 10−4 , and c2 = 9 × 10−4 ).
As a matter of fact, the calculation of mean value is a
special kind of convolution, which is also named by average
pooling, while variance operation actually involve twice
operations of average pooling. It’s obvious that the function
of SSIM in Eq. (4-5) is differentiable, so the SSIM error can
be efficiently back-propagated via optimization.
On the other hand, according to the Gamal and Cover
theorem of [35, 36], the MDGN network should pledge to
have mutual information between two generated descriptions
so that we can receive a acceptable reconstruction, even
when only one description is got at the client. It’s obvious
that SSIM loss function keeps the two descriptions yielded
by the MDGN network structurally similar. In the meantime,
the two produced descriptions by neural networks are used
as opposing labels to regularize the training of MDGN
network.
Consequently,
the
high-quality
central
reconstruction with two diverse descriptions can be
guaranteed. Contrary to the SSIM loss, the distance loss
function is utilized to keep the detail difference between two
descriptions, which is written as:
1 X
(||IAi − IB i ||L ). (6)
Ldis (f (IA ), f (IB )) = −
M ·N
For brevity latter, the content loss function and gradient
difference loss function between two images X and Y are
defined as:
1 X
(||Xi − Yi ||L ),
Lc (X, Y ) =
(7)
M ·N i
Lgd (X, Y ) =

1 X X
((
||∇s Xi − ∇s Yi ||L )), (8)
M ·N i
s∈Ωi

where ∇s is the s-th gradient between each pixel and s-th
pixels among 8-neighbourhood Ωi . Here, L1-norm is chosen to
produce sharper results than L2-norm, which has been reported
in [37, 38].
In the MDRN network, both content loss Lc and gradient
difference loss Lgd supervise the learning of side
reconstruction R(c(IA , φ), α1 ), R(c(IB , φ), α2 ) and central
reconstruction R(c(IA , φ), c(IB , φ), α3 ), which is presented
as follows:
LM DRN (IA , IB , I, α) =
Lc (I, R(c(IA , φ), α1 )) + Lgd (I, R(c(IA , φ), α1 ))
+ Lc (I, R(c(IB , φ), α2 )) + Lgd (I, R(c(IB , φ), α2 ))
+ Lc (I, R(c(IA , φ), c(IB , φ), α3 ))
+ Lgd (I, R(c(IA , φ), c(IB , φ), α3 )).

(5)

2
where µu(IA )i and σu(I
respectively denote the mean
A )i
value and the variance of the neighborhood window centered

(9)

In order to back-propagate the error from the MDRN
network to the MDGN network, we learn MDVCN network
to approximate the procedure from the lossless descriptions
to the lossy description reconstruction. Both content loss and

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TABLE I
T HE STRUCTURE OF MDGN NETWORK
Layer
conv-1f
conv-2f
conv-3f
conv-4f
conv-5A
conv-6A
convs-7A
conv-8A
conv-5B
conv-6B
conv-7B
conv-8B

k
9
3
3
3
3
3
3
9
3
3
3
9

MDGN Network
s
c-in
c-out
1
1
128
2
128
128
1
128
128
1
128
128
1
128
128
1
128
128
1
128
128
1
128
1
1
128
128
1
128
128
1
128
128
1
128
1

TABLE II
T HE STRUCTURE OF MDRN NETWORK

input
I
conv-1f
conv-2f
conv-3f
conv-4f
conv-5A
conv-6A
conv-7A
conv-4B
conv-5B
conv-6B
conv-7B

gradient difference loss are used to regularize the training of
MDVCN network, which are given as follows:
LM DV CN (IA , IB , θ) =
Lc (I˜A , V (IA , θ1 )) + Lgd (I˜A , V (IA , θ1 ))
+ Lc (I˜B , V (IB , θ2 )) + Lgd (I˜B , V (IB , θ2 ))
˜ V (IA , IB , θ3 )) + Lgd (I,
˜ V (IA , IB , θ3 )).
+ Lc (I,

(10)

In addition to the aforementioned loss, we use MDVCN
network to explicitly supervise the learning of the MDGN
network or directly use gradient from MDVCN network as
the gradient approximation from the standard codec. It’s
worth noticing that MDVCN network does not be used any
more, once the whole training is finished, that is to say, only
the MDRN network and the MDGN network during the
testing are respectively leveraged to create multiple
descriptions for compression and reconstruct these
descriptions.
C. Network architecture
The MDGN network is composed with eight convolutional
layers, which has one input stream, but two output streams,
that is to say, the extracted feature maps with feature
extraction network (FEN) from layer 1-4 are shared by
generator network-A (GNA) and generator network-B
(GNB). The FEN network has four convolutional layers,
whose first layer’s spatial kernel size is 9 × 9 and other
layers’ is 3 × 3. In the GNA and GNB networks, there are
four convolutional layers with spatial kernel size 3 × 3
except for the last layer with 9 × 9. The large spatial kernel
9 × 9 of convolutional layer in the first layer and last layer
could further enlarge the receptive field of convolutional
networks on the basis of small kernel 3 × 3. Hence, image’s
context information is well considered during the generation
of descriptions. The details about each layer in the MDGN
network are listed in the Table I, where ”k” represents the
kernel size, ”c-in” denotes the number of channel input,
”c-out” is the total output map’s number in the
corresponding layer. Meanwhile, ”conv” represents
convolutional
layer
and
”deconv”
indicates
the
de-convolutional layer. From this table, it can be seen that all
the layers employ stride step 1 except for the second
convolutional layer with stride of 2. All the convolutional

Layer
conv-1a
conv-2a
conv-3a
conv-4a
conv-5a
conv-6a
conv-7a
deconv-8a

k
9
3
3
3
3
3
3
9

Layer
conv-1b
conv-2b
conv-3b
conv-4b
conv-5b
conv-6b
conv-7b
deconv-8b

k
9
3
3
3
3
3
3
9

Layer
conv-1c
conv-2c
conv-3c
conv-4c
conv-5c
conv-6c
conv-7c
deconv-8c

k
9
3
3
3
3
3
3
9

SRNA Network
s
c-in
c-out
1
1
128
1
128
128
1
128
128
1
128
128
1
128
128
1
128
128
1
128
128
2
128
1
SRNB Network
s
c-in
c-out
1
1
128
1
128
128
1
128
128
1
128
128
1
128
128
1
128
128
1
128
128
2
128
1
CRN Network
s
c-in
c-out
1
2
128
1
128
128
1
128
128
1
128
128
1
128
128
1
128
128
1
128
128
2
128
1

input
IˆA
conv-1a
conv-2a
conv-3a
conv-4a
conv-5a
conv-6a
conv-7a
input
IˆB
conv-1b
conv-2b
conv-3b
conv-4b
conv-5b
conv-6b
conv-7b
input
IˆA and IˆB
conv-1c
conv-2c
conv-3c
conv-4c
conv-5c
conv-6c
conv-7c

TABLE III
T HE STRUCTURE OF MDVCN NETWORK
Layer
conv-1a
conv-2a
conv-3a
conv-4a
conv-5a
conv-6a
conv-7a
deconv-8a

k
9
3
3
3
3
3
3
9

Layer
conv-1b
conv-2b
conv-3b
conv-4b
conv-5b
conv-6b
conv-7b
deconv-8b

k
9
3
3
3
3
3
3
9

Layer
conv-1c
conv-2c
conv-3c
conv-4c
conv-5c
conv-6c
conv-7c
deconv-8c

k
9
3
3
3
3
3
3
9

VSRNA Network
s
c-in
c-out
1
1
128
1
128
128
1
128
128
1
128
128
1
128
128
1
128
128
1
128
128
2
128
1
VSRNB Network
s
c-in
c-out
1
1
128
1
128
128
1
128
128
1
128
128
1
128
128
1
128
128
1
128
128
2
128
1
VCRN Network
s
c-in
c-out
1
2
128
1
128
128
1
128
128
1
128
128
1
128
128
1
128
128
1
128
128
2
128
1

input
IA
conv-1a
conv-2a
conv-3a
conv-4a
conv-5a
conv-6a
conv-7a
input
IB
conv-1b
conv-2b
conv-3b
conv-4b
conv-5b
conv-6b
conv-7b
input
IA and IB
conv-1c
conv-2c
conv-3c
conv-4c
conv-5c
conv-6c
conv-7c

layers are activated by the ReLU activation function apart
from the last layer in the MDGN network.
The MDRN network consists of SRNA network, SRNB
network, and CRN network. In fact, we can let SRNA
network and SRNB network share the same parameter set.
Meanwhile, CRN network uses the outputs from the
SRNA-network and SRNB-network to reconstruct the central
images. But, in order to better back-propagate the errors

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from the MDRN network to the previous networks and avoid
too deep networks for central reconstruction, we use three
separate networks without cross connection and no weights
sharing to respectively reconstruct side images and central
image. They all use the eight convolutional layers. Seven
convolutional layers and one deconvolution layer are used in
the MDRN network so as to remove the coding artifacts and
up-scale feature maps to the full-resolution at the same time.
The obvious difference between them is that CRN network
has two lossy descriptions as input while the two other
networks only have one lossy descriptions as input. All the
details are specified in the Table II, from which we can
observe that the first and last convolutional layers use the
9 × 9 spatial kernel to ensure the receptive field large
enough, so that more spatial features are captured to better
reconstruct the degraded descriptions. In addition, all the
convolutional layers are activated by the ReLU, but the last
layers of SRNA network, SRNB network, and CRN network
are processed without any activation.
As described above, MDVCN network bridges the gap
between MDGN network and MDRN network so that the
errors of the reconstruction can be properly back-propagated
from MDRN network to MDGN network. MDVCN network
and MDRN network are designed to have same structure,
because they can be seen as the same class of low-level
image processing problems by learning. Thus, we have three
virtual networks for MDVCN network: virtual side
reconstruction
network-A
(VSRNA),
virtual
side
reconstruction network-B (VSRNB), and virtual central
reconstruction network (VCRN), whose network structures in
the Table III are similar to the one’s of MDRN network in
the Table II. However, the inputs of MDVCN network and
MDRN network are different, in which the former one takes
the decoded lossy descriptions IˆA and IˆB as inputs, while
the later one is fed with lossless multiple descriptions IA
and IB .
D. Network learning
Obviously, it’s challenging to learn our whole framework
directly, but our problem of learning multiple description
neural networks can be separated into several sub-problems
learning. In order to resolve these problems, we provide two
learning ways for error back-propagation. These two ways
are presented in the following and respectively referred to as
learning algorithm-1 and learning algorithm-2. Our learning
algorithm-1 treats MDVCN network as feature function to
build the reconstruction by fixing the parameter of MDVCN
network so that reconstruction errors from MDVCN network
can be back-propagated for the supervision of the MDGN
network ahead of standard codec. It means that the MDGN
network and the MDRN network are trained separately. On
the contrary, our learning algorithm-2 uses MDVCN
network’s back-propagated error for MDGN network to
approximately estimate the error from the codec without
fixing any network’s parameter, when explicitly training the
MDGN network and the MDRN network simultaneously.
The details about these two learning algorithms will be
described next.

6

Algorithm 1 Learning Multiple Description Neural Networks
Input: Ground truth image: I; the number of iteration: R; the total number of images
for training: n; the batch size during training: m;
Output: The parameter sets of MDGN network and MDRN network: ω, α;
1: Initialize to produce multiple descriptions IA and IB by down-sampling for
preparation of the training of MDRN network;
2: Initialize parameter sets: ω, α, φ, θ;
3: for r = 1 to R do
4:
Compress multiple descriptions IA and IB by standard codec with φ;
5:
for epoch = 1 to p do
6:
for i = 1 to floor(n/m) do
7:
Update the parameter set of α to train the MDRN network according to
8:
the minimization of the Eq. (9) with i-th batch images;
9:
end for
10:
end for
11:
Generate the multiple descriptions reconstruction dataset I˜A and I˜B with the
12:
parameter set of α by the MDGN network;
13:
for epoch = 1 to p do
14:
for j = 1 to floor(n/m) do
15:
Update the parameter set of θ by training MDVCN network to minimize
16:
the Eq. (10) with j-th batch images from I˜A and I˜B dataset;
17:
end for
18:
end for
19:
for epoch = 1 to q do
20:
for l = 1 to floor(n/m) do
21:
Update the parameter set of ω with fixed θ to train the MDGN network
22:
based on minimization the Eq. (3) and Eq. (9) with l-th batch images;
23:
end for
24:
end for
25:
Generate the multiple descriptions images IA and IB with the parameter set
26:
of ω by the MDGN network;
27: end for
28: for epoch = 1 to p do
29:
for i = 1 to floor(n/m) do
30:
Update the parameter set of α by training the MDRN network to minimize
31:
Eq. (9) with i-th batch images;
32:
end for
33: end for
34: return ω, α;

1) Learning algorithm-1: To back-propagate the error
from the MDRN network to MDGN network, we decompose
the learning problem of MDGN network, MDRN network
and MDVCN network once in Eq. (1) into three separate
subproblem learning, but they depends on each other closely.
Specifically, we first initialize all the parameter sets
mentioned previously, and multiple descriptions IA and IB
dataset by down-sampling for the training of MDRN
network and compress this dataset. Secondly, the parameter
set of α is updated by training MDRN network based on
minimization of the Eq. (9). Then, we generate multiple
descriptions reconstruction images I˜A , I˜B , and I˜ dataset
with the parameter set of α of MDRN network. This
reconstruction dataset I˜A , I˜B , and I˜ can be used to train
MDVCN network by updating the parameter set of θ based
on the minimization of the Eq. (10). Next, we update the
parameter set of ω with fixed θ to train MDGN network
according to the minimization of the Eq. (3) and Eq. (9).
After training MDGN network, the multiple descriptions
images IA and IB are generated with the parameter set ω of
MDGN network and then start the next iteration. The details
about learning algorithm-1 are summarized in the
Algorithm-1.
2) Learning algorithm-2: Different from our learning
algorithm-1, we separate the whole framework learning as
two
sub-problem
learning:
the
sub-problem
of
simultaneously learning MDGN network and MDRN
network, and the learning sub-problem of MDVCN network.
Concretely, the parameter sets of MDGN network and

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MDRN network: ω, α are learned by the optimization with
gradient descent method at the same time. After feeding
input data into MDGN network to produce multiple
descriptions IA and IB and compressing them with standard
codec, MDRN network are used to reconstruct these
compressed multiple descriptions IˆA and IˆB . Meanwhile,
the lossless multiple descriptions IA and IB are fed into
MDVCN network. This is feed-forward propagation of our
deep convolution neural networks, but the error from the
MDRN network is blocked by the codec. Here, we can
explicitly use the error from MDVCN network as the
approximate error from the codec. Thus, we can
simultaneously update MDGN network and MDRN network.
The whole process is detailed in the Algorithm-2.
Algorithm 2 Learning Multiple Description Neural Networks
Input: Ground truth image: I; the number of iteration: T ; the total number of images
for training: n; the batch size during training: m;
Output: The parameter sets of MDGN network and MDRN network: ω, α;
1: Initialize parameter sets: ω, α, φ, θ;
2: Pre-train MDVCN network;
3: for t = 1 to T do
4:
for epoch = 1 to l do
5:
for i = 1 to floor(n/m) do
6:
a) : Generate multiple descriptions IA and IB with the the parameter
7:
set of ω; Then, compress multiple descriptions IA and IB with standard
8:
codec with φ;
9:
b) : Update the parameter set of ω and α by training the MDGN network
10:
and MDRN network simultaneously by minimizing Eq. (3) and Eq. (9)
11:
with i-th batch images. Note that the MDGN network uses errors from
12:
MDVCN network for back-propagation to update parameter set of ω;
13:
c) : Generate multiple descriptions reconstruction images I˜A and I˜B
14:
with the parameter set of α by MDRN network;
15:
d) : Update the parameter set of θ by training MDVCN network based
16:
on minimization of Eq. (10) with i-th batch images with I˜A and I˜B ;
17:
end for
18:
end for
19: end for
20: return ω, α;

After comparing learning algorithm-1 with learning
algorithm-2, we can see that the training stability of the
second one relies on whether pre-trained MDVCN network
is well trained or not. Meanwhile, this network also has
great impacts on the learning of MDGN network, because
the bad accuracy of approximated error propagation from
MDVCN network will results in the insufficiency of multiple
description generation. On the contrary, the first algorithm is
more easily implemented in any neural network platform,
because there is no any changes in the process of neural
network’s optimization. Meanwhile, the performance of
learning algorithm-1 tends to be more stable than the second
one due to the reliable dependency among three neural
networks. It comes from a fact that the good training of
MDRN network will directly lead to the good training of
MDVCN network, and then MDVCN network will give a
supervision of the MDGN network. Conclusively, both of
them can resolve the learning problem of multiple
description neural networks, but the learning algorithm-1 is
more practical, so we use it to illustrate the efficiency of the
whole framework in the experimental sections.
III. E XPERIMENTAL RESULTS
We evaluate the proposed method against eight baselines
with state-of-the-art artifacts removal techniques [22–25] and

7

Fig. 2. The data-set is used for our testing

advanced super-resolution based on very deep convolutional
neural networks, such as [31, 32]. Note that there are 20
convolutional layers used for super-resolution in [31, 32].
Four baselines ”MDB1a-MDB4a” are formed with the
techniques of artifacts removal [22–25] and very deep
convolutional neural network based super-resolution [31].
Meanwhile, super-resolution of [32] are combined with
artifacts removal [22–25] to build four other baselines
”MDB1b-MDB4b”. Furthermore, in order to fully
demonstrate the efficiency of the proposed method, we form
a baseline model, which is denoted as ”Our-base”, when
replacing MDGN network to generate multiple descriptions
with the poly-phase down-sampling technique in [19]. For
simplicity, the proposed method is marked as ”Ours”.
Besides, the training of the proposed framework will be in
detail described next.
A. Training data and implementation details
Our whole framework is implemented in the platform of
TensorFlow [39] with Algorithm-1. The 400 images with
size 180x180 from [40] are used as our training data-set,
which are augmented by cropping, flipping, and rotating
image to build our training data set. There are the total
number 3200 of image patches with size of 160x160 used
for our framework’s training. Four images in Fig. 2 are used
to evaluate the efficiency of the proposed method for testing.
Our framework is trained with the Adam optimization
method [41]. The parameters for Adam optimization are set
to be β1 = 0.9, β2 = 0.999. The learning rate of training is
initially set as 0.0001, but the learning rate decays to be half
of the initial one when the training step reaches 3/5 of total
step. Once the training step reaches 4/5 of total step, it
reduces to be 1/4 of the initial one. The multiple descriptions
are compressed by standard JPEG codec with QF to be 2, 6,
10, 20, and 40 for the proposed framework during the
training and testing. The multiple descriptions for
MDB1a-MDB4a and MDB1b-MDB4b as well as ”Our-base”
are compressed with the QF set 2, 3, 4, 10, and 50.
B. Comparisons with several baselines
To validate the efficiency of the proposed framework, we
employ the Peak Signal to Noise Ratio (PSNR) and SSIM to
measure the objective quality. The multiple description
artifacts removal results with Fois [14], BM3D [22], DicTV
[17] and CONCOLOR [18] are got with strict usage of the
authors open codes according to the parameter settings in
their papers. Meanwhile, for image super-resolution in

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8

Fig. 3. The side reconstruction and central reconstruction objective measurement comparison on PSNR and SSIM for several state-of-the-art approaches.
(a1,b1) are respectively the side-reconstruction PSNR results of image (a) and (b) in Fig. 2, (a2,b2) are the central reconstruction PSNR results of image (a)
and (b) in Fig. 2, (a3,b3) are respectively the side reconstruction SSIM results of image (a) and (b) in Fig. 2, (a4,b4) are the central reconstruction SSIM
results of image (a) and (b) in Fig. 2

[31, 32], we use their official provided model to enlarge
these multiple description after artifacts removal so as to
guarantee the advances of eight baselines, when comparing
with the proposed method.

From the comparison in Fig. 3 and Fig. 4, it can be seen
that ours-base has better performance on SSIM for the side
reconstruction and cental reconstruction against eight baselines
MDB1a-MDB4a and MDB1b-MDB4b in the full range. In the

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9

Fig. 4. The side-reconstruction and central reconstruction objective measurement comparison on PSNR and SSIM for several state-of-the-art approaches.
(a1,b1) are respectively the side-reconstruction PSNR results of image (c) and (d) in Fig. 2, (a2,b2) are the central reconstruction PSNR results of image (c)
and (d) in Fig. 2, (a3,b3) are respectively the side-reconstruction SSIM results of image (c) and (d) in Fig. 2, (a4,b4) are the central reconstruction SSIM
results of image (c) and (d) in Fig. 2

most cases, the PSNR measurement of ours-base is better than
eight baselines MDB1a-MDB4a and MDB1b-MDB4b. Only
at the very low bit-rate, the PSNR of ours-base has slight
smaller than MDB4a and MDB4b, but our-base with higher
SSIM measurement has priority than MDB4a and MDB4b.

This comes from that the structural preservation of image is
more significant than detail preservation at the very low bitrate, as shown in Fig. 3 and Fig. 4.
Compared to the ours-base, the proposed method has more
PSNR and SSIM gains in the most cases, especially at the

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10

Fig. 5. The visual comparison of different methods for (a) in Fig. 2. (a1) input image (left) and the enlargement in the red line boxed region of input image (right),
(a2) multiple description created by a poly-phase down-sampling technique, (a3) multiple description generated by the proposed MDGN network, (a4) left image
is the difference between a pair of image in (a2) and the right image is difference between a pair of image in (a3), (a5) is the compressed image in (a2), (a6) is the
compressed image in (a3); (b-f) description reconstruction images, where the (b1-f1, b2-f2) and (b4-f4, b5-f5) are the side reconstruction images, the (b3-f3) and
(b6-f6) are the central reconstruction images; (b1-b3) MDB1a(24.574/0.714/0.292(s) and 27.849/0.778/0.583(c)), (b4-b6) MDB1b(24.637/0.715/0.292(s) and
27.849/0.778/0.583(c)), (c1-c3) MDB2a(25.202/0.725/0.292(s) and 28.088/0.769/0.583(c)), (c4-c6) MDB2b(25.318/0.727/0.292(s) and 28.215/0.771/0.583(c)),
(d1-d3) MDB3a(25.399/0.723/0.292(s) and 28.129/0.763/0.583(c)), (d4-d6) MDB3b(25.507/0.726/0.292(s) and 28.231/0.765/0.583(c)); (e1e3) MDB4a(25.785/0.748/0.292(s) and 25.969/0.754/0.583(c)), (e4-e6) MDB4a(25.847/0.749/0.292(s) and 26.051/0.756/0.583(c));(f1-f3) Oursbase(28.641/0.787/0.292(s) and 30.870/0.831/0.583(c)), (f4-f6) Ours(28.577/0.803/0.292(s) and 31.410/0.842/0.585(c)). (Note that the red line boxed
regions in (b-f) represent the part regions enlarged from the corresponding full resolution images like (a1); the real image size of (a2-a6) is half of input
image’s size, while all the other images have the same size as the input image)

high bit-rate. Because the proposed method in this paper
focuses on the appearance similarity but details difference
for multiple descriptions generation without apparent
structural distance loss to regularize the training at the very
low bit-rate, the proposed method has a litter lower PSNR
gains than ours-base in some cases. For the improvement of
the proposed method at low bit-rate, the first way is to
replace direct description distance loss with structural
distance loss during training. Another feasible way is to
employ 4x resolution reduction when generating the
descriptions with MDGN network and compressing
descriptions at the very low bit-rate, but larger QF is used
for the proposed method like our previous work [33].
Among these baselines, MDB4-a and MDB4-b defeat
against MDB1a-MDB3a and MDB1b-MDB3b on PSNR and
SSIM measurement, when comparing side description
reconstruction quality. But for the central reconstruction,
MDB4a and MDB4b can not compete with the
MDB1a-MDB3a and MDB1b-MDB3b. MDB3a and MDB3b
have the best PSNR performance of the central
reconstruction among the eight baselines. MDB1a-MDB3a
have very similar performance on central reconstruction.

Although the literature of [32] has reported that their
approach has greater PSNR gains than [31] for general
image super-resolution, the performance of [32] is slight
better than the one’s of [31], when these super-resolution
approaches are used for description’s resolution enhancement
after artifacts removal, which can be found in Fig. 3 and Fig.
4, when comparing MDB1a-MDB4a with MDB1b-MDB4b.
We have compared the visual quality of the proposed
method with different methods’ for multiple description
coding based on deep convolutional neural networks, which
is displayed in Fig. 5 and Fig. 6. In these figures,
MDB1a(24.574/0.714/0.292(s) and 27.849/0.778/0.583(c))
represents the measurements of PSNR/SSIM/bpp for side
reconstruction and central reconstruction based on the
approach of MDB1a. Similarly, other methods can be
denoted in this way. Our MDGN network-produced
descriptions, as displayed in Fig. 5-(a3) and Fig. 6-(a3),
maintain more important details than the ones generated with
the poly-phase down-sampling technique [19], even after
image compression. The differences between these pairs of
descriptions are exhibited in Fig. 5-(a4) and Fig. 6-(a4), from
which it can be observed that the proposed method tends to

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11

Fig. 6. The visual comparison of different methods for (d) in Fig. 2. (a1) input image (left) and the enlargement in the red line boxed region of input image
(right), (a2) multiple description created by a poly-phase down-sampling technique, (a3) multiple description generated by the proposed MDGN network, (a4)
left image is the difference between a pair of image in (a2) and the right image is difference between a pair of image in (a3), (a5) is the compressed image
in (a2), (a6) is the compressed image in (a3); (b-f) description reconstruction images, where the (b1-f1, b2-f2) and (b4-f4, b5-f5) are the side reconstruction
images, the (b3-f3) and (b6-f6) are the central reconstruction images; (b1-b3) MDB1a(27.808/0.824/0.232(s) and 31.319/0.861/0.463(c)), (b4-b6) MDB1b
(27.843/0.825/0.232(s) and 31.367/0.862/0.463(c)), (c1-c3) MDB2a(28.524/0.832/0.232(s) and 31.526/0.857/0.463(c)), (c4-c6) MDB2b(28.579/0.833/0.232(s)
and 31.601/0.858/0.463(c)), (d1-d3) MDB3a(28.642/0.827/0.232(s) and 31.288/0.850/0.463(c)), (d4-d6) MDB3b(28.700/0.828/0.232(s) and
31.352/0.851/0.463(c)); (e1-e3) MDB4a(29.244/0.846/0.232(s) and 29.428/0.850/0.463(c)), (e4-e6) MDB4a(29.270/0.846/0.232(s) and 29.471/0.850/0.463(c));
(f1-f3) Ours-base(32.040/0.865/0.232(s) and 33.098/0.881/0.463(c)), (f4-f6) Ours(31.913/0.874/0.229(s) and 33.865/0.889/0.458(c))). (Note that the red line
boxed regions in (b-f) represent the part regions enlarged from the corresponding full resolution images like (a1); the real image size of (a2-a6) is half of
input image’s size, while all the other images have the same size as the input image)

keep the description distance on the details and has less
structural difference preservation. Furthermore, the
descriptions from our MDGN network tend to highlight
obvious feature pixels for all the descriptions in order to
protect the key features. Therefore, the protected feature of
lossy descriptions always can be kept, although they are
possibly badly smoothed and contaminated by compression,
as shown in Fig. 5-(a5-a6) and Fig. 6-(a5-a6).
The side reconstruction images and cental reconstruction
images have been displayed in Fig. 5-(b-f) and Fig. 6-(b-f).
From these figures, it can be clearly seen that the side
reconstruction images and cental reconstruction images with
the proposed method look more natural and have more detail
preservation than eight baselines MDB1a-MDB4a,
MDB1b-MDB4b, and our-base. Our-base has better
performance than the eight baselines. Among these baselines,
both MDB4a and MDB4b keep more details than
MDB1a-MDB3a, MDB1b-MDB3b, which can be seen in
Fig. 5-(b-e) and Fig. 6-(b-e). From the above objective and
visual comparisons, it can be concluded that it’s very
important to emphasize on significant context features when
automatically
generating
appearance-similar
but

details-different descriptions with convolutional neural
networks, as compared to the poly-phase down-sampling
technique. Meanwhile, the better descriptions always benefit
the better side and central description reconstruction.
IV. C ONCLUSION
In this paper, we introduce multiple description image
coding based on deep convolutional neural networks. First,
multiple description network is employed to automatically
yield valid multiple descriptions. Then, these multiple
descriptions are compressed by standard codec so that our
whole framework is compatible with standard codec. Thirdly,
we use multiple description reconstruction network to
enhance these descriptions and restore them to be full
resolution for the reconstruction of the compressed multiple
descriptions. Besides, two learning algorithms are provided
to train our whole framework. Moreover, both distance loss
and SSIM loss are combined together to train the multiple
description generator networks in order to make sure that the
generated multiple descriptions are diverse, but they have
shared structures information.

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