Traffic Reduction in Video Call and Chat using
DNN-based Image Reconstruction
Shota Watanabe∗ , Takuya Fujihashi† , Shunsuke Saruwatari∗ and Takashi Watanabe∗∗ Graduate School of Information Science and Technology, Osaka University, Japan† Graduate School of Science and Engineering, Ehime University, Japan

Abstract—In this paper, a traffic reduction scheme for videobased call and chat applications that uses deep neural network(DNN) based super resolution is proposed. Specifically, a sendertransmits low-quality, low-resolution video frames containing face
information in order to reduce the amount of video traffic. The
receiver uses DNN-based super resolution to reconstruct highquality, high-resolution video frames from the low-quality video
frames. The proposed scheme makes two contributions. First,
face features are adopted for parameter optimization of DNNbased super resolution for high-quality image reconstruction.
Second, the scheme includes a newly designed loss function thatconsiders face features that allows high-quality face-containing
video frames to be reconstructed at the receiver. According toour evaluation results using real video frames of video calls,
the proposed scheme reduces the amount of video traffic bymore than 90% as compared with the conventional schemes that
implement the standard video encoder. In this case, the proposed
scheme achieves a reconstructed image quality up to 0.85 in termsof structural similarity (SSIM).
Index Terms—Deep Neural Network, Video Call, Video Chat,
Super-Resolution

I. I NTRODUCTION
As the number of mobile users increases, the number ofusers of social networking service (SNSs), e.g., Facebook
and Instagram, communicating with on-line/off-line friends isalso increasing. In such services, the user frequently captures
his/her face using a mobile device’s camera and shares thecaptured video with users so that they can enjoy video calls
and chat with friends [1]. Face-containing video contents arealso applied in many applications for teleconference and customer support to allow smooth communications with remoteusers [2].
A major issue pertaining to applications that use facecontaining video contents is the large amount of video traffic that they generate as compared with audio- and textbased applications. A large amount of video traffic causes
low video quality in band-limited links/networks. To reduce
video traffic, video compression techniques, such as Advanced
Video Coding (H.264/AVC) and High Efficiency Video Coding (H.265/HEVC), have been proposed for general video
applications [3], [4]. These techniques use motion estimation,
quantization, and entropy coding to attain better compression
gains; however, the problems caused by the large amounts ofvideo traffic in video-based call and chat applications remain.
To achieve significant traffic reduction in such applications,an application-specific method is required that considers thevideo features in video call and chat applications: many video

frames captured in the applications may contain the sender’sface.
For this purpose, a novel transmission scheme is proposed for video call and chat applications, motivated bydeep neural network (DNN)-based super resolution, i.e., deep
convolutional generative adversarial networks (DCGANs) [5].
DCGANs reconstruct high-quality and high-resolution facecontaining video frames from low-quality and low-resolution
face-containing video frames by using a pre-trained generation
model. The key idea of our scheme is that a sender transmits
only low resolution and quality face-containing video frames
over networks and the receiver uses a DCGAN to reconstructhigh-resolution, high-quality face-containing video frames.
Since the quality of the reconstructed video frames depends on
the parameters of the generation model, the parameters must
be optimized using the sender’s face-containing video frames
and a loss function before video transmissions. The optimized
parameters are then shared with the receiver. The evaluation
results show that our scheme achieves an approximately 90%
traffic reduction as compared with the existing scheme, whichuses the conventional video compression. In this case, theproposed scheme provides a clean face image after the image
reconstruction with a structural similarity (SSIM) of 0.85.
The contributions of the proposed scheme are three fold: 1)
it uses the face features in addition to the face-containing video
frames for parameter optimization of the generation model toachieve high-quality reconstruction, 2) it includes a new loss
function designed such that the face features are considered
in order to enhance the image reconstruction quality at thereceiver, and 3) it both realizes significant traffic reduction andmaintains visual quality for applications using face-containing
video contents, such as video call and chat.
II. R ELATED R ESEARCH
Our study is related to studies on super resolution-based
image reconstruction, DNN/generative adversarial network(GAN) based image reconstruction, and DNN-based video
delivery.
A. Super Resolution-based Image Reconstruction
In super resolution techniques, low-resolution images arerestored to high-resolution images. There are two types ofsuper resolution techniques: single-image and multiple-image.
For example, A+ [6] and RAISR [7] have been proposed forsingle-image super resolution, while [8], [9] proposed superresolution for multiple images. In addition, the scheme in [10],[11] realizes high-quality super resolution for multiple imagesby using recurrent convolutional networks.
In our study, we used single-image-oriented super resolution
for face-containing video frame reconstruction in video calland chat applications. For high-quality image reconstruction,
the proposed scheme considers DNN- and GAN-based superresolution at the receiver. The super resolution uses generationand discriminant models to reconstruct a high-quality image
from the corresponding noisy image to realize low-traffic video
delivery. In this case, the parameters of both models are trainedusing the face features in addition to the face-containing video
frames to allow high-quality reconstruction.
B. Deep Neural Networks/Generative Adversarial Networkbased Image Reconstruction
In recent years, some studies were aimed at DNN/GANbased image reconstruction, such as super resolution using
convolutional neural networks (SRCNN) [12], DCGAN [5],Laplacian pyramid of GAN (LPGAN), and super resolution
using a GAN (SRGAN) [13] to further improve the reconstruction quality. Specifically, DCGAN combines a CNN witha GAN to generate a high-quality image from a noisy image.
LPGAN and SRGAN extend DCGAN for super resolution.
For example, LPGAN stacks multiple layers of DCGAN togradually improve the reconstructed image quality. The study
most relevant to our proposed scheme is that on srez [14].
In this study, the authors used DCGAN to reconstruct highresolution face-containing images from low-resolution images.
The super resolution technique in the proposed scheme isbased on srez. We use super resolution to reconstruct facecontaining images to achieve traffic reduction for video calland chat applications. To further improve the reconstruction
quality, we focused on the face features and designed a newloss function for super resolution that considers them.

III. P ROPOSED S CHEME
In this paper, a transmission scheme for face-containing
video contents is proposed that realizes a significant traffic
reduction for video-based call and chat applications. Fig. 1
shows an overview of the proposed scheme. The scheme firstextracts the face features from the captured face-containing
video frames using the face detector in dlib. The captured
video frames are then converted to low resolution and encoded by the H.264/AVC encoder prior to transmission. Afterthe encoding, the sender transmits the low-resolution video
frames and the corresponding face features to the receiver.
The receiver first uses the H.264/AVC decoder to decode thereceived video frames. Since the resolution of the decoded
video frames is lower than that of the original video frames,
they are then reconstructed to the original resolution by using
DCGAN and the face features.
A. Generation and Discriminant Model
DCGAN uses both generation and discriminant models forimage reconstruction. For high-quality image reconstruction,
the optimized parameters of the generation and discriminantmodels are determined by using numerous face-containing
video frames in the training phase.
We consider an original video frame containing the sender’sface Iorg with a resolution of W × H × C pixels and the lowresolution video frame Idown with a resolution of rW ×rH×C
pixels. Here, W and H represent the width and height of theoriginal video frame, respectively, and C is the number ofcolor channels. In addition, r is a downscaling factor. The
generation model G(= GθG (Idown )) is one that reconstructs avideo frame Irec with a resolution of W × H × C pixels fromthe low-resolution video frame Idown using the parameters of
the generation model θG . In this case, the parameters need
to be optimized to improve the reconstruction video quality
using the generation model. This optimization is expressedby the following equation using N original video frames

C. Deep Neural Network-based Video Delivery
In a few studies, DNNs were applied to video delivery overthe Internet [15], [16]. The main objective of these studies
was to obtain better streaming quality by using DNN-based
predictions.
In [15], a rate control method for real-time video streaming
using DNN-based future bit-rates and delay predictions was
proposed. In [16], the authors presented a video delivery
framework to improve video quality in order to enhance users’
quality of experience (QoE) when viewing video-on-demandcontents, such as films and TV shows.
The approach most closely related to our study was includedin [16], that is, the utilization of DNNs for video quality
improvement. This approach is focused on the overall system
structure and partially uses the conventional DNN for videoon-demand streaming. However, in our approach a new DNN
is designed that includes a loss function that uses face features,for real-time video call and chat applications.

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Fig. 3. Discriminant model of the proposed scheme.

(i)

Iorg , i = 1, ..., N and the same number of low-resolution(i)images Idown , i = 1, ..., N :
(
)
)
1 ∑ (
(i)
(i)
l GθG Idown , Iorg
,
θG N
n=1
N

θ̂G = arg min

(1)

(
(i)
(i) )
where l GθG (Idown ), Iorg represents the loss function of
the generation model with the inputs of the original andreconstructed video frames for parameter optimization of thegeneration model. The details of the loss function implemented
in our scheme are described in Section III-B.
The discriminant model D is a binary classifier and used
for the parameter optimization of the generation model G.
The discriminant model attempts to distinguish between thereconstructed (false) data and the true data of the dataset.
To improve the reconstruction accuracy, the proposed scheme
solves the following minimax optimization by using bothgeneration and discriminant models based on the method
in [17]:[ ()]
min max EIorg ∼Pdata (I) log DθD (Iorg )
θG θD
[ (
(
))] (2)+EIdown ∼Pmosaic (I) log 1 − DθD GθG (Idown ),where DθD (Iorg ) is the probability that the original video
frame Iorg belongs to the true dataset, 1 − DθD (GθG (Idown ))is the probability that the reconstructed video frame generated
by the generation model G belongs to the false dataset, andE[·] is the expected value. Since the discriminant model D
needs to correctly distinguish between the original and thereconstructed video frame, the parameters of the discriminantmodel θD should be optimized to maximize both probabilities.
However, in view of the generation model, the reconstructed
image should be classified into the correct dataset. This meansthat the generation model should find the optimized parametersthat minimize the probability of 1 − DθD (GθG (Idown )).
Figs. 2 and 3 show our generation model and discriminantmodel, respectively, based on the srez scheme [14]. In thegeneration model, the proposed scheme uses the convolution
layer at the first layer. The convolution extracts the independent features of each image using certain convolution kernels.
Note that the image features are shown in the weights between
the layers. In the second layer, we use a rectified linear unit
(ReLU) layer. Before the third layer, the proposed scheme adds
the second layer output and the low-resolution frame generated
by bilinear interpolation. This is the third layer input. At
both the third and fourth layers, the proposed scheme uses
convolution layers. In the fifth to seventh layers, the proposed
scheme uses batch normalization, ReLU, and convolutional
layers, respectively. The batch normalization standardizes the
input values to balance the values in order to reduce the effectof outliers. The proposed scheme repeats these 3 layers one
time to construct up to the 10-th layer, and then adds the 10-thlayer output and 4-th layer output. At the 11-th to 13-th layers,
we use batch normalization, ReLU, and the transconvolution
layer, respectively, and then repeat the 4-th to 13-th layers
one time to obtain the 14-th to 23-rd layers. Furthermore, theproposed scheme uses the convolution and the ReLU layers at
the 24-th and 25-th layers and repeats the same layers for the
26-th and 27-th layers, and uses the convolution layer in the
28-th layer. Finally, the sigmoid layer is allocated to the final
layer.
In the discriminant model, the first layer is convolution,
the second layer is batch normalization, and the third layer is
ReLU. The proposed scheme repeats these 3 layers up to the
18-th layer. In the 19-th and 20-th layers, the model uses the
convolution and finally average operations.

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(i)

B. Loss Function
In our scheme, the loss function consists of three factors:
generation loss, pixel-domain loss, and face feature loss.
Specifically, the loss function is expressed as
)
)
(
(
(i)
(i)
(i)
(i)
(i)
= α1 · ladversarial + α2 · lpixel + α3 · lface
l GθG Idown , Iorg
(3)
Here, α1 , α2 , α3 are weights of each factor, the value of which(i)is from 0 to 1. ladversarial represents the generation loss:
(
(
(
)))
(i)
(i)
ladversarial = − log DθD GθG Idown(4)
The generation loss increases as the probability that thereconstructed video frame generated by the generation model(i)
G belongs to the false dataset increases. lpixel represents the
pixel-domain loss:(i)

lpixel
=

rW ∑
rH
(
(
))
∑
1
(i)
(i)
,
I
(x,
y)
−
R
G
I
(x,
y)
θ
G
down
r2 W H x=1 y=1 down

(5)
(i)

where R(·) represents a resize function and Idown (x, y) represents the (x, y)-th pixel value of the i-th low-resolution
video frame. This loss factor represents the average difference
between the pixel values of the low-resolution original and thereconstructed video frames.

lface represents the difference between the face features in
the original and reconstructed video frames:(i)lface =68(())
1 ∑ ( (i) )(i)Ψ Iorg − Ψ GθG Idown
68
2

(6)

k=1

where Ψ(·) is an extraction function of face features fromface-containing video frames. To extract the face features fromthe face-containing video frames, the proposed scheme uses
histogram of oriented gradient (HOG) features [18] and dlib
detection. Here, we used the existing dataset http://dlib.net/
files/shape predictor 68 face landmarks.dat.bz2 published by
iBUG 300-W to obtain the HOG features in each video frame.
The dlib detection algorithm extracts 68 face features, including jaw, eyes, eyebrows, nose, and mouth, from each facecontaining video frame based on the HOG features. Note that(i)the proposed scheme calculates lface only when the sender’sface is detected by dlib in the i-th face-containing video frame.
If dlib does not detect the sender’s face in the video frames,
the proposed scheme uses the following loss function insteadof Eq. (3):
(
(
)
)
(i)
(i)
(i)
(i)
l GθG Idown , Iorg
= (1 − α) · ladversarial + α · lpixel . (7)Here, α is a parameter that takes a real value between 0 and
1.
C. Parameter Sharing of Generation Model
The proposed scheme finally shares the parameters of thegeneration model between the transmitter and the receiverbefore video transmissions. It considers two methods forparameter sharing: sequential sharing and pre-sharing. The
sequential sharing method periodically updates the parametersof the generation model based on the new sender’s facecontaining video frames while the parameter optimization
overhead becomes larger. The pre-sharing method always
sends the pre-trained parameters to the receiver to decrease
the overhead. The reconstruction quality is degraded if thecaptured video frames contain objects and scenes that differ
from those of the training phase.
1) Sequential Sharing: The sequential sharing method consists of two phases: learning and reconstruction. Fig. 4 shows
an overview of the learning phase. In this phase, the receiver
simultaneously displays the high-resolution video frames andtrains the parameters of the generation model using theseframes. Specifically, a sender first encodes the high-resolution
video frames using the standard video encoder H.264/AVC
and transmits the encoded video frames to the receiver. The
receiver first obtains the high-resolution frames by decoding
the received video frames and displays the decoded video
frames. At the same time, the receiver resizes the decoded
video frames into low-resolution frames and extracts the facefeatures from the high-resolution video frames. The receiverthen trains the parameters of the generation model using
both the high- and low-resolution video frames and the facefeatures to reconstruct the high-resolution video frames fromthe low-resolution video frames and the corresponding facefeatures. When the training in the learning phase has been
completed, the receiver sends a signal to the transmitter thatthe reconstruction phase should be initiated by the transmitterand the receiver.
Fig. 5 shows an overview of the reconstruction phase.
The transmitter resizes the high-resolution video frames into
low-resolution frames and extracts the face features from thehigh-resolution video frames. The low-resolution video frames
are encoded by H.264/AVC, and then, the encoded video
frames and the corresponding face features are transmitted to
the receiver. The receiver reconstructs high-resolution video
frames by using the generation model obtained in the learning
phase. In this case, the receiver uses the low-resolution facecontaining video frames and the corresponding face features
as inputs of the generation model. Finally, the reconstructed
video frames are displayed on the receiving device.
2) Pre-Sharing Method: The pre-sharing method sends the
pre-trained parameters of the generation model before initializing video-based call and chat applications. After sharing the
pre-trained parameters with the receiver, the transmitter sends
low-resolution face-containing video frames and the extracted
face features. Finally, the receiver reconstructs high-resolution
video frames using the generation model and the face features;the operations are the same as in the reconstruction phase in
the sequential sharing method.

IV. P ERFORMANCE E VALUATION
A. Evaluation Settings
In the performance evaluation of the proposed scheme forvideo-based call and chat applications, we used real video
sequences of video call applications. The video frames were
captured by a PENTAX KS-2. The frame rate was 30 frames
per second (fps) and the duration of the video sequence was
10 m. Each video frame was compressed by using Joint
Photographic Experts Group (JPEG). The compressed video
frames were resized to two resolutions, i.e., 80×80 pixels and160 × 160 pixels, and regarded as the input high-resolution
video frames. We prepared 18,000 input video frames fortraining and testing. The batch size of both training and testingwas 16.
The parameters of α, α1 , α2 , and α3 were 0.90, 0.90,
0.09, and 0.40, respectively. The initial value of the learning
coefficient was 0.0002. In addition, the initial values of theweight and bias were obtained from the normal distribution
and 0, respectively. In our scheme, the parameter optimization
algorithm and the initial values were based on those of
Adam [19].
B. Effect of Batch Counts
We first discuss the visual quality of the reconstructed video
frames as a function of batch counts. Figs. 6(a)–(c) show thevisual quality of the reconstructed video frames for different
numbers of batch counts. In Fig. 6(a), the reconstruction
algorithm is based on srez [14]. Figs. 6(b) and (c) show thevisual quality of the results of the proposed scheme using bothloss functions, Eqs. (3) and (7). Figs. 6(c) and (b) respectively
show the results when the proposed scheme does and does
not use the face features for image reconstruction. From left toright, each image is the mosaic image, the reconstructed image
using bicubic interpolation, the reconstructed image after 100,
200, 300, 400, 500, 800, 1,000, 2,000, 3,000, 4,000, 5,000,
8,000, and 10,000 batch counts, and the original image. To
create the mosaic image, we resize the input image to 20 × 20
pixels and adopt nearest neighbor interpolation to reconstruct
the image. Here, two images are selected from the testing
images to compare the visual quality.
The evaluation results show that the proposed scheme
reconstructs a genuine face in a smaller number of batchcounts as compared with the existing srez scheme. This means
the proposed loss function reconstructs high-resolution images
with a low overhead requirement. In addition, we can see thatthe reconstructed images in the proposed scheme that includes
the face features show a clear face at a smaller batch countas compared with those in the proposed scheme that does notinclude the face features. This means that the face features
lead to a better reconstruction quality even in a small numberof batch counts.
C. Data Size Reduction
This section evaluates the data size of the proposed scheme
to demonstrate the effect on traffic reduction. Fig. 7 shows the
JPEG-coded (intra-coded), low-resolution, and reconstructed

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images and their data size at a batch count of 1,250. Here, theresolution of the upper and lower images is 80 × 80 pixels and160 × 160 pixels, respectively.
It is demonstrated that the data size of the intra-coded
and low-resolution images is 13,250 Bytes and 1,042 Bytes,
respectively, at a resolution of 80 × 80 pixels, while at aresolution of 160 × 160 pixels the data size is 46,340 Bytes
and 3,230 Bytes, respectively. Thus, it is demonstrated that the
proposed scheme reduces the amount of video traffic by morethan 90% as compared with the intra coding in H.264/AVC,
irrespective of image resolutions.
D. Image Quality
In this section, we evaluate the quality of the reconstructed
image in each reference scheme in terms of the weighted peaksignal-to-noise ratio (WPSNR) and SSIM [20]. The WPSNR is
obtained by performing a weighted average of peak signal-tonoise ratios (PSNR) in the YCbCr color components, since the
Y component contains considerable information as comparedwith the other color components. Here, the weight in eachcolor component is Y : Cb : Cr = 8 : 1 : 1 [21]. SSIM
predicts the perceived video streaming quality. Larger valuesof SSIM close to 1 indicate a higher perceptual similarity
between the original and reconstructed images. In contrast to
WPSNR, SSIM considers the correlation between each pixel
and its surroundings based on the luminance, contrast, andstructure. It has been confirmed that SSIM reflects humanvisual characteristics as compared with PSNR [22].
Fig. 8 shows the quality of the mosaic image, a reconstructed image using bicubic interpolation, srez, and the proposed scheme in terms of WPSNR. The evaluation results
show that the performance of srez and the proposed scheme
are almost the same, even at a large number of batch counts.
Since WPSNR considers pixel-wise distortion and suffers fromlow quality even in a 1-bit pixel shift, its use as an image
reconstruction metric is not suitable.
Fig. 9 shows the quality of the mosaic image, the reconstructed image using bicubic interpolation, srez, and the
proposed scheme in terms of SSIM. As seen in this figure,the proposed scheme with face features achieves the highest
video quality. This means the proposed scheme should use
face features to improve the reconstructed image quality. In

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addition, the reconstruction quality of the proposed schemewithout face features is almost the same as that of srez. It
is noted that the proposed scheme obtains a clear face witha small number of batch counts as compared with srez, as
shown in Fig. 6.
V. C ONCLUSION
In this paper, a traffic reduction scheme for video call andchat applications that uses super resolution based on DNNs
was proposed. Specifically, the proposed scheme uses facefeatures for loss functions in the parameter optimization andhigh-quality image reconstruction. According to the evaluation
results, the proposed scheme significantly reduces the amountof video traffic as compared with the conventional video
encoding and simultaneously maintains better reconstruction
quality.
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