Dynamic texture map based artifact reduction for compressed videos

n Figure 11
and Figure 12. As can be seen in these results, Chen
method result (Figure 12(b)) is blurry; the Liu method
result (Figure 12(c)) still has many ringing artifacts,
the MCSTF method introduces good result but many
details are lost (Figure 12(d)); the CNN method re-
sult (Figure 12(e)) and the proposed method result
(Figure 12(f)) improve quality better than the other
methods. However, according to Figure 13, the flicker
metric of the CNN method is not improved. Similarly,
the results of the 10th frame of the Foreman video
(a) (b)
(c) (d)
(e) (f)
Figure 12. The zoomed 10th frame of Mobile video sequence. (a)
Compressed; (b) Chen; (c) Liu; (d) MCSTF; (e) CNN; (f) The proposed
method.
Figure 13. Flicker metric comparision in Mobile video sequence.
sequence are shown on Figure 14 and Figure 15, where
the proposed method, the MCSTF method, and the
CNN method introduce the best qualities. However, the
result of the CNN method is color bleeding, the result
of the MCSTF method is lost many details.
In the MJPEG encoded video sequcence enhance-
ment, based on the above results, the proposed method
outperforms the other methods in term of PSNRs,
SSIMs (except the CNN method), the flicker met-
rics, and the visual quality. The SSIM value of the
CNN method and the proposed method are equavalent
each other.
52 REV Journal on Electronics and Communications, Vol. 9, No. 3–4, July–December, 2019
(a) (b)
(c) (d)
(e) (f)
Figure 14. The 10th frame of Foreman video sequence. (a) Com-
pressed; (b) Chen; (c) Liu; (d) MCSTF; (e) CNN; (f) The proposed
method.
Table IV
PSNR Comparison of the Proposed Method for H.265 Video
Sequences
Sequences H.265 Chen Liu MCSTF CNN Proposed
Highway 31.6271 30.9281 31.3742 31.8720 30.8110 31.8736
Mother 32.5383 32.6104 32.4123 32.4226 32.7943 32.7198
Foreman 30.0859 30.7423 30.4011 31.2270 29.2476 31.4596
Mobile 26.0222 23.1958 25.2848 24.9134 25.6546 25.8499
Bridge-far 31.5543 31.3683 31.4304 31.6530 29.4862 31.7308
Bridge-
close
27.5614 27.2508 27.4861 27.2326 24.4270 27.6360
Hall 29.8270 29.1281 29.3884 29.1417 30.0891 29.7552
Beauty 33.3822 33.4866 33.4464 33.5123 33.5496 33.5153
Bosphorus 36.2591 36.1707 35.6105 35.4355 36.0618 36.0361
Honey 32.8902 33.1216 33.0254 33.2205 33.1163 33.3114
ReadyStea-
dyGo
30.8218 30.8305 30.5702 30.5857 31.2092 30.9901
YachtRide 32.0129 32.1024 31.8633 31.9799 32.3608 32.1844
Averaged difference -0.3039 -0.1908 -0.1155 -0.4812 0.2067
6.2 Enhancement for H.265 Encoded Video
Sequences
H.265 standard is the lastest video encoding stan-
dard. In this subsection, the original frames are com-
pressed using this standard. The authors simulate dif-
ferent methods to enhance the H.265 video sequences.
The configuration parameters encoding the H.265 stan-
(a) (b)
(c) (d)
(e) (f)
Figure 15. The zoomed 10th frame of Foreman video sequence. (a)
Compressed; (b) Chen; (c) Liu; (d) MCSTF; (e) CNN; (f) The proposed
method.
dard are as follows: the prediction structure is IPP-
PIPPP, QP (Quality Parameter) is 38, and deblocking
and deringing filters are turn off. The objective simu-
lation results of the Chen method, the Liu method, the
MCSTF method, the CNN method, and the proposed
method are shown in Table IV, Table V, and Table VI,
respectively. The averaged PSNR improvement values
of the Chen method, the Liu method, the MCSTF
method, the CNN method, and the proposed method
are -0.3039 dB, -0.1908 dB, -0.1155, -0.4812 dB, and
+0.2067 dB, respectively. In comparison of the PSNR
value of the H.265 encoded video sequences, only
the proposed method provides improvement while the
other methods do not. The averaged SSIM improvement
values of the Chen method, the Liu method, the MCSTF
method, and the proposed method are -0.0001, -0.0027,
T. V. Nguyen et al.: Dynamic Texture Map Based Artifact Reduction for Compressed Videos 53
Table V
SSIM Comparison of the Proposed Method for H.265 Video
Sequences
Sequences H.265 Chen Liu MCSTF CNN Proposed
Highway 0.8064 0.8085 0.8039 0.8237 0.8185 0.8233
Mother 0.8357 0.8379 0.8323 0.8285 0.8427 0.8394
Foreman 0.7741 0.8431 0.7809 0.8462 0.8577 0.8579
Mobile 0.8943 0.8097 0.8842 0.8426 0.8821 0.8842
Bridge-far 0.7522 0.7543 0.7544 0.7591 0.7577 0.7591
Bridge-
close
0.6943 0.6791 0.6860 0.6531 0.6861 0.6848
Hall 0.8451 0.8412 0.8423 0.8428 0.8624 0.8520
Beauty 0.7517 0.7574 0.7545 0.7571 0.7605 0.7571
Bosphorus 0.8975 0.8953 0.8802 0.8607 0.8816 0.8847
Honey 0.8716 0.8836 0.8786 0.8885 0.8894 0.8872
ReadyStea-
dyGo
0.8575 0.8617 0.8518 0.8496 0.8690 0.8629
YachtRide 0.8254 0.8332 0.8244 0.8300 0.8404 0.8353
Averaged difference -0.0001 -0.0027 -0.0020 0.0119 0.0102
-0.0020, +0.0119, and +0.0102, respectively. In compar-
ison of the SSIM value of the H.265 encoded video
sequences, the proposed method is slightly higher and
is in similar level with the CNN method while the
other methods are lightly lower. The averaged flicker
metric improvement values of the Chen method, the
Liu method, the MCSTF method, and the proposed
method are -0.0758, +0.0253, -0.1621, -0.0225, and -
0.2142, respectively. In comparison of the Chen method,
the Liu method, the MCSTF method, and the CNN
method, the averaged PSNR improvement value of the
proposed method increases +0.5155, +0.3974, +0.3222,
and +0.6879, respectively. Similarly, the averaged SSIM
improvement value of the proposed method increases
+0.0102, +0.0129, +0.0122, and -0.0017. The averaged
flicker metric improvement value of the proposed
method decreases -0.1385, -0.2396, -0.0521, and -0.1918.
The H.265 standard is benefical from both intraframe
and interframe encoding structure to enhance flicker
metric and optimise PSNR value. So, in H.265 video
sequences, PSNRs of the proposed method improve less
than in MJPEG video sequences although PSNRs of the
proposed method still are the best among compared
methods. Flicker metric of the proposed method also
improves better than that of the other methods. The av-
eraged SSIM improvement value of the propose method
is equivalent to that of the CNN method and higher
than the other methods.
7 Conclusions
Lossy compression introduces annoying artifacts to vi-
sual human. This paper proposes a novel method to en-
hance the compressed video sequences. With the com-
pressed video sequence input, the authors implement
advanced methods to construct the dynamic texture
map, the flicker artifact map and the mosquito artifact
map. These maps are used to control the fuzzy filter’s
strength to remove artifacts while significantly preserv-
Table VI
Flicker Comparison of the Proposed Method for H.265 Video
Sequences
Sequences H.265 Chen Liu MCSTF CNN Proposed
Highway 3.2593 3.1835 3.2360 3.1025 3.6555 3.0008
Mother 0.2731 0.2370 0.2731 0.3217 0.2888 0.3310
Foreman 2.4169 1.5799 2.3516 1.3126 1.9443 1.1598
Mobile 1.3999 1.2856 1.3952 1.0346 1.2024 0.9828
Bridge-far 0.8340 0.8606 0.8546 0.7991 1.1422 0.7954
Bridge-
close
1.7015 2.0159 2.0716 1.9880 2.0140 1.7613
Hall 2.9285 3.1165 3.2249 3.0004 2.8316 2.8899
Beauty 0.8486 0.8126 0.8223 0.7271 0.7928 0.7418
Bosphorus 0.2127 0.1951 0.2025 0.1554 0.2005 0.1472
Honey 0.4196 0.3368 0.3380 0.3061 0.3133 0.3070
ReadyStea-
dyGo
1.4680 1.2576 1.3310 1.1425 1.1787 1.1530
YachtRide 0.3846 0.3564 0.3500 0.3115 0.3130 0.3061
Averaged difference -0.0758 0.0253 -0.1621 -0.0225 -0.2142
ing more details of the compressed video sequences.
The simulation results show that the proposed method
improves effectively in terms of PSNR, SSIM, flicker
metric and visual quality in comparision with other
state of the art methods.
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Thai Van Nguyen graduates in telecommuni-
cation electronics at the Can Tho University
in 2003 and receives M.S degree in electron-
ics engineering from the Ho Chi Minh City
University of Technology (HCMUT), HCMC,
Vietnam, in 2009. He is currently pursuing the
Ph.D. degree at HCMUT. He is working at
MobiFone Corporation, Vietnam. His research
interests are image and video quality enhance-
ment, MIMO.
Tuan Do-Hong received the B.S. and M. Eng.
degrees in electrical engineering from the Ho
Chi Minh City University of Technology, Viet-
nam National University Ho Chi Minh city,
Vietnam, in 1994 and 1997, respectively, the
M.Sc. and Ph.D. degrees in communication
engineering from the Munich University of
Technology, Germany, in 2000 and 2004, re-
spectively. He has been is Dean of Faculty of
Electrical and Electronics Engineering, the Ho
Chi Minh City University of Technology, Viet-
nam National University Ho Chi Minh City, Vietnam. His research
interests include stochastic signal processing and applications for
image and video processing.
Dung Trung Vo (S’06 - M’09) received the
B.S. and M.S. degrees from HCMUT, Vietnam,
in 2002 and 2004, respectively, and the Ph.D.
degree from the University of California at
San Diego, La Jolla, in 2009. He has been
a Fellow of the Vietnam Education Founda-
tion (VEF) and is with HCMUT since 2002.
He interned at Mitsubishi Electric Research
Laboratories (MERL), Cambridge, MA, and
Thomson Corporate Research, Princeton, NJ,
in the summers of 2007 and 2008, respectively.
He has been a staff 2 research engineer at the Digital Media Solutions
Lab, Samsung Research America, Irvine, CA, since 2009. He receives
the Special Merit Awards for Outstanding Paper at IEEE Conference
on Consumer Electronics (ICCE) 2011 and 2012. His research interests
are algorithms and applications for image and video coding and post-
processing.