Open Journal of Applied Sciences, 2013, 3, 36-40
Published Online March 2013 (
Copyright © 2013 SciRes. OJAppS
Aerial Video Encoding Optimization Based On x264
Fan Yang, Hongbing Ma
Department of Electronic Engineering, Tsinghua University, Beijing, China
Received 2012
x264 video codec uses lots of new video encoding technology based on H.264/AVC video encoding standard which
enhances compression efficiency. However this results in so heavy computation that the x264 codec is not fit for
real-time encoding application of high resolution video. This paper analyses the character of aerial video and then opti-
mizes the inter-frame mode decision and motion estimation in x264 codec according to its character by reducing a lot of
unnecessary computation. In the result, about 19% computation and encoding time is reduced with total bits and PSNR
decreasing lightly.
Keywords: x264; Aerial Video Encoding; Mode Decision; Motion Estimation
1. Introduction
Aerial video real-time encoding and transmission is very
important in application. With the definition of aerial
video increasing to about 1080P, the big data brings the
challenge for real-time encoding. Therefore, some scho-
lars do lots of analysis and research about aerial video
encoding which focus on two aspects mainly. (1) Ac-
cording to H.264/AVC encoding standard[1] and the
character of aerial video, they use the global motion vec-
tor estimated by the speed of aircraft to substitute local
motion vector to save the encoding time[2]. (2) By iden-
tifying the new area and object motion, they integrate the
last frame into a new one[3]. This is a new encoding me-
thod which is independent of the traditional encoding
standard. The two methods both decrease the complexity
and both have locality. The first one is only used for the
aerial video which is taken when the aircraft is parallel
with the horizon. Otherwise, the method will lead to very
low compression efficiency. The second method’s chal-
lenge is in the recognition of the object motion. The error
of recognition will have large bad impact in encoding.
H.264/AVC is a new generation video encoding stan-
dard which is proposed by ITU-T and ISO. H.264/AVC
has high compression efficiency. x264[4] is the
open-source release of H.264/AVC which is used widely.
As x264 is not satisfying in the application of real-time
encoding for aerial video, x264 must be optimized firstly
before being used.
In x264, mode decision account for about 50%70%
in all the encoding time according to different command
line parameter. For P frame, the candidate modes include
P_skip, P16x16, P16x8, P8x16, P8x8, P4x4, P8x4, P4x8,
I16x16, I8x8, I4x4. For B frame, the candidate modes
include B_skip, B16x16, B16x8, B8x16, B8x8, I16x16,
I8x8, I4x4. We must calculate the encoding cost of all
the modes to select the best one to gain the best com-
pression efficiency. x264 use the Rate Distortion Cost[1]
to measure the encoding cost. The process of calculating
all the modes’ encoding cost will cause so heavy compu-
tation. How to reduce the unnecessary computation is the
focus of this paper.
Currently, some scholars propose improvement pro-
gra ms about the mode decision[5-8]. In one word, the
main process is like this. Because of the correlation of
adjacent frame in time and space for the aerial video,
they predict the mode type of current macro block di-
rectly according to the analysis of the surrounding macro
block’s mode, motion vector and content. This method
reduces lots of needless computation. But the pred iction
still needs lots of analysis and computation and may not
be so accurate. It has limited optimization for the appli-
The second part in this paper mainly a na l yse s the cha-
racter of the aerial video. The third part proposes the op-
timization algorithm in mode decision for the aerial vid-
eo. The fou r t h part optimizes the motion estimation and
the fift h part makes the simulation of the two algorithms.
The last part gives the conclusion.
2. Character of Aerial Video
In aerial video, the change of adjacent frame is caused by
the motion and the shake of the camera mainly. The mo-
tion of object in video will lead to so slight change that
we can omit it. This means that the middle part of every
Copyright © 2013 SciRes. OJAppS
frame forms from the rotation and flat move of last frame
and the edge is made of the new area.
Figure 1. Video Sequence Change.
From the perspective of motion vector, the direction of
motion vector is consistent for most macro blocks. But
the magnitude of the motion vector has something to do
with the angle between the camera and horizon. When
the camera is parallel with the horizon, the magnitude is
mostly the same which can be called the global motion
vector. When the camera forms some angle with the ho-
rizon, the magnitude of the upper macro blocks are less.
However, the motion vector is almost the same for the
adjacent macro blocks in direction and magnitude.
In one word, the character of the aerial video is as fol-
(1) The change of the adjacent frame mainly occurs in
the edge district.
(2) The motion vector of most adjacent macro blocks
are the same in direction and magnitude.
3. Mode Decision Optimization
In the process of mode decision, the big macro
blocks(16x16) are more suitable for the area of back-
ground or changing slowly because of the same motion
vector. While the small macro blocks(4x4) are fitter for
the area changing quickly. As in these areas, we can split
the big macro block into the small macro blocks to do
motion estimation respectively to gain better compres-
sion efficiency. Based on the analysis and the character
of aerial video, we do some statistics about the mode
decision on aerial video. We define that the edge area is
the district whose distance is less than 5% to the boun-
dary. We split the frame into two areas called the edge
and the middle respectively. Then we do the statistics
about the mode type for the two areas. Table 1 shows
that the mode type ratio in the edge and the middle area.
From Table 1, we can see that in the middle area, the
skip mode and inter-frame 16x16 mode account for about
98% and the intra-frame mode about 1.5%, others about
less than 0.5%. The current x264 encoder will seek the
less than 0.5% inter-frame mode(except skip and In-
ter-fra m e 16x16 mode) in the cost of calculating all the
inter-frame modes.
Table 1. Mode Type Ratio .
Skip Inter16x16 Inter16x8 Inter8x16
41.33% 52.78% 0.13% 3.81%
Mid 41.18% 56.93% 0.11% 0.25%
Inter8x8 Intra16x16 Intra8x8 Intra4x4
Mid 0.07% 1.0% 0.44% 0.02%
This process will consume lots of encoding time and
enhance little compression efficiency. Therefore, we
propose an optimization algorithm. In the edge area, we
compute all the intra-frame mode and inter-frame mode
to find the best mode. While in the middle area, we only
check the skip mode, inter-frame 16x16 mode and in-
tra-frame mode, Omitting the check of the other in-
ter-frame mode. The flow of mode decision after optimi-
zation is as Figure 2 sho ws.
Figure 2. Mode Decision Flow
4. Motion Estimation Optimization
Motion estimation is the process of searching the macro
block which makes the motion compensation least. This
is the most time-consuming process. x264 realizes 5
kind s of full -pixel motion estimation algorithms. They
are dia(diamond search), hex(hexagon search),
umh(UMH -e xa gon search), esa(exhaustive search) and
Inter 16x16 mode selection, satisfy
termination early?
macro block in the edge?
Other inter mode decision
Satisfy termination early?
satisfy skip mode?
Intra-frame mode decision
Mode decision
Input macro block
Output mode
Copyright © 2013 SciRes. OJAppS
tesa(hadamard exhaustive search)[4]. The compression
efficiency becomes better, the computation more com-
plex, and encoding time longer. We do some statistics
about the five motion estimation algorithms with the
aerial video. Then we compare the change in encoding
time, total bits and PSNR. The result shows in Table 2.
Table 2. Contrast of Motion Estimation Algorithms
dia hex Umh
Coding time 14.00s 14.84s 18.13s
Total bits 424.9KB 424.3KB 424.2KB
PSNR 41.91 41.92 41.94
esa tesa
Coding time 27.52s 33.35s
Total bits 423.9KB 422.5KB
PSNR 41.97 41.97
In Table 2 we can see that the encoding time increase
by 138% at most, while the total bits and PSNR change
within 0.5% which can be omitted. According to the
character of the aerial video, the motion vector of the
adjacent macro block is familiar. x264 encoder can pre-
dict the motion vector accurately with some simple me-
thod such as median prediction. Based on the predicted
motion vector, the encoder can find the macro block very
fast which makes the current motion compensation least.
Therefore, the best motion estimation method for aerial
video is diamond search given compression efficiency
and encoding time.
Diamond search algorithm is the process of iteratively
matching macro blocks with diamond template. For the
diamond search algorithm in aerial video, we do the sta-
tistic about the relationship between iterative counts and
ratio. The result shows in Table 3.
Table 3.Diamond Search Algorithm Iterative Number
1 2 3 4 5
65.7% 25.3% 5.60% 1.41% 0.62%
6 7 8 9 10
0.37% 0.24% 0.17% 0.12% 0.08%
11 12 13 14 15
0.07% 0.05% 0.03% 0.02% 0.02%
From Table 3, we can see something as follows:
(1) The macro blocks whose iterative number is 1 ac-
count for 65.7%. This kind of macro block’s matching
macro block is the same as the predicted one. Therefore,
iterative computation is not usef ul for this kind macro
(2) The macro blocks whose iterative number are 2 or
less than 2 account for about 96.6%. The macro blocks
whose iterative number is small accounting for so high
ratio shows that the prediction for the motion vector is
very accurate. The matching macro block is almost near
the predicted one.
According to the fact, this paper proposes an algorithm
which is used to terminate the motion estimation earlier
by self-adaptive threshold.
(1) Initialize the variable threshold = 0.
(2) Calculate the average cost of predicted matching ma-
cro block.
)*/( WHPCAC =
AC means average cost of predicted matching macro
block. PC means the cost of predicted matching macro
block. H and W are the height and the wide of current
macro block.
If the average cost AC is less than (ratio * threshold),
terminate the motion estimation, otherwise go to step 3.
Ratio is the control constant which can be defined by
ourse l ve s.
(3) Conduct diamond search. If the count of the iteration
equal to 1, calculate average value between the threshold
and average cost AC computed in the step 2, assign the
value to threshold. If the count of iteration is more than 1,
fini sh the motion estimation directly.
Algorithm statement:
(1) Initialize the threshold at the first time we conduct the
motion estimation.
(2) At the second step, the prediction cost is calculated
according to the predicted vector which is realized in
(3) Ratio is a constant used to control the filtering. For
example, the ratio being set to 0 means filtering nothing,
the same to no optimization. If the ratio is set to 100000,
this means all the motion estimation will be omitted.
According to the statistic, it is reasonable that the ratio is
set to 0.8.
5. Simulation
This part simulates the optimization algorithms in the
third and fo ur th part describe respectively. Then we
combine the two algorithms to do the simulation together.
The simulation result is as follows.
We take some aerial videos in the suburbs of Beijing
whose contents include farmland, forest, factory school
and moving cars. We name the video as video1, video2 ,
video3, video4, video5.
Copyright © 2013 SciRes. OJAppS
Figure 3. Aerial Video Samples
Firstly, we explain some command line parameters in
(1) --no-psy, disable psychology optimization. PSNR
will fail if the psychology optimization enabled.
(2) --qp 30, qp is quantizatio n parameter. 30 is a good
threshold for the video encoding for our psychology.
(3) --partitions all , select the best mode from all the
mode type.
(4) -me[dia, umh, hex, esa, tesa], which motion estima-
tion method will be selected. In our experiment we
choose dia.
(5) --psnr , calculate the signal and noise ratio.
The negative data in the following tables mean s de-
crease after optimization. Positive data means increase
after optimization.
We optimize the x264 encoder as the third part de-
scrib es and Table 4 shows the change in the field of en-
coding time, total bits and PSNR after optimization.
Table 4. Change after Mode Decision Opti mization.
Total bits
We optimize the x264 encoder as the fourt h part states
and Table 5 shows the difference after optimization.
From the experiments we can reach some conclusions.
1) The two algorithms both decrease the needless com-
putation to reduce the encoding time with the total bits
and PSNR decreasing lightly.
2) From the first two experiments we can see that the
mode decision optimization is better than motion estima-
tion optimization.
3) The combined optimization is better than the separate
4) The two algorithms are simple to realize and have
stable effect on the real-time encoding for the aerial vid-
Table 5. Change after Motion Estimation Optimiza-
Total bits PSNR
We combine the two algorithms and optimize the x264
encoder. The difference is as Table 6 shows.
Table 6. Change after Mode Decision and Motion Es-
timation Optimization.
Total bits PSNR
6. Conclusion
This paper analyzes the aerial video and summarizes its
characters. According to the characters, we optimize the
x264 encoder from two perspectives. 1: We omit the
needless mode decision to reduce the computation ac-
cording to the position of the macro block. 2: We com-
pute the threshold which is used to terminate the motion
estimation earlier to reduce the encoding time. The two
optimization save about 19% encoding time with the bit
rates and PSNR decreasing slightly.
[1] Draft Recommendation and Final Draft International
Standard of Joint Video Specification, Mar.2003, ITU-T
Rec.H.264 and ISO/IEC 14 496-10 AVC, Joint Video
[2] Malavika Bhaskaranand and Jerry D.Gibson.
“Low-complexity Video Encoding For UAV Reconnais-
sance And Surveillance.” The 2011 Military Communica-
tions Conference, Nov.2011.
[3] Holger Meuel, Macro Mundeloh and Jörn Ostermann.
“Low Bit Rate Based ROI Video Coding for HDTV
Aerial Surveillance Video Sequence.” 2011 IEEE Com-
puter Society Conference On Computer Vision And Pat-
tern Recognition Workshops, June 2011.
[4] x264 Software(120711). Available from:
Copyright © 2013 SciRes. OJAppS
[5] Huanqiang Zeng,Canhui Cai and Kai-Kuang Ma. “Fast
Mode Decision For H.264/AVC Based On Macroblock
Motion Activity.” IEEE Transactions On Circuits And
Systems For Video Technology, Vol.19, No.4,
pp.491-499, April 2009.
[6] Tiesong Zhao, Hanli Wang, Sam Kwong and C.-C.Jay
Kuo. “Fast Mode Decision Based on Mode Adaptation.”
IEEE Transactions On Circuits and Systems For Video
Technology, Vol.20, No.5, pp.697-705, May 2010.
[7] D.Wu, F.Pan, K.P.Lim, S.Wu, Z.G.Li, X.Lin, S.Rahardja,
and C.C.Ko. “Fast Intermode Decision in H.264/AVC
Video Coding.” IEEE Transactions On Circuits And Sys-
tems For Video Technology. Vol.15, No.7, pp.953-958,
July 2005.
[8] Po-Hung Chen, Hung-Ming Chen, Mon-Chau
Shie,Che-Hung Su,Wei-Lung Mao,Chia-Ke. Huang.
“Adaptive Fast Block Mode Decision Algorithm for
H.264/AVC.” 2010 the 5th IEEE conference on Industrial
Electronics and Applications, June 2010.