An Efficient Motion Estimation Scheme
with Low Complexity in Video Coding
Hyo-sun Yoon
Mi-Young Kim(corresponding author)
Department of computer science
Chonnam national university
Gwang-ju, South Korea
estheryoon@hotmail.com
Department of health and medical
Jeonnam Provincial college
Damyang, Jeollanamdo, South Korea
kimmee@dorip.ac.kr.
Abstract—To transmit and to store digital video sequences,
compression is necessary. Motion Estimation (ME) Technique is
used to redundant data in video sequences. ME which limits the
performance of image quality, generated bitrates and encoding
time requires huge complexity. To reduce the computational
complexity, a Hierarchical motion estimation scheme in Multiview Video Coding (MVC) is proposed. The proposed method
exploits the characteristics of the distribution of motion vectors
to terminate the motion estimation early and to place the search
points in the search area. Experiment results show that the
complexity reduction of the proposed method over FS and TZ
can be up to 98.9% and 42~78% respectively while maintaining
image quality and bitrates. This electronic document is a “live”
template and already
Keywords—motion estimation; motion vector; multi-view video
coding
I. INTRODUCTION
Digital video is being transmitted over various networks
and is stored in various storage devices. To transmit and store
digital video, compression is necessary. The international video
compression standards exploit motion estimation (ME)
technique to remove redundant data in video sequences. ME
technique which limits the performance of image quality and
the coding speed plays an important role in digital video
compression. Recently, there has been a growing interest in 3D
TV and free viewpoint video systems. These systems use
multi-view video which is obtained by capturing one threedimensional scene with many cameras at different positions.
The amount of initial data of multi-view video (MVV) is very
huge. To be stored or transmitted to the user, efficient
compression technique for MVV is needed. Multi-view video
coding (MVC) exploits motion estimation technique to remove
temporal redundancy and inter-view redundancy in
MVV[1][2].
ME techniques have become one of the most important
issues and have attracted much attention. Many motion
estimation methods have been proposed. FS which checks
every point in search area of the block in reference frame to
find best matched point requires huge complexity. Many fast
motion estimation methods have been proposed to reduce the
computational complexity while maintaining the image quality
at the same time. Diamond Search (DS) [3, 4], Hexagon Search
(HS) [5], Four Step Search (FSS) [6], Three step search (TSS)
[7], New Three Step Search (NTSS) [8] and Predictive Motion
Vector Field Adaptive Search Technique (PMVFAST) [9] are
well known fast motion estimation methods in H.263 and
MPEG 4. UMHexagonS[10] in H.264 is a kind of hierarchical
motion search strategy. These search methods [3-10] are
generally used for single view video sequence or small size
sequences. For multi view video sequence, TZ search
algorithm [11] is used. TZ search method performs raster
search after initial search. Both raster search and initial search
place search points that can cover the overall search area. It
implies no need to perform initial search when uiBestDistance
is larger than iRaster. It becomes the cause of using the
unnecessary computational complexity. To reduce this
computational complexity and to maintain the image quality,
we proposed an hierarchical motion estimation scheme in
multi-view video coding. The proposed search method exploits
the characteristics of the distribution of motion vectors to
terminate the motion vector search early and to place the
search points in the search area of reference frames.
This paper is organized as follows. Section 2 describes Pel
Block Sesrch (PBS)_and TZ search method in JMVC. The
proposed motion estimation scheme is described in Section 3.
Section 4 reports simulation results and conclusions are given
in Section 5.
II. PBS AND TZ
Pel Block Search (PBC) and TZ search are the motion
estimation algorithms for multi view video in JMVC. Pel
Block Search checks all points in search area of reference
frames to find the optimal motion vector. However, it requires
huge computational complexity. To reduce the complexity of
PBS, TZ search is used as a fast motion estimation algorithm.
TZ search method uses the search patterns in Fig. 1 and is
summarized as follows [12].
Step 1: Motion Vector Prediction - Median predictor, left
predictor, up predictor, upper right predictor and (0,0) are used
to decide a starting point for next step.
Step 2: Initial Grid Search - Run the diamond search with
different stride length X in search area[-96, 96] shown in Fig.
1(a)the obtained search center in the previous step. X is 1, 2, 4,
8, 16, 32 or 64. The points shown in Fig. 1.A are tested to
This research was supported by Basic Science Research Program through
the National Research Foundation of Korea (NRF) funded by the Ministry of
Education, Science and Technology (No. 2010-0024120)
c
978-1-4799-8389-6/15/$31.00 2015
IEEE
198
decide the point with the minimum SAD. The point with the
minimum SAD is taken as a search center for next search.
Calculate the distance between the search center and the point
with the minimum SAD. The stride length for this minimum
SAD point is stored in variable uiBestDistance. If the
uiBestDistance is equal to 0, decide the point with the
minimum SAD as the motion vector of the block and terminate
the motion estimation search. If uiBestDistance is larger than
iRaster, go to Step 3. Otherwise go to 4.
To reduce the computational complexity of TZ, a new
search scheme for motion estimation is proposed. The
proposed hierarchical motion estimation search scheme
exploits the fact that about 50% ~98% of the motion vector are
within a radius 2 pixels around the search origin (0,0) to
terminate the motion vector search early and to place the
search points in the search area.
Step 3: Raster Search - Raster Search is a simple full search
on a down-sampled version of the search window [-96, 96].
Run the raster search with raster length 3(iRaster=3) shown in
Fig. 1(b)he points on raster search pattern are tested. The point
with the minimum SAD is taken as a search center for next
search.
Step 4: Star Refinement Search - The search center is
moved to the point with the minimum SAD obtained from the
previous Step. Repeat the operation of the Step 2. The points in
this search pattern are tested and decide the point with the
minimum SAD. If the uiBestDistance is equal to 0, decide the
point with the minimum SAD as the motion vector of the block
and terminate the motion estimation search. Otherwise, repeat
this step until uiBestDistance =0.
(a)multi-diamond search pattern (b) modified diamond search pattern
(c)two-points search pattern
(d) modified raster search pattern
Figure 2. Proposed search patterns.
(a) initial grid search pattern
(b) raster search pattern
Fig. 1. TZ search patterns.
III.
PROPOSED HIERARCHICAL SEARCH SCHEME
There are some problems in TZ. At first, if the motion of
the block is large, raster search is performed after initial gird
search. Both raster search and initial grid search place search
points that can cover the overall search area [-96, 96]. It
becomes the cause of using the unnecessary computational
complexity. Secondly, TZ search method used star refinement
search which is similar to initial grid search. In star refinement
search, there is no need to check the points with stride length 4,
8, 16, 32 or 64. Because there is a strong likelihood that the
optimal motion vector exists around the point with the
minimum SAD obtained previous step. This refinement search
is performed repeatedly until uiBestDistance=0. This process
needs unnecessary complexity. Lastly, raster search is
performed when the motion of the block is large. Raster search
of TZ search configures iRaster as 3 in search area [-96, 96]
shown in Fig. 1(b). When the motion of the block is large,
iRaster has to be larger than 3. This is because refinement
search is performed after raster search.
The proposed motion estimation scheme is a hierarchical
search strategy. It consists of search patterns in Fig.2. Multi
diamond search pattern in Fig. 2.(a) is used as a first search
pattern to find the motion size of the current block. Modified
diamond search pattern in Fig. 2.(b) and two points search
pattern in Fig. 2.(c) are used as a last search pattern respective.
If the motion of the current block is large, modified raster
search pattern in Fig. 2.(d) is carried out. Modified diamond
Search pattern as a refinement search pattern is used to find the
best motion vector. The proposed search method is
summarized as follows.
Step 1: Motion Vector Prediction - Median predictor, left
predictor, up predictor, upper right predictor and (0, 0) are
used to decide a starting point for next step.
Step 2: Multi diamond search - Multi diamond search
shown in Fig. 2.(a) at the obtained search center in the previous
step is carried out. The points (ķĸĹin Fig. 2.(a)) in multi
diamond search pattern are tested and the point with the
minimum SAD is decided. Calculate the distance between the
search center and the point with the minimum SAD. The
distance is stored in uiBestDistance. If the uiBestDistance is
equal to 0, decide the search center as the motion vector of the
block and terminate the motion estimation search. If the
uiBestDistance is equal to 1, go to Step 3. If the uiBestDistance
is equal to 2, go to Step 5, Otherwise go to Step 4.
2015 IEEE 10th Conference on Industrial Electronics and Applications (ICIEA)
199
Step 3: two points search - Two neighboring points (ĸ
points) of the point with the minimum SAD obtained from the
previous Step is carried out. The point with the minimum SAD
is decided as the motion vector of the block. And then
terminate the motion estimation search. .
Step 4: Modified raster search - Run modified raster search
with raster length 5(iRaster=6) shown in Fig. 2(d). The points
on modified raster search pattern are tested. The point with the
minimum SAD is taken as a search center for next search
center.
Step 5: Modified diamond search - Modified diamond
Search at the point with the minimum SAD obtained from the
previous Step is carried out. The point with the minimum SAD
is decided. Calculate the distance between the search center
and the point with the minimum SAD. The distance is stored in
uiBestDistance. If the uiBestDistance is equal to 0, decide the
search center as the motion vector of the block and terminate
the motion estimation search. If the uiBestDistance is equal to
1, go to Step 3. If the uiBestDistance is equal to 2, repeat this
step .
IV.
terminate motion estimation search early. Simulation results
show that the encoding time for motion estimation is reduced
TABLE I. BDPSNR AND BDBR
Sequence
kbps
Exit
Uli
Ballroom
SIMULATION RESULTS
In this section, we show the experiment results for the
proposed motion estimation method. The simulation is carried
out on JMVC reference software version 6.0. Multi-view test
sequences are used for the experiment; Exit, Uli and Ballroom.
The frame size of Exit and Ballroom is 640*480 and the frame
size of Uli is 1024*768. The total number of frames is 100 in
each view. QP is varied from 22 through 37 (22, 27, 32 and
37). The search range is 96.
We compared TZ to the proposed method in image quality,
bit rates and the total encoding time. Table 1 shows the
Bjontegarrd Delta (BD) bitrate and BD-PSNR values taken
over the four QPs using the Bjntegaard Delta metric. The
results shows that there is almost 42(the motion of block is
small)~78(the motion of block is large)% reduction in the total
encoding time shown in Table 2, while obtaining negligible
change in the PSNR and bitrates shown in Table 1 using BDPSNR and BD-bitrates values. In other words, image quality
degradation of the proposed method over TZ is about 0.05(the
motion of block is small)~2.0(the motion of block is large)
(dB) and bitrates increment is about 1.3(the motion of block is
small) ~ 62.35(the motion of block is large) (kbps). We also
compared FS to the proposed method in image quality, bit rates
and the total encoding time. We used Ballroom and QP is 37.
Table 3 shows image quality, bitrates and encoding time. There
is almost 98.9% reduction in the total encoding time in Table 3
Race1
Flamenco
Sequence
dB
39.8
848
39.8
27
337
38.0
370
38.2
32
177
36.4
207
36.3
37
105
34.1
128
34.0
22
4148
38.9
4198
38.9
27
2197
36.9
2232
36.9
32
1215
34.4
1242
34.4
37
667
31.6
677
31.6
22
1494
39.0
1596
39.0
27
751
36.8
828
36.7
32
402
34.2
451
34.2
37
226
31.5
261
31.5
22
1024
39.9
1548
39.6
27
582
37.3
814
36.8
32
298
34.6
475
34.0
37
174
31.9
289
31.2
22
1759
41.2
1808
41.2
27
962
38.4
1103
38.3
32
521
35.3
549
35.3
37
278
32.3
299
32.3
QP
Exit
27
32
37
22
Uli
27
32
37
22
27
32
37
22
Race1
27
32
37
22
Flamenco
27
32
200
kbps
794
22
CONCLUSION
Pel Block Search (PBC) and TZ search are the motion
estimation algorithms To reduce computational complexity and
maintain the image quality, a new motion estimation search
method for multi-view video coding is proposed in this paper.
The proposed search method which is a hierarchical search
method exploits the characteristics of the distribution of motion
vectors to place the search points in the search area and
dB
22
BD
PSNR
BD
BR
dB
(%)
-0.2
13.8
-0.05
1.3
-0.4
12.0
-2.0
62.3
-0.4
9.2
TABLE Ċ. COMPARISION VALUES OF ENCODING TIME
Ballroom
V.
Proposed
method
TZ
QP
TZ
Proposed
method
T
sec
sec
(%)
77122
69999
64453
60665
386955
355760
324375
298839
83309
78003
72337
67215
215806
199540
176361
154004
69597
65944
61253
41718
38155
36107
34985
113865
106458
101272
96424
45344
42220
40429
37752
46724
43295
40286
37864
27254
26127
24961
-46
-45
-44
-42
-71
-70
-69
-68
-46
-46
-44
-44
-78
-78
-77
-75
-61
-60
-59
2015 IEEE 10th Conference on Industrial Electronics and Applications (ICIEA)
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60091
23375
-61
TABLE ċ. BITRATES, PSNR AND ENCODING TIME
Full Search
Ballroom
(QP:37)
Proposed Method
(kbps)
(dB)
(sec)
(kbps)
0 view
295
32.1
417191
309
32.0
(dB)
4497
(sec)
1 view
156
31.2
434363
195
31.0
4853
2 view
245
32.2
427006
308
32.2
4573
3 view
153
31.2
436203
200
30.9
4874
4 view
268
31.5
428528
319
31.4
4623
5 view
171
31.7
434723
219
31.5
4862
6 view
265
31.8
428527
326
31.8
4660
7 view
244
31.1
430623
297
31.0
4810
by 42~78%, with negligible degradation in PSNR and increase
in bitrates
ACKNOWLEDGMENT
This research was supported by Basic Science Research
Program through the National Research Foundation of Korea
(NRF) funded by the Ministry of Education, Science and
Technology (No. 2010-0024120).
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