New block motion estimation algorithm for video compression
C.-K. Ning, K.-T. Lo and H.-T. Tang
Centre for Multimedia Signal Processing
Department of Electronic and Information Engineering
The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
enktlo@polyu.edu.hk
Abstract: Most of the existing fast block matching algorithms like three-step search (TSS) and fourstep search (4SS) are optimized only for smaller search window. Their MSE performance will be
degraded when the window size is getting larger, which is unreasonable that a poorer performance is
obtained when more efforts are done. To tackle this problem, a new quadrant based four-step search
(QB4SS) algorithm is developed in this paper. This algorithm refines the searching steps of the 4SS so
as to adjust the searching pattern to suit for different local characteristics of the block. Experimental
results show that the proposed algorithm achieves much better MSE performance than other search
methods when the search range is set to ± 15.
Keywords: Video compression, motion compensation, motion estimation
[2-10]. However, most of these fast search
algorithms like the three-step search (TSS) do not
encounter the local characteristic of the test
block, and only a regular search pattern is applied
for all kinds of motion. Moreover, they preassume the motion of the scene is mainly centerbiased in [6]. Actually, most of the motions in
real life may not be center-biased. On the other
hand, the search pattern of such algorithms is
optimized for small search window only. Their
MSE performance will be degraded when the
search window becomes larger. In this paper, a
quadrant based four-step search (QB4S)
algorithm is proposed for block matching motion
estimation. The QB4S uses different search
patterns for blocks with different characteristics.
For each testing block, the motion type is first
determined and the corresponding search pattern
is applied. With this arrangement, the probability
of trapped by local minima will be reduced.
Introduction
Motion Compensation is one of the efficient
interframe coding techniques for its ability to
reduce the high redundancy between successive
frames of an image sequence.
Motion
compensation consists of two main processes:
motion estimation and prediction error coding.
Motion estimation is to obtain the movement
between successive frames and the resulting
motion information is then exploited in
interframe predictive coding. Both the motion
information and the prediction error are requested
to transmit to the receiver for image
reconstruction. The better and higher speed of
the motion estimation process is, the more
efficient the encoding process is.
In the last two decades, various motion
estimation techniques have been proposed.
Among them, the most popular method so far has
been the block matching motion estimation
technique. This approach is used in various
coding standards like MPEG [1]. The block
matching (BM) method is to find out a candidate
block, within a search window in the reference
frame, that is most similar to the test block in the
current frame. The full search method tests all
locations in the search window and produces the
optimal results. If the performance in terms of
prediction error is only the criterion for BMA, the
full search method is optimal.
However, its
large computational requirement makes it
difficult to be realized in hardware or software.
Therefore, investigation of fast search algorithms
has been the focus of research in the last decade
In the rest of the paper, section 2 describes the
concept of the proposed fast algorithm and the
details of the quadrant based four-step search
(QB4SS) algorithm are discussed in section 3.
Computer simulations are used to evaluate the
performance of the new algorithm and the results
are presented in section 4. Finally, some
concluding remarks are given in section 5.
Concept of the Quadrant Based Four-Step
Search Algorithm
In general, most of the fast search algorithms [210] are based on two very important assumptions.
The first assumption is that MAD will increase
84
monotonically as the checking point moves away
from the global minimum and the second is the
principle of locality. With the multiple stage
search schemes like TSS, the checking points in
each step are allocated uniformly in the search
window. Such a configuration may not be
appropriate for those blocks having small
motions. Also, most of the fast algorithms, their
probability of trapped by local minima will
increase when search range increases. The
reasons for that phenomenon are: 1) The
reliability of monotonic assumption and principle
of locality for the error surface plane are greatly
reduced; and 2) Continuing reduce in step size
will greatly accumulate the error especially for
large motion.
Step 3: If the minimum point is located at the
middle of horizontal or vertical axis of the
pervious search windows, use the searching
pattern of four-step search to locate the motion
vection. The step is demonstrated in Fig.1b.
Step 4: If the minimum point is located at the
corner of horizontal or vertical axis of the
pervious search windows, most likely the block
will have a large motion and the motion vector
will be located in the quadrant of the minimum
point. In this case, a hierarchical search is
designed to locate the motion vector in this
quadrant.
For the hierarchical search, the search points in
the target quadrant will be sub-sampled by a
factor of 2 (or 4 when the search range is
doubled). All marked points are then checked.
When the minimum point is found, eight more
neighboring points are checked to conclude the
search and locate the motion vector. The step is
illustrated in Fig.1c.
On the other hand, some search schemes such as
new three step search (NTSS), eight extra points
are added in the first step to determine whether
the motion of the block is center-biased or not.
Such configuration is not good for large motion.
Therefore, how to optimally design a universal
search pattern becomes the main concern of this
paper.
In our algorithm, the first task is to determine
whether the motion of the block is center-biased
or not. This can be accomplished by checking
the location of the point having smallest mean
absolute difference (MAD) value in the nine test
points at the first step. If the minimum point is at
the center point, then most likely the motion will
be center-biased. It should be a stationary and
quasi-stationary motion block. Otherwise, it
belongs to fast motion block. In that case, we
need to determine it belongs to diagonal,
horizontal or vertical motion.
Quadrant Based 4-Step Search
In the proposed QB4S algorithm, a center-biased
search pattern with 9 checking points on a 55
window is utilized in the first step. Then the
search method of next step is depended on the
location of the minimum points. The QB4S
algorithm is summarized as follows:
Figure 1 Different search paths of QB4S
Simulation Results
Step 1: Search the 9 checking points on a 55
window
Computer simulations have been performed to
compare the performance of the proposed
Quadrant Based 4-step step (QB4S) with full
search (FS), three step search (TSS), new three
step search (N3SS), four step search (4SS),
orthogonal search (OSA) and 2D_logrithms
Search (2D-Log). Two test sequences, namely
“Miss America” and “Tennis”, are used in our
Step 2: Find out the point with minimumt MAD
value. If the location of the minimum point is in
center, use the searching pattern of the block
based gradient descent search (BBGDS) to locate
the motion vector, which is illustrated in Fig.1a
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experiments. They are of CIF standard and the
simulations have been performed on the first 90
frames of the sequences. In our experiment, the
maximum motion displacement W is set to ± 7
and ± 15. The performance of the motionpredicted images is measured by mean square
error (MSE), and the computational complexity is
evaluated by the average number of search point
per frame.
Tennis with 15 search window
800
FS
700
MSE per pixel
TSS
600
N3SS
500
4SS
400
Ortho
300
2D_log
200
QB4S
85
79
73
67
61
55
49
43
37
31
25
19
7
1
0
13
100
frame number
Figure 2 shows the MSE performance of different
search methods for the two test sequences with
different search ranges. Tables 1 to 4 summarize
the average MSE performance and the average
number of search points for various search
methods for the two test sequences.
(d) Tennis with search range ± 15
Figure.2 MSE performance of different search
methods
Missa with 7 search window
MSE per pixel
16
FS
TSS
N3SS
4SS
Ortho
2D_log
Q4BS
FS
14
TSS
12
NtSS
10
4SS
8
Ortho
6
2D_log
4
QB4S
2
Average
Search
points/frame
225
25
21.67
19.249
13.0
15.105
18.966
Average MSE per
frame
8.177295
8.83
8.2779
8.6937
9.818
8.67
8.5856
85
78
71
64
57
50
43
36
29
22
8
15
1
0
Table 1 Average MSE performance for Miss America
with search range ±7
FrameNumber
(a) Miss America with search range ± 7
FS
Missa with 15 search window
N3SS
FS
TSS
N3SS
4SS
Ortho
2D_log
Q4BS
4SS
Ortho
2D_log
89
81
73
65
57
49
41
33
25
17
9
QB4S
1
MSE per pixel
TSS
16
14
12
10
8
6
4
2
0
Average
Search
points/frame
961
33
21.61
27.56
17.0
19.27
22.8030
Average MSE per
frame
8.10
9.025
8.295
8.79
9.889
8.83
8.5819
frame number
Table 2 Average MSE performance for Miss America
with search range ±15
(b) Miss America with search range ± 15
Tenni s wi t h 7 search wi ndow
FS
600
TSS
N3SS
400
4SS
300
Ort ho
200
2D_l og
100
QB4S
89
81
73
65
57
49
41
33
25
9
17
0
1
MSE per pixel
500
f rame number
FS
TSS
N3SS
4SS
Ortho
2D_log
Q4BS
Average
Search
points/frame
225
25
22.62
20.106
13.00
16.105
19.175
Average MSE per
frame
180.5639
239.00
215.56
210.4068
296.79
215.42
206.85
Table 3 Average MSE performance for Tennis with
search range ±7
(c) Tennis with search range ± 7
86
Acknowledgement
FS
TSS
N3SS
4SS
Ortho
2D_log
Q4BS
Average
Search
points/frame
961
33
23.15
28.69
17.0
20.154
32.852
Average MSE per
frame
This work was supported by The Hong Kong
Polytechnic University under Grant A/C G-V466
and the Centre for Multimedia Signal Processing,
Department of Electronic and Information
Engineering, The Hong Kong Polytechnic
University.
158.57
258.632
236.62
229.09
333.63
236.69
196.227
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Table 4 Average MSE performance for Tennis with
search range ±15
It is seen that the FS method achieves the lowest
MSE, and our proposed QB4S method performs a
very little worse than FS. When compared with
other methods, our proposed QB4S method
performs better than TSS, OSA, 2D-Log and 4SS
in all cases since it requires less number of search
points but also results in smaller MSE than those
methods.
It is also noted from the tables that the
performance all search algorithms degrades when
the search range is increased from ±7 to ±15. The
degradation will become significant when the
sequence is of large motion like “Tennis”. It is
consistent with the fact that most of the search
algorithms are based on the center-biased motion
assumption. For our proposed QB4SS algorithm,
the MSE performance is improved when the
search window increases. Take the sequence
“Tennis” as an example, the average MSE of the
predicted images is 206.85 and 196.227 search
range of ±7 and ±15 respectively. So we may
conclude our proposed algorithm is robust and
can handle sequences with different motions.
Conclusions
In this paper, a new search algorithm called
quadrant based four-step search (QB4SS)
algorithm is developed for block matching
motion estimation. The search pattern of the
QB4SS algorithm is adjusted according to the
local characteristics of the block and is not
optimized only for small search window.
Experimental results show that QB4SS not only
achieves compatible MSE performance with
other existing algorithms, but also perform much
better than others when the search window is
getting larger. The improvement is more
significant especially for the sequences with large
motions.
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