International Journal of Engineering Trends and Technology (IJETT) – Volume 23 Number 5- May 2015
Implementation of Novel Threshold Diamond Search
(TDS) Algorithm for Fast Motion Estimation
Chandana Pandey#1, Deependra Pandey *2
#
Department Of Electronics and Communication Engineering
Amity School of Engineering & Technology
Amity University, Lucknow Campus, India
Abstract— In entire video compression process the motion
estimation requires significant amount of computation.
There are many computational effective block motion
estimation algorithms. These fast motion estimation
algorithms reduce the computational complexity, at the
expense of reduced performance. This results a significant
interest in the research community to develop novel
algorithms that are capable of saving computations with
minimal effects on the coding quality. A novel Threshold
Diamond Search (TDS) algorithm for fast block matching
motion estimation has been proposed in this paper. In this
algorithm we assumed monotonic error surface. A threshold
condition to early terminate the Diamond Search (DS) block
matching process is used to speed up motion estimation time
and to reduce the computation. Simulation results show that
this algorithm reduces the average computation with a very
low degradation in video quality by providing speed
improvement of about 90% to 96% over Exhaustive Search
(ES) algorithm and about 17% to 37% over DS algorithm.
Keywords— Video compression, motion estimation
algorithms, Diamond Search, Exhaustive Search, early
termination.
useful for the video images that have little changes in
their quality. In this method the current Mean Absolute
Difference (MAD) value is compared with a threshold
‗th‘. If the MAD<=‘th‘, the searching process will stop in
advance so that we can improve the searching speed and
can reduce computational complexity.
In this paper we specifically reviewed the diamond
search algorithm and then a new modified fast block
matching algorithm has been proposed called Threshold
Diamond Search (TDS). The performance of this
algorithm is compared with ES and DS in terms of Peak
Signal to Noise Ratio (PSNR) and computations
performed.
Coming up in section II, review on the diamond
search algorithm is presented. Following that, in section
III we have discussed in detail our new proposed
searching algorithm. In section IV performance metrics
are defined. In section V the algorithm is implemented
and comparisons are made between the proposed TDS
and other fast motion estimation algorithms. Finally, we
draw conclusion in section VI.
I. INTRODUCTION
Video processing is expensive in terms of
computations and storage requirements. This is the reason
for an extensive and ongoing research in video
compression leading to existing standards and algorithms
in this field [1]. Motion estimation plays a key role in the
entire process of video compression. Motion estimation
algorithms can be of three kinds - pixel based, block
based and region based [2]. The block based motion
estimation (BBME) is the best in terms of quality and
simplicity. Block matching motion estimation algorithms
such as the three-step search and the diamond search
algorithms are currently being used in video coding
schemes as alternatives to full search algorithms [3-4].
Fast motion estimation algorithms reduce the
computational complexity, at the expense of reduced
performance. In order to speed up these algorithms
assume monotonic error surface. In block based motion
estimation computations can be reduced by reducing
number of search points and by the use of early
termination process [5]. In early termination strategy a
premature end is done to reduce block matching
computation in the search process [6]. This technique is
II. DIAMOND SEARCH ALGORITHM
DS is the most accurate suboptimal ME algorithm
among others. This is why it was chosen to be
implemented in the reference software of the standard
H.264 [7]. The Diamond Search algorithm (DS) is a fast
BBME. It was proposed by Shan Zhu and Kai-Kuang Ma
in the year 2000. The DS [7-8] algorithm makes use of
two types of search patterns. The first type of search
pattern is called large diamond search pattern (LDSP) and
it contains nine checking points out of which eight points
are surrounding the center one point to compose a shape
of diamond. The second type of search pattern contains
five checking points to compose a small shape of
diamond. Hence it is called as small diamond search
pattern (SDSP). In the searching procedure of the DS
algorithm, LDSP is repeatedly used until the minimum
block distortion (MBD) occurs at the center of the search
pattern. The switching from LDSP to SDSP takes place
when it reaches the final search stage. Out of the five
checking points in SDSP, the position yielding the
minimum block distortion (MBD) provides the motion
vector of the best matching block. The whole process is
summarized in the following Fig 1.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 23 Number 5- May 2015
Step 5: Stop searching. The center point is the final
solution of the motion vector which points to the best
matching block.
Start
Video
Frames
Fig. 1 Working of Diamond Search (DS) algorithm from (a) to
(d) [8]
But DS cannot stop the search early even when the
SAD at a particular checking point is already small
enough, e.g., below a threshold value [9]. Therefore by
incorporating threshold technique in this method, we can
stop unnecessary searching. Hence we can easily reduce
the computational complexity by accepting a little
degradation in the quality of the video sequences.
III. PROPOSED ALGORITHM
The motion field of consecutive frames in a video
sequence is smooth and gentle. Therefore the optimum
Global Motion Vectors (GMVs) are located at or very
close to the search center most of the time [10]. In this
paper, we exploit the centre bias property of the real
world video sequences to reduce the computational
complexity by comparing the MAD value of initial search
center with a predefined threshold value (‗th‘=100).
The TDS algorithm is summarized as follows:
Step 1: The MAD of the search center (0,0) is calculated
and is compared with the threshold value ‗th‘. If
MAD(0,0)<=‘th‘, then jump to step 5 otherwise go to step
2.
Step 2: Test the nine checking points of LDSP. If the
MBD point is located at the center position then go to
step 3, otherwise reposition the MBD point found in the
previous step as the center point to form a new LDSP and
recursively repeat this step till the MBD point is found at
center.
Step 3: If MAD of center point is less than or equal to ‗th‘
then go to step 5 otherwise go to step 4.
Step 4: Change the search pattern to SDSP and find the
MBD point then stop.
ISSN: 2231-5381
Current frame
Reference frame
Divide frame in macroblocks
Divide frame in macroblocks
Calculate MAD at the center
of search window (0,0)
Select a macroblock
Yes
If
MAD(0,0)
is <=‘th‘
No
Test 9 checking points of LDSP
If MBD point
is located at
center
No
Re-position the MBD
point as center point
to form a new LDSP
Yes
If MAD of
MBD point
<=‘th‘
Yes
No
Change from LDSP to SDSP to find
the MBD point
Stop
Fig. 2 Flowchart of TDS algorithm
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International Journal of Engineering Trends and Technology (IJETT) – Volume 23 Number 5- May 2015
IV. PERFORMANCE METRICS
The best match for a block present in the current
frame can be found in the reference frame by making use
of some matching criterion [11]. There are a number of
criteria available to evaluate the ―goodness‖ of a match.
Three popular matching criteria used for block-based
motion estimation are- mean of squared error (MSE),
mean absolute difference (MAD) [12] and matching pixel
count (MPC). Among these distortion criteria, the most
common MAD has the minimum computational load [13].
MAD between current and reference blocks with size
N×N is calculated as:
(a)
Salesman Frame
(b)
Miss America Fame
(c)
Caltrain Frame
(d)
Surfside Frame
(e)
Trivor Frame
…………………………….. (1)
MSE between current and reference blocks with size
N×N is calculated as:
…………………………….. (2)
Peak Signal to Noise Ratio (PSNR) given by equation
(3) characterizes the motion compensated image that is
created by using motion vectors and macro clocks from
the reference frame. PSNR [14] is used for quality
comparison between different algorithms.
…………………………….. (3)
V. IMPLEMENTATION AND RESULT
This section contains the implementation results of the
exhaustive search algorithm [15], diamond search
algorithm and the proposed threshold diamond search
algorithm. The experiments were performed on Intel(R)
Core(TM) i3-3217U CPU@1.80GHz configured system
using MATLAB version R2010a. The Fig. 3 shows the
first frame for input video sequences. In these video
sequences the reference frames are divided into macro
blocks of size (l6,16) pixels and to find where the macro
blocks present in the reference frame moves in the next
frame we have applied the above mentioned motion
estimation algorithms. To find motion vectors we used
the search range ‗p‘ of (+7,-7) and mean absolute
difference (MAD). Table I displays average PSNR and
the computation values required to find the motion
vectors for ES, DS and TDS algorithms using different
video sequences.
The proposed algorithm provides speed improvement
of about 90% to 96% over Exhaustive Search (ES)
algorithm and about 17% to 37% over DS algorithm.
The comparison graph for computation values acquired
from 'Salesman', ‘Miss America‘, ‘Caltrain‘, ‘Surfside‘
and ‗Trevor‘ video sequences for ES, DS and TDS
algorithms are presented in Fig. 4-8.respectively.
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Fig. 3 First frame of input video sequences
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International Journal of Engineering Trends and Technology (IJETT) – Volume 23 Number 5- May 2015
Table 1 PSNR and the computation values for ES, DS and TDS algorithms using different video sequences
.
Video
Sequences
Block Matching
Algorithm
Average PSNR
ES
35.05
204.28
DS
34.93
13.07
TDS
34.29
8.27
SALESMAN
MISS AMERICA
CALTRAIN
SURFSIDE
TREVOR
Average
Computations
ES
37.80
204.28
DS
37.50
16.24
TDS
36.85
11.40
ES
30.37
207.41
DS
30.24
15.92
TDS
26.72
11.05
ES
34.84
218.48
DS
34.48
26.17
TDS
34.41
21.66
ES
34.88
199.51
DS
34.85
12.6455
TDS
34.36
7.89
Speed Improvement
Ratio (SIR)
Over ES
Over DS
95.94
36.69
94.41
29.78
94.67
30.62
90.08
17.24
96.04
37.56
Fig. 4 computation comparision for salesman video sequences
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International Journal of Engineering Trends and Technology (IJETT) – Volume 23 Number 5- May 2015
Fig. 5 computation comparision for missa video sequences
. 6 computation comparision for caltain video sequences
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International Journal of Engineering Trends and Technology (IJETT) – Volume 23 Number 5- May 2015
Fig Fig. 7 computation comparision for surfside video sequences
Fig. 8 computation comparision for trevor video sequences
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International Journal of Engineering Trends and Technology (IJETT) – Volume 23 Number 5- May 2015
VI. CONCLUSION
In this work the conventional DS algorithm is
modified by adopting early termination method to reduce
the search points, computational cost and computation
time in the block based motion estimation process. Its
performance is compared with Exhaustive Search (ES)
and Diamond Search (DS) by measuring the PSNR
values and computations performed. The results obtained
shows that the proposed algorithm has improvements
over the existing DS algorithms in terms of average
number of computations with a very low degradation in
quality of video sequences. Hence TDS algorithm speeds
up the searching process in BMME.
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Acknowledgment
The authors would like to thank Maj. General K. K.
Ohri (AVSM, Retd.) Pro-VC & Director General, Amity
University, Uttar Pradesh Lucknow, Wg. Cdr. (Dr.) Anil
Kumar (Retd.) (Director ASET, Lucknow Campus),
Brig. U. K. Chopra (Director AIIT & Dy. Director
ASET) and Prof. O. P. Singh (HOD, Electronics) for
their valuable comments and suggestions that helped to
improve this paper.
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