A Survey on Block Matching Algorithms for Video Coding

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 7, No. 1, February 2017, pp. 216~224
ISSN: 2088-8708, DOI: 10.11591/ijece.v7i1.pp216-224  216
Journal homepage: http://iaesjournal.com/online/index.php/IJECE
A Survey on Block Matching Algorithms for Video Coding
Adapa Venkata Paramkusam, Vuyyuru Arun
Department of Electronics and Communication Engineering, MLR Institute of Technology, Hyderabad, India
Article Info ABSTRACT
Article history:
Received Oct 7, 2016
Revised Dec 30, 2016
Accepted Jan 15, 2017
Block matching algorithm (BMA) for motion estimation (ME) is the heart to
many motion-compensated video-coding techniques/standards, such as ISO
MPEG-1/2/4 and ITU-T H.261/262/263/264/265, to reduce the temporal
redundancy between different frames. During the last three decades,
hundreds of fast block matching algorithms have been proposed. The shape
and size of search patterns in motion estimation will influence more on the
searching speed and quality of performance. This article provides an
overview of the famous block matching algorithms and compares their
computational complexity and motion prediction quality.
Keyword:
Block matching algorithm
Motion estimation
Motion vector
Search pattern
Copyright © 2017 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Adapa Venkata Paramkusam,
Department of Electronics and Communication Engineering,
MLR Institute of Technology,
Hyderabad, India.
Email: adapa74@gmail.com
1. INTRODUCTION
The main objective of block matching algorithms is to determine the direction and magnitude of
motion (motion vector) for a macroblock in the current frame relative to the best matched candidate block in
the reference frame. The search for the best matching candidate block may be carried out by comparing the
macroblock in the current frame with some or all the possible candidate blocks in a search window. Full-
Search (FS) is an exhaustive search algorithm and it is the simplest method to find the optimal motion vectors
for each macroblock. It determines the best matched candidate block through calculating the Sum of
Absolute Difference (SAD) for all candidate blocks in the search window. Although Full-Search can obtain
the global optimal result, it has very intensive computations.
To reduce the complexity of Full-Search, several fast search motion estimation algorithms have
been proposed at the price of slightly impaired Peak Signal-to-Noise Ratio (PSNR) performance. A possible
classification of these fast search motion estimation algorithms into the following categories: reduction in
search positions [1-9], predictive search [10-14], search pattern switching [15], [16], multi-resolution
search [17-20] and Fractional-Pixel Interpolation [21-22]. Existing fast search motion estimation algorithms
use one or a combination of these categories. A popular example is the three-step search (TSS) algorithm [1].
However, its uniformly spaced search pattern is not well matched to most real-world video sequences in
which the motion vector distribution is non-uniformly biased toward the zero vector. Such an observation
inspired the new three-step search (NTSS) which has a centre-biased search pattern and supports a halfway-
stop technique [2]. The centre-biased NTSS algorithm is an improved version of TSS, tends to achieve much
superior performance with fewer number of search points on average. The search pattern has an important
influence on speed and distortion performance in block motion estimation.
In TSS and NTSS algorithms square-shaped search patterns of different sizes are employed. The
Diamond search (DS) algorithm adopts a diamond-shaped search pattern [3], which is more efficient than

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A Survey on Block Matching Algorithms for Video Coding (Adapa Venkata Paramkusam)
217
square-shaped search patterns TSS and NTSS. The search patterns in DS algorithm are derived from the
checking points within circle of radium of 2 pels. The hexagon based search algorithm [4] was developed by
investigating why the DS pattern can yield speed improvement over some square-shaped search patterns and
what the mechanism behind is.
Hexagon-based search (HS) algorithm can achieve substantial speed improvement over the DS
algorithm with similar distortion performance. The enhanced versions of hexagonal search algorithm [6-8]
have been proposed for further reduction of the search points over Hexagonal Search algorithm. These
algorithms mainly concentrate on the fast inner search technique to improve the inner search speed. The main
purpose of this paper is to make a comprehensive studyof block matching algorithms. Comparisons in many
directions are made for system designers to determine the best tradeoff. The rest of this paper is organized as
follows. In section II, The well known block matching algorithms such as three step search, New three step
search, Diamond search, Hexagon based search and enhanced versions of hexagonal search algorithm have
been discussed. Section III is devoted to the discussions of the experimental results for various sequences.
Finally, section IV concludes this paper.
2. OVERVIEW
2.1. Three Step Search (TSS) Block Matching Algorithm
Three Step Search (TSS) is one of the first non full search algorithms and was introduced by Koga et
al [1] in 1981. It searches for the best motion vectors in three steps. The search pattern of TSS is shown in
Figure 1. In the first step 8 blocks, at a distance of step size equal to or slightly larger than half of the
maximum search range, from the centre point corresponding to zero motion vector are selected for
comparison. In the second step the step size is halved, the centre point is moved to the point with the
minimum distortion. Step-1 and step-2 are repeated till the step size becomes smaller than one. It is mainly
used for real time video compression with low bit rate video application such as video conferencing and
videophone.
The TSS is one of the most popular BMAs and is also recommended by RM8 of H.261 and SM3 of
MPEG owing to its simplicity and effectiveness. For a maximum displacement window of 7 i.e. d=7, the
number of checking points required is (9+8+8) =25. For a maximum displacement window of„d‟, the number
of checking points required equals to [1+8{log2(d+1)}].
Figure 1. Example of Three Step Search Path to Locate a Motion Vector at (-3, 2)
2.2. New Three Step Search (NTSS) Block Matching Algorithm
This algorithm was proposed by Renxiang Li, Bing Zeng and Ming L.Liou [2]. It is a modified
version of the three step search algorithm for searching small motion video sequences. For these video
sequences, the motion vector distribution is highly centre biased. Therefore, in addition to the original
checking points used in TSS, 8 extra neighbouring checking points of search window centre are searched in
the first step of NTSS (total 17) as shown Figure 2. There are three cases where the minimum BDM point
occurs.
a. For stationary block the minimum BDM point occurs at the search window centre then searching will stop
(This is called first step stop).
b. For quasi stationary block (small motion video sequence) the minimum BDM point occurs at any one of
the eight neighbours of the search window centre, the search in the second step will be performed only for
eight neighbouring points of the minimum BDM point and then stop the search (This is called second step
stop). Depending on position of this minimum BDM point, only five or three new checking points are

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218
required to be tested. The number of checking points required is then either (17+3) =20 or (17+5) =22.
c. If the minimum BDM point occurs at any one of the remaining eight checking points then complete three
step search will be executed. In the worst case (i.e. there is no single stationary block) the NTSS requires
33 block matches as compared to 25 matches in TSS. Eight block matches will be saved once a first step-
stop occurs. Depending on the position of the minimum BDM point (in the first step) on the 8 neighbours
of the window centre, five or three block matches will be saved once a second step-stop occurs:(1) if the
minimum is one of the four neighbouring positions along the horizontal or vertical directions, five block
matches will be saved;(2) if the minimum is one of the four neighbouring positions along the two
diagonal directions, three block matches will be saved.
The number of block matches needed in NTSS for estimating a block motion vector can be
estimated by 17P1 + 20P2 + 22P2' + 33 ( 1-P1-P2- P2'), where P1 is the probability of occurring a first step-
stop while P2 and P2' are respectively probabilities of occurring a second step-stop in the two cases
mentioned above. These probabilities are dependent on how many stationary or quasi-stationary blocks a
video frame contains.
Figure 2. Circles are the checking points in the first step of TSS, triangles are the 8 extra points added in the
first step of NTSS, and squares are the new checking points (3 or 5) in the second step depending on
minimum BDM point, in the first step, on the 8 neighbors of the window centre
2.3. Diamond Search (DS) Block Matching Algorithm
The diamond search algorithm (DS) is proposed by S. Zhu and K. K. Ma [3].It is based on MV
distribution of real world video sequences. It is an outstanding algorithm adopted by MPEG-4 verification
model (VM) due to its superiority to other methods in the class of fixed search pattern algorithms. It employs
two search patterns as shown in Figure 3, which are derived from the checking points within circle of radium
of 2 pels. The first pattern, called large diamond search pattern (LDSP) comprises nine checking points and
form a diamond shape. The second pattern consisting of five checking points forms a smaller diamond shape,
called Small diamond search pattern (SDSP).The search starts with the LDSP which is centered at the origin
of the search window, and the 9 checking points of LDSP are tested. If the minimum block distortion (MBD)
calculated is not located at the centre position, new LDSP is formed which is centered at the MBD point
found inthe search. The search procedure is repeated until MBD occurs at the centre point. At any stage if
MBD occurs at centre of LDSP, the search pattern is switched to SDSP as reaching to the final search stage.
Among the five checking points in SDSP, the position yielding the MBD provides the motion vector of the
best matching block.
(a) (b)

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Figure 3. Search Patterns of DS (a) LDSP with 9 Search Points (b) SDSP with 5 Search Points
The checking points are partially overlapped between adjacent steps; especially, when LDSP is
repeatedly used. For illustration, three cases of checking-point overlapping are presented in Figure 4. When
the previous MBD point is located at one of the corners or edge points of LDSP, only five or three new
checking points are required to be tested as shown in Figure 4(a) and (b), respectively. If the centre point of
LDSP produces the MBD, the search pattern is changed from LDSP to SDSP in the final search. In this case,
only four new points are required to be tested, as shown in Figure 4(c).
2.4. Hexagon Based Search (HS) Block Matching Algorithm
Based on an in-depth examination of the influence of search pattern on speed performance in block
motion estimation, Ce Zhu, Xiao Lin, and Lap-Pui Chau [4]. Propose an algorithm using a hexagon-based
search pattern to achieve substantial speed improvement over the DS algorithm with similar distortion
performance. The diamond shaped pattern is more efficient than square shaped search patterns, but the
diamond shape is not approximate enough to a circle, which is just 90 rotation of a square. The 8 checking
points have different distances from the centre, so that each search point cannot be equally utilized with
maximum efficiency in DS algorithm.
A hexagon-based search pattern is depicted in Figure 5(a), which consists of seven checking points
with the centre surrounded by six endpoints of the hexagon with the two edge points (up and down) being
excluded. Of the six endpoints in the hexagon, two horizontal points are away from the centre with distance 2
and the remaining four points have a distance of from the centre point, respectively. From the Figure 5(a),
we can see the six endpoints are approximately uniformly distributed around the centre, which is highly
desirable.
This algorithm uses two search patterns large HS and small HS as shown in Figure 5(a) and 5(b). In
the first step of search, the large hexagonal pattern with seven checking points is used for search. In the
second step if the MBD is found at the centre, switch to use the small hexagonal pattern, including four
checking points for the focused inner search. Otherwise, the search continues around the point with minimum
block distortion (MBD) using the same large hexagonal pattern. Note that while the large hexagonal pattern
moves along the direction of decreasing distortion, only three new non overlapped checking points will be
evaluated as candidates each time. The total number of search points per block will be
NHS (mx, my) = 7 X (3 X n) + 4. (1)
Where (mx, my) is the final motion vector found, and n is the number of executions of Step 2. In
Equation 1 the digit 7 indicates number of checking points used in step 1, the digit 3 indicates number of
checking points used in each execution ofstep 2 and the digit 4 indicates number of checking points used with
small HS as a final stage.
(a) (b)
Figure 5. Search patterns ofHS (a) Large HS (b) Small HS
2.5. Enhanced Versions of Hexagonal Search Algorithm
An Enhanced Hexagonal Search (EHS) algorithm [6] is proposed for further reduction of the search
points over Hexagonal Search algorithm. This EHS uses the 6-side-based fast inner search technique to
improve the inner search speed against the Hexagonal Search. In Hexagonal Search algorithm, all the search
points inside the large hexagon need to be evaluated, this is computationally ineffective.

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The EHS algorithm only checks the most promising inner search points by exploiting the group-sum
distortion information of the six checked endpoints of the large hexagon. At first EHS starts coarse search
with the large hexagonal pattern (Figure 5(b)) to trace a small area of optimal motion vector. After locating a
small area in the coarse search, EHS groups the six checked endpoints of the large hexagon as shown in
Figure 6(a). At each group, the group-sum distortion is calculated and then focused inner checking points
nearest to the smallest distortion group will be evaluated to obtain the minimum distortion point. Three inner
searching points nearby the smallest distortion group will be calculated in the focused inner search if the
smallest distortion group is 3 or 6. Otherwise, two inner searching points nearest to the smallest distortion
group will be used in the focused inner search.
An Enhanced Hexagonal-based Search using Point-Oriented Inner Search (EHS-POIS) is presented
in [7] to reduce the search points further. The EHS-POIS uses an efficient grouping method for the large
hexagon which is based on minimizing Mean Internal Distance (MID) for each inner point, as shown in
Figure 6(b). The eight inner points enclosed by large hexagon are divided into two sets by the MID value.
First set contains a′, c′, e′, f′, g′ and h′ points and second set includes b′ and d′ points. These points are
surrounded by two or three nearest checked large hexagon points.
The Normalized Group Distortions (NGDs) of the hexagon are calculated and the minimum NGDs
in each set are found. The two inner points related to minimum NGDs in two sets are finally evaluated to find
the final motion vector. The fine search of EHS requires 2 or 3 search points depending on the portion of
inner points with smallest group distortion whereas the fine search of EHS-POIS requires only 2 search
points constantly.
(a) (b) (c)
Figure 6. Strategies used to predict portion of inner searches in EHS, EHS-POIS and EHS-DOIS, (a) Group
oriented prediction of EHS, (b) Point-oriented prediction of EHS-POIS, (c) Direction-oriented prediction of
EHS-DOIS
An Enhanced Hexagonal-based Search using Direction-Oriented Inner Search (EHS-DOIS) [8] has
shown the considerable improvement over EHS-POIS by increasing the inner search speed double. The EHS-
DOIS forms pseudo-points prediction pattern {A′, B′, C′, D′, E′, F′, G′, H′} as shown in Figure 6(c). The
group distortions of these eight pseudo-points are calculated through six checked points of large hexagon.
Select one point in {a′, b′, c′, d′, e′, f′, g′, h′} that would be on the arrow from the center of large hexagon to
the pseudo-point with minimum distortion among eightpseudo-points. The SAD at this selected point is
finally evaluated to find the final motion vector.
3. COMPARISION
A comprehensive set of experiments have been conducted using the luminance components of the
first 100 frames of six video sequences to assess the performance and computational complexity of state-of-
the-art fast search motion estimation algorithms. These video sequences consist of different types of motion
characteristics and have various formats including QCIF, CIF, and HD. The search range is set to ±63 for
HD video sequences (Kirsten-Sara and Rocket launch sequences) and ±15 for the remaining (QCIF and CIF)
video sequences.
The experimental results are shown in terms of two testing criteria: speed and motion prediction
quality. The speed performance is shown in the Average Number of total Operations per Block (ANOB). For

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the latter, the average Peak Signal to Noise Ratio (PSNR) per frame is calculated. The size of a macroblock is
set to 16 × 16 pixels in all the fast block matching algorithms.
One problem that occurs with the TSS is that it uses a uniformly allocated checking point pattern in
the first step, which is not very efficient to search small motions appearing in stationary or quasi-stationary
blocks. To remedy this problem the NTSS employs a centre-biased checking point pattern in the first step and
halfway-stop technique. As compared to TSS, NTSS is much more robust, produces smaller motion
compensation errors, and has a very compatible computational complexity.
Although NTSS uses more checking points in its first step as compared to TSS, the first step-stop
and second step-stop can reduce computation effectively. Eight block matches will be saved once a first step-
stop occurs and five or three block matches will be saved once a second step-stop occurs. From the
experimental results conducted on Salesman and Miss America test sequences, TSS and NTSS are compared
in terms of speed-up ratios with respect to Full search, Probabilities of catching true motions, and Average
distances, as shown in Table I. For a search window of size 15x15, w=7. Full search will check
(2w+1)2=225, while TSS check (1+8 log2(w+1)) = 25, thus leading to a speed-up ratio of 9. The speed-up
ratios of NTSS with respect to full search are given in Table 1. Comparing with the results of TSS the NTSS
basically possess rather comparable computational complexity. The probability that the true motion vector is
found by NTSS and TSS are also presented in Table 1, from which it is seen that NTSS provides higher
probabilities than those of TSS. The average distances calculated in NTSS are small as compared with TSS,
this is because of centre-biased pattern is used in NTSS where as TSS employs uniformly distributed pattern.
In TSS and NTSS algorithms, square-shaped search patterns are employed, whereas the DS
algorithm adopts a diamond-shaped search pattern, which has faster processing with similar distortion in
comparison with TSS and NTSS. Compared with TSS, the DS pattern can find large motion blocks with
fewer search points and also reduce its susceptibility to getting stuck in local optima due to its relatively large
step size in horizontal and vertical directions.
Table 1. Comparison between TSS and NTSS (In terms of speed-up-ratios w.r.t.Full search, Probabilities of
catching True motions, Average distances)
The compact shape of the DS pattern around the centre also yields fewer search points than NTSS
for finding stationary or small motion vectors. The diamond pattern (large one) is so compact in terms of
distance between neighboring points that there may exist some redundancy among the search points,
especially in the beginning of lower resolution search. Consequently, such distribution of search points in DS
pattern is inefficient in finding possible candidates in the next step. The reason for this disadvantage is that
the diamond shape is not approximate enough to a circle, which is just 90 rotation of a square. The Hexagon
based search pattern is more approximated to a circle with a uniform distribution of a minimum number of
search points and each search point is equally utilized with maximum efficiency, where the redundancy
among search points is removed maximally. This HS algorithm can find a same motion vector with fewer
search points than the DS algorithm. Generally speaking, the larger the motion vector, the more search points
the HS algorithm can save. The average number of search points per block with respect to different
algorithms and different video sequences are shown in Table 2.
Table 2. The average number of search points per block with respect to block matching different algorithms
and different video sequence
Speed-up ratios Probabilities Average distances
Salesman
Miss
America
Salesman
Miss
America
Salesman
Miss
America
TSS 9.0 9.0 0.951 0.535 0.369 1.193
NTSS 10.94 7.94 0.990 0.722 0.044 0.687
Salesman
Miss
America
Tennis Mobile
Kirsten-
Sara
Rocket
launch
NTSS 18.0 21.3 22.7 28.4 23.8 24.9
DS 13.0 16.3 16.9 22.7 19.0 19.8
HS 10.7 12.8 13.0 16.3 13.9 14.8
EHS 10.3 12.2 12.8 15.8 13.3 14.1
EHS-POIS 10.1 12.0 12.3 15.3 13.0 12.8
EHS-DOIS 09.5 11.2 11.6 11.6 12.6 12.1

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The search point number per block for the FS and TSS are fixed as 255 and 25 respectively for all
video sequences. Compared with TSS, NTSS and DS search patterns theHS takes less number of search
points in an average per block. The EHS-DOIS is the fastest among the compared algorithms, andits
performance is in the most cases comparable with DSand HS.The average PSNR values per frame in all the
fast block matching algorithms are furnished in Table 3. It can be observed from Table 3 that the average
PSNRs obtained by DS are better than those of HSand enhanced versions of HSi.e., EHS, EHS-POIS and
EHS-DOIS algorithms in all the cases. The total average PSNR of the DS is better by 1.11dB when compared
to that of EHS-DOIS algorithm.
Table 3. The average PSNRs in dB for all the fastalgorithms
To comprehend the efficiency of all the fast block matching algorithms more vividly, speed and
motion prediction quality of all the fast block matching algorithms using Mobile and Rocket launch video
sequences have been plotted in Figure 7 and Figure 8 respectively. Figure 7(a) and Figure 8(a) plot a frame-
by-frame comparison of the average number of search points per block for all the fast block matching
algorithms applied to the Mobile and Rocket launch video sequences respectively. Figure 7(b) and
Figure 8(b) plot a frame-by-frame comparison of average PSNR for all the fast block matching algorithms
applied to the Mobile and Rocket launch video sequences respectively. Figure 7(a) and Figure 8(a) clearly
manifest the superiority of the EHS-DOIS against other algorithms in terms of average number of search
points, while Figure 7(b) and 8(b) show that the PSNR performance of the DS are better than that of
EHS-DOIS.
(a)
(b)
Figure 7. Performance comparison of the fast block matching algorithms for “Mobile” sequence in terms of:
(a) theaverage number of search points per block and (b) average PSNR per frame
Salesman
Miss
America
Tennis Mobile
Kirsten-
Sara
Rocket
launch
NTSS 26.52 32.32 24.22 23.32 44.13 37.43
DS 27.45 33.85 24.82 23.85 44.21 37.69
HS 26.83 32.69 24.45 23.63 44.04 37.12
EHS 26.64 32.55 24.56 23.54 43.68 36.86
EHS-POIS 26.31 32.23 24.33 23.21 43.34 36.54
EHS-DOIS 26.91 32.91 23.81 22.71 42.45 36.20

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(a)
(b)
Figure 8. Performance comparison of the fast block matching algorithms for “Rocket launch” sequence in
terms of: (a) theaverage number of search points per block and (b) average PSNR per frame
4. CONCLUSION
Motion estimation generally consumes the most computation in a video coding. In this paper, many
fast BMA algorithmsbelonging to different search patterns and search strategies are analyzed. Performance
and computational complexity of selected algorithms is compared to facilitate the choice of an appropriate
algorithm to a specific application.
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