JOURNAL OF TELECOMMUNICATIONS, VOLUME 6, ISSUE 1, DECEMBER 2010
51
Motion Estimation and H.264/AVC: A
Review
Md Anwarul Kaium Patwary and Mohamed Othman
Abstract – Image/video compression and coding technique rapidly growing to ease the visual and digital communication. The
latest coding standard is H.264/AVC which has been developed by Joint Video Team (JVT) of ISO/IEC’s MPEG and IUT-T’s
VCEG (Video Coding Expert Group). This coding standard mainly features for high coding efficiency, which may be twice faster
than the existing video coding standard for a given level of fidelity. This paper aims to present a review of recent motion
estimation application of H.264/AVC as well as issues that lies on H.264/AVC in multimedia communication and video
processing. And we analyzed the performance of several motion estimation search technique and compared.
Index terms – H.264/AVC, Motion Estimation
♦
1 INTRODUCTION
computationally expensive component for video
encoding. Motion Estimation (ME) examines the
movement of object in an image sequence to try
H
.264/AVC is a video coding standard,
which is the best state of the art in video
compression. H.264/AVC provides gains in
compression efficiency of up to 50% over a wide
range
of
bit
rates
and
video
resolutions
compared to previous standards. This standard
aims to ensure that compliant encoders and
decoders can successfully interwork with each
other,
whilst
allowing
manufacturers
the
freedom to develop competitive and innovative
products [21]. This video coding standard has
been adopted number with several technical
developments
such
as,
variable
block-size
motion compensation with small block sizes,
quarter-sample
compensation,
accuracy
motion
vector
for
motion
over
picture
boundaries, multiple reference picture motion
compensation, and directional spatial prediction
for intra coding [21].
Motion
estimation
is
one
of
the
best
————————————————
• Mohamed Othman and Md Anwarul Kaium Patwary are with the
Department of communication Technology and Network, Universiti
Putra Malaysia, 43400 UPM, Serdang D.E., Malaysia.
• The first author also an associate researcher at the Lab of
Computational Science and Informatics, Institute of Mathematical
Research (INSPEM), Universiti Putra Malaysia.
to obtain vectors representing estimated motion.
There are several studies has been done in last
couple of year to reduce the motion estimation
time
and
complexity
obtain
in
the
video
lower
computational
encoding
and
image
processing. In video coding another function is
Rate-Distortion (RD) which is to find the
distortion rate of picture quality after being
encoded. To calculate the RD, researcher use to
optimize this technique in the process of video
coding
which
is
called
Optimization (RDO).
Rate-Distortion
RDO widely used for
H.264 in mode decision and rate control,
consequently it increases the computational
complexity compared to the previous standard
[8]. In this consequence researcher are being
more interested to produce efficient and fast
RDO algorithm and parallel rate distortion as
well.
2 OVERVIEW OF H.264/AVC
ITU-T H.264/AVC is the newest entry in the
series of international video coding standard, it
was announced in 2003[1]. Currently it is most
powerful and state-of-the-art standard in video
coding
area
with
more
efficient
motion
estimation and compensation. It was developed
by Joint Video Team (JVT) with the expert from
© 2010 JOT
http://sites.google.com/site/journaloftelecommunications/
JOURNAL OF TELECOMMUNICATIONS, VOLUME 6, ISSUE 1, DECEMBER 2010
52
ITU-T’s Video Coding Expert Group (VCEG) and
sequence(pictures(slices(macrblocks(macroblock
ISO/IEC’s Moving Picture Expert Group (MPEG).
Partitions(sub-macroblock
As compare with the past standard, its design
(samples))))))) [14].
provides the most current balance between the
3 PROBLEM OVERVIEW
partitions(blocks
coding efficiency, implementation complexity
and cost, those determinations are based on VLSI
Nowadays image and video compression is
design
playing vital role in our life due to rapid
technology
(CPU’s,
DSP’s,
ASIC’s,
development of digital technique and increasing
FPGA’s etc)[14].
use of internet. Video data transmitting and
H.264/AVC is based on three redundancy
storage needs more computational power and
reduction
redundancy,
high storage capacity. In some major application
temporal redundancy, and entropy coding. In
principles:
spatial
such as wireless low power surveillance and
spatial redundancy a block in frame of a picture
multimedia
has similar pattern with its neighbors, and this
cameras and mobile camera phones, it is really a
redundancy can be reduced by predicting the
challenge
pixel values of one block from its neighboring
architecture. For both encoder and decoder, it is
blocks and compensate errors with residuals. On
necessary to have low power consumption in
the other hand temporal redundancy is reduced
order to transmit data efficiently and reduce the
by motion estimation. The motion estimation is
storage size.
sensor
for
networks,
traditional
wireless
video
PC
coding
an operation of finding a motion vector, which
indicates the similar block in reference frames,
and residuals to compensate prediction errors.
Finally the entropy coding further reduces the
encoded video size by assigning more frequently
appearing symbols to shorter codes. In the H.264
standard,
code
Context-Adaptive
(CAVLC),
Variable-Length
Context-Adaptive
Binary-
Adaptive Code (CABAC) and Exp-Golomb code
are used.
4 MOTION ESTIMATION
ASPECTS OF H.264
ON
DIFFERENT
Motion Estimation (ME) is an important part of
data compression methods adopted by all
existing video coding standard such as H.263,
MPEG-4, etc. In H.264, the ME technique is
designed with many new features, such as the
variable block size, multiple reference frames, a
quarter pixel accurate estimation, and so on. It
This coding standard structure is similar to all
gained the better coding efficiency and high
prior coding standards such as H.261, MPEG-1,
performance [7]. However, it is one of the most
MPEG-2, H.263, and MPEG-4 part 2. As it is
computationally expensive part and critical to
architecture each picture is compressed by
perform the video coding in H.264.
partitioning it as one or more slices; each slice is
consist of macrblocks, which are blocks of 16 x 16
luma
samples
with
corresponding
chroma
samples, and each macroblock is divided into
sub-macrooblock
for
motion
compensated
prediction. Those prediction partitions can have
seven different sizes 16x16, 16x8, 8x16, 8x8, 8x4,
4x8, and 4x4. The block size is used for spatial
transform is always same or smaller than block
size used for prediction. The hierarchy of video
sequences from sequence to sample is given by:
In video sequences there are high level temporal
redundancy exist between consecutive frames.
ME examine the reference frame for any
similarities to the input macroblock and reduce
temporal redundancy. Temporal reduction is to
encode first a reference frame and for the
consecutive frames encode only the difference
between the reference frame and current frame
[25]. The results of this search technique
generally one of these three: an exact match has
been found, a close match has been found, or no
match has been found. H.264 supports motion
compensation block size ranging from 16x16 to
© 2010 JOT
http://sites.google.com/site/journaloftelecommunications/
JOURNAL OF TELECOMMUNICATIONS, VOLUME 6, ISSUE 1, DECEMBER 2010
53
4x4, and the maximum number of reference
and
frame is 16 in H.264 [4]. Multiple reference
estimation and motion compensation. Finally, a
frame, variable-block size, and quarter-pixel
Direction-based Selection Rule is utilized which is
accuracy
employed
sub-pixel ME method.
standard
and
in
achieved
H.264/AVC
better
coding
it
multi-frame
motion estimation in terms of improving coding
efficiency but still computational complexity is
higher. C. Chai, H.Zeng, and S.K. Mitra [7]
proposed a fast motion estimation, this algorithm
achieves a reduction of 88% ME process time on
average which really an efficient computational
time during a video process, they reduces the
computational complexity with the consider of
improving of coding efficiency as well. In this
fast ME algorithm they used three methods
sequentially
to
implement
it.
in
motion
And there are fast algorithms have been
frames.
4.1 Multiple Reference Frame Selection
In
multiple
reference
frame
motion
compensation, the best picture in several precoded can be used to perform the ME. Its
prediction performance is better than that of
single reference in a situation such as repetitive
motion, uncovered background, non-integer
pixel displacement and lighting change. The
number of reference frame can be a maximum of
16 in the H.264/AVC encoder, but the encoder
can refer five frames for backward and forward
motion estimation.
proposed
difference
proposed in this regard of multiple reference
sub-section we will describe details of them.
researchers
little
compression
efficiency in coding process. In the following
Several
makes
First,
Mode
Discriminant method where 7 modes classified
into two categories: large block size class and
small block size class, large block size modes are
suitable for coding homogeneous regions in slow
motion, while small block size modes improve
the coding efficiency in the case of fast motion.
Second, a Condensed Hierarchical Block Matching
method is proposed which speeds up the optimal
Motion Vector (MV) search process for a given
mode, if the optimal block of an MB is M x N,
then an M x N block in the MB can be well
approximated by a condensed block, and uses
only 4 x 4 sample pixels which help to find the
best matching in a sampled searching window
In [5], fast reference frame selection
algorithm proposed based on the information
from reference frame for motion estimation
process.
They
reduced
the
computational
complexity in motion estimation process when to
predict the reference frame. They focus on the
issue of correlation on temporal redundancy in
between current Macroblock (MB) and the
reference region as well. They achieved better
speed up than the used algorithm before, they
compared with Kuo’s method [6] using three
Quantization Parameters (QP=24, 28, 32).
4.2 Search Range
Search range or window is also a key factor in
order to get more efficiency during motion
estimation. An appropriate search range can
claim the reduction of computational load and
efficient encoding scheme. Small search window
produces poor matching results; in the other
hand large search range produces the precise
matching result than small. Recently many fast
motion estimation algorithms have been
proposed to accelerate the motion estimation
time by reducing the search range rather than
full search range. Besides this algorithm there is
another study has been done in [4], they
proposed a fast multi-frame motion estimation
algorithm to gain better rat-distortion and lower
complexity for encoding in H.264. They used
several search method rather than only full
search
technique
to
reduce
searching
computation in motion estimation process with
five reference frames. In H.264/AVC video
encoding the search range is fixed throughout
the encoding process. In some cases we need to
change the search range such as, for the slow
motion macroblocks, because the distance
between best MVs and search center is very
small, so the search range is inefficient in this
© 2010 JOT
http://sites.google.com/site/journaloftelecommunications/
JOURNAL OF TELECOMMUNICATIONS, VOLUME 6, ISSUE 1, DECEMBER 2010
54
case. But for the large motion MBs a large search
range is more efficient. Thus, we can reduce the
computational complexity of the ME process by
changing adaptively the search range. The sum
of absolute value of previously computed motion
vectors and summation of prediction errors are
used to change the search range adaptively [13].
In [9], an adaptive search range decision
algorithm is proposed which has a search area
selection method by utilizing the information of
the
previously
computed
motion
vector
differences (MVDs). It determines the size of
search range dynamically and improves coding
efficiency and picture quality as well.
8 x 8 Block
8 x 4 Block
4 x 8 Block
4 x 4 Block
Fig 1 (b): Macroblock Sub-partitions
In a full search based VBS selection all allowed
modes are checked for the current macroblock.
Cost is calculated at each location in a search
window in the previous frame using all block
sizes. In [11] N.A. Khan, S. Masud, and A.
Ahmad proposed a variable block size motion
estimation algorithm which is implemented in
real time video encoding for H.264/AVC codec
standard. In this algorithm they proposed 3D
recursive search approach, while selecting the
best mode and motion vector for encoding the
4.3 Variable Block Size Motion Estimation
(VBSME)
VBSME is a new feature introduced in
H.264/AVC video coding standard. It plays an
important role in achieving efficient coding and
video quality in video compression process. But
it has high computational complexity and huge
memory traffic make design difficult. The
processing power requirement for VBSME
depends on many factors such as frame size,
frame rate and search area. In order to gain more
precise motion information; H.264/AVC allows a
macroblock into several blocks with variable
size, ranging from 16 pixels to 4 pixels in each
dimension [9]. This variable block size allows the
combination of block sizes ranging from 16 x 16
pixels to 4 x 4 pixels within a macroblock for
luminance component and correspondingly
blocks of quarter sizes chrominance component.
Luminance component can be portioned in one
of the four modes as shown in Fig 1 (a), if 8 x 8
block size is chosen then each of the four 8 x 8
may be further split into four more modes as
shown in Fig 1(b).
current macroblock during video process. For
any real time application must be reduced the
computational complexity in searching the best
mode of macroblock. This [11] algorithm uses the
‘previous mode’ information and the ‘previous
motion vector’ information in the current and
previous frames to determine the motion vector
by using SAD or other criteria and mode for the
current macroblock. Thus considerably achieved
reduction in computational complexity and
decreases the bit stream size by restricting the
number of candidate macroblocks.
In an H.264 encoder, the most time consuming
component
is
variable
block
size
motion
estimation. To reduce the complexity and
computational time another technique is early
termination of motion estimation during video
process. Early termination happens when it
predicts the best motion vector by examining
only one search point. In this process some of the
motion searches can be stopped early and large
number of search point can be skipped. The
objective of the early termination is to decide
whether a search point has met RD cost criterion,
so that the best search point for the current block
can be found early [12]. In [12], an adaptive
variable block size early termination motion
16 x 16 Block
16 x 8 Block
8 x 16 Block
Fig 1 (a): Macroblock Partitions
8 x 8 Block
estimation algorithm is proposed for H.264/AVC
coding standard. This algorithm has adaptive
threshold
which
characteristics
regarding
of
current
is
based
on
rate-distortion
block
© 2010 JOT
http://sites.google.com/site/journaloftelecommunications/
and
statistical
(RD)
cost
previously
JOURNAL OF TELECOMMUNICATIONS, VOLUME 6, ISSUE 1, DECEMBER 2010
55
processed blocks and modes. By using this
method. FS has high reliability and efficiency in
method motion searches can be stopped early
video coding for H.264/AVC standard. High
and saves large number of search point. This
accuracy evolved in this search algorithm cause
proposed method saves about 93% and 69% of
its traverse all possible search points in a search
search points compared to H.264/AVCs typical
window.
full
search
and
fast
motion
estimations,
However this method left some drawback such
respectively.
as, computational time due to the fact it needs to
4.4 Quarter Pixel Accurate Estimation
H.264/AVC allows quarter pixel (1/4) motion
estimation while searching MV and gets pixel
accuracy after encoding process. In H.264 ME is
more encouraged to have the quarter pixel
accuracy to achieve the better compression
efficiency, but it takes significant amount of time
in motion searches process. With full search for
integer-pel motion estimation, the portion of subpixel motion estimation is about 20%. Fast full
search motion estimation for integer-pel motion
estimation, the portion of sub-pixel motion
estimation increases of about 40% of ME process
[22]. However, it gets more accuracy than
integer-pel motion estimation. In H.264 ME
process starts with each block in the location of
integer-pixel first, and then sub-pixel around the
best integer-pixel position, finally the quarter
pixel around the best sub-pixel position [23].
Thus, it does find the best matching point of
Motion Vector (MV). In [22], proposed a
mathematical model for quarter-pixel motion
estimation. This proposed algorithm achieved
almost the same motion estimation accuracy
same as integer-pel motion estimation for full
search algorithm.
5
TYPICAL
ALGORITHMS
M OTION
ESTIMATION
search all possible search point in a search
window. It takes long time and a very large
computation.
5.2 Enhanced Predictive Zonal Search
(EPZS)
EPZS is a predictive motion estimating
algorithm. Compared with the FS method,
improvement feature is selection of motion
vector (MV) prediction. It consists of 3 steps: 1)
predictor selection, 2) selects the best Motion
Vector (MV) predictor from a set of potentiality
likely predictors, 3) early termination allows the
termination of search at certain stage when the
criteria satisfies.
Predictor selection is most important feature in
EPZS process. Examine only a smaller set of
highly likely MV predictors instead of checking
all possible search point in a given search area
[19]. Designing of this set and select the correct
MV predictor are vital to the performance of this
search algorithm in terms of both compression
efficiency
and
complexity.
Based
on
the
distortion of adjacent blocks several early
terminations are examined in this method. And
this allowed us reduce the computational
complexity notably.
The best predictor is chosen by minimizing the
Some typical ME algorithm for H.264/AVC is
exist based on several research. In the next subsection few typical motion estimation algorithm
will be described and simulation result will be
shown and compared them.
cost function in equation 1. EPZS algorithm finds
the smaller distortion by comparing with the
threshold criterion.
, , . (1)
5.1 Full- Search (FS) Method
Block matching motion estimation with the
highest precision belongs to Full- search
algorithm also known as, exhaustive search
Where m= , is the current MV being
considered, p , is the MV used as the
prediction during the MV coding process, and
© 2010 JOT
http://sites.google.com/site/journaloftelecommunications/
JOURNAL OF TELECOMMUNICATIONS, VOLUME 6, ISSUE 1, DECEMBER 2010
56
a Lagragian multiplier. The rate term R
(m-p) represents the motion rate.
5.3 Unsymmetrical-cross Mutli-Hexagongrid Search (UMHexagonS)
UMHexagonS algorithm uses the hybrid and
hierarchical motion search strategies. It includes
four steps and consists of several search patterns;
they are cross pattern, multi-hexagon-grid, 24
adjacent points, classical hexagon pattern and
small diamond pattern. In this regard the
algorithm called hybrid motion search algorithm.
Fig. 2: UMHexagonS Search Step [20]
UMHexagonS
is
a
block-matching
motion
estimation technique. And estimate the initial
motion vector by using four models obtained.
They are, vector forecast value, upper forecast
value, relevant piece of vector forecast and
reference block vector forecast. The algorithm
second step is unsymmetrical-cross search is
performed to locate the search location with
minimum cost function value. As shown in fig. 2
third step of this algorithm is uneven multiTable 1
Performance Comparisons of several Search Algorithm
Video Sequences
ME Algorithm
ME Time(sec)
PSNR Y (db)
Total encoding time
(sec)
akiyo
bridge
foreman
carphone
FS Method
7.793
39.521
9.428
UMHexagonS [20]
1.203
39.521
2.920
EPZS [19]
0.899
39.521
2.419
FS Method
10.208
35.612
11.954
UMHexagonS [20]
1.242
35.604
3.003
EPZS [19]
0.999
35.607
2.710
FS Method
10.850
37.118
12.743
UMHexagonS [20]
2.0591
37.137
3.903
EPZS [19]
1.672
37.240
3.498
FS Method
11.349
37.291
13.560
UMHexagonS [20]
1.715
37.429
3.536
EPZS [19]
1.431
37.363
3.224
© 2010 JOT
http://sites.google.com/site/journaloftelecommunications/
JOURNAL OF TELECOMMUNICATIONS, VOLUME 6, ISSUE 1, DECEMBER 2010
57
hexagon-grid search is used and it starts from the
in the parameter of ME time, PSNR, and Total
center and traverse through the search window
encoding time. The experiment environment is:
until the search window boundary. The fourth
Windows 7 Home Premium, JM 16.0 reference
and final step uses an expanded hexagon and a
software [26] for H.264/AVC encoding, Microsoft
small search pattern to find the minimum cost
Visual Studio C++ 2010 Express Edition. We used
function value, which as the current block
several QCIF (Quarter Common Intermediate
motion vector position.
Format) video sequences (i.e. akiyo, bridge,
foreman, carphone) to test the algorithm.
Table 2
Reviewed Studies
Proposed Technique
H.264 Features
Findings
Variable Block-Size
Adaptive variable block-based
A region based search is suggested to reduce
Motion Estimation
early termination motion
the ME time
(VBSME)
estimation algorithm [12]
Variable block size motion
Fine search technique is used to utilize the
estimation algorithm for real time
best mode and best motion vector at first
video encoding [11]
stage
Multiple Reference
Fast Multi-frame motion
According to the estimated parameter, one
Frames
estimation [4]
can decide which kind of search method is
suitable for the previous nth frame to
optimize speed and accuracy
Fast motion estimation for H.264
Probability of using large block size modes is
[7]
higher than that of using the small ones
Efficient reference frame selector
All subblocks inside the same
for H.264 [6]
submacroblock partition must refer to the
same reference frame
Search Range
Adaptive search range decision
High probability of large Motion vector
algorithm [9]
(MV) does not mean a large search window
is required
Quarter Pixel
Mathematical model for a quarter
Quadratic Motion Compensation (MC)
Accuracy
pixel motion estimation[22]
prediction errors around an integer-pixel
motion vector, and appraise the
corresponding MC prediction error at subpixel positions.
5.4 Performance Comparisons of Different
Search Techniques
In this section several search method is inspected
in terms of ME time, total encoding time, and
Peak Signal-to-Noise Ratio (PSNR). Table 1
shows the different search method performance
We observed three different popular search
techniques and analyzed through the total
encoding time, ME time and PSNR for Y
component in a video sequence. Full Search
Method is containing highest computational
complexity in every video sequences and picture
© 2010 JOT
http://sites.google.com/site/journaloftelecommunications/
JOURNAL OF TELECOMMUNICATIONS, VOLUME 6, ISSUE 1, DECEMBER 2010
58
quality remain same in every search technique.
[5]
And it is highly notified in table 1 that EPZS
J.Gwanggil,
Reference
Frame
H.264/AVC,”
algorithm is claiming as the most efficient
algorithm regarding on ME time and encoding
L.Kangjun,
IEEE
and
J.Jechang,
Selection
Algorithm
Transaction
on
“Fast
for
Consumer
Electronics, Vol. 55, No. 2, May 2009.
[6]
time but the picture quality remain nearly same.
T.Y. Kuo, and H.J. Lu, “Efficient reference frame
selector for H.264,” IEEE Transaction on Circuits
Systems for Video Technology, vol, 18. no. 3, pp. 400-
6 FINDINGS
405, March 2008.
In table 2 shows the summarization of the review
[7]
estimation for H.264,” Signal Processing: Image
of H.264/AVC and some proposed technique in
terms aspect of H.264/AVC features.
C. Chai, H. Zeng, and S. K. Mitra, “Fast motion
Communication 24 (2009) pp 630-636.
[8]
K. Y. Liao, J. F. Yang, and M. T. Sun, “Rate-Distortion
Cost Estimation for H.264/AVC”, IEEE Transaction on
7 CONCLUSIONS
Circuits Systems for Video Technology, vol. 20, no. 1, pp
In this paper we reviewed the motion estimation
and its trend of progress on aspects of
H.264/AVC coding standard. H.264/AVC has
tremendous feature in terms of coding efficiency
and accuracy. But it left huge computational
complexity in motion estimation process. Plenty
of techniques and algorithms have been
proposed in the term of software and hardware
to reduce computational load in video/image
process. And researchers are gaining much more
efficient results over typical motion estimation
technique day by day.
38-49, January 2010.
[9]
M. G. Sarwar, and Q. M. Jonathan Wu, “An Efficient
Search
Algorithm for Motion
Circuits, System and Signal Processing, issue 4, volume
3, pp 173-180, 2009.
[10] C.Y. Kao, and Y.L. Lin, “A Memory-Efficient and
Highly Parallel Architecture for Variable Block Size
Integer Motion Estimation in H.264/AVC,” IEEE
Transactions on Very Large Scale Integration (VLSI)
Systems, Vol. 18, No.16, pp 866-874, June 2010.
[11] N.A. Khan, S. Masud, and A. Ahmad, “A variable
block size motion estimation algorithm for real-time
video
ACKNOWLEDGMENT
Range Decision
Estimation in H.264/AVC,” International Journal of
encoding,”
Signal
Processing:
Image
Communication, Vol. 21, pp 306-315, 2006.
The research was supported by the Research
[12] M. G. Sarwar, and Q. M. Jonathan Wu, “Adaptive
Variable
Block-Size
Early
Motion
Estimation
University Grant Scheme (RUGS), RUGS No. 05-
Termination Algorithm for H.264/AVC Video Coding
03-10-1039RU.
Standard,” IEEE Transactions on Circuits and Systems
REFERENCES
August 2009.
for Video Technology, Vol. 19, No.18, pp 1196-1201,
[13] T. Yamada, , “Fast and accurate motion estimation
[1]
T. Wiegand, G. J. Sullivan, G. Bijontegaard and A.
Luthra, “Overview of the H.264/AVC Video Coding
[2]
[14] G. J. Sullivan, P. Topiwala†, and A. Luthra, ”The
AND SYSTEMS FOR VIDEO TECNOLOGY, VOL. 13,
H.264/AVC Advanced Video Coding Standard:
NO.7, PP 560 – 576, JULY 2003.
Overview and Introduction to the Fidelity Range
M. Svetislav and S. Leonel, “A Parallel Algorithm for
Extensions,” SPIE Conference on Applications of Digital
Architectures,” International Conference on Complex,
Image Processing. August,2004.
[15] D. Krik and W. Hwu, Textbook for UIUC ECE 498 AL :
Programming Massively Parallel Processors. Draft, 2009.
Intelligent and Software Intensive System
C. Ngai-Man, C. Au Oscar, K. Man-Cheung, H.W.
Wong Peter, and L. Chun Hung, “Highly Parllel
Rate-Distortion optimized Intra-Mode Decision on
[16] NVIDIA, “CUDA programming guide,” NVIDIA,
Tech. Rep., 2009.
[17] C.-W. Ho, O. Au, G. Chan, S. –K. Yip, and H. –M.
IEEE
Wong, “Motion estimation for H.264/AVC using
TRANSACTIONS ON CICUITS AND SYSTEMS FOR
programmable graphics hardware,” in Proc. IEEE
Multicore
Graphics
Processors,”
VIDEO TECHNOLOGY, VOL. 19, NO,11 pp 16921703,
[4]
Proc. ICASSP, vol.2, pp. 897-900, March-2005.
Standard,” IEEE TRANSACTIONS ON CIRCUITS
Advanced Video Motion Estimation on Multicore
[3]
algorithm by adaptive search range shape selection,”
International conference on Multimedia and Expo, 2006.
[18] M. C. Kung, O. C. Au, P. H. W. Wong, C. H. Liu,
NOVEMBER 2009.
H. Shih-Chang, and H. Yu-Chun, “Fast Multi-frame
“Block based parallel motion estimation using
motion estimation for H.264/AVC system,” Signal,
programmable
Image and Video Processing, Vol. 4, No.2, 2010.
International Conference on Audio, Language and Image
graphics
Processing, 2008.
© 2010 JOT
http://sites.google.com/site/journaloftelecommunications/
hardware,”
in
Proc.
JOURNAL OF TELECOMMUNICATIONS, VOLUME 6, ISSUE 1, DECEMBER 2010
59
[19] T. H. Y. Cheong, and T. A. Michael, “Fast Motion
Prior to that he was a Deputy Director of the Information
Estimation within the H.264 codec,” in Proc.
Development and Communication Center (iDEC) where
International Conference on Multimedia and Expo
he was in charge of the UMPNet network campus, the
(ICME’03), vol. 3, pp. 517-520. Baltimore MD, 2003.
uSport
Wireless
Communication
project,
High
[20] Fast integer pel and fractional pel motion estimation
Performance Enterprise Servers and the UPM Data Center.
for AVC. In: Joint Video Team (JVT) of ISO/IEC
He received his Ph.D. from the National University of
MPEG and ITU-T VCEG, JVT-F016, December, 2002.
Malaysia with distinction (Best Ph.D. Thesis in 2000).
[21] I.E.G.
Richardson,
Compression:
Video
H.264
and
Coding
Video
Between 2002 and 2009, he received many gold and silver
Next-Generation
medal awards for University Research and Development
MPEG-4
for
Multimedia. New York: Wiley, 2003.
Exhibitions and Malaysia Technologies Exhibition which
[22] J.W.Suh, J. Chao, and J. Jeong, “Model-based quarter-
is at the national level. His main research interests are in
pixel motion estimation with low computational
the fields of parallel and distributed algorithms, high-
complexity,” IEEE Electronics Letter, Vol. 45, No. 12,
speed networking, network design and management
pp 618-620, 4th June 2009.
(network security, wireless and traffic monitoring) and
[23] Z.
Hongliu,
W.
Rangding,
and
X.
Dawen,
scientific computing. He is a member of the IEEE
“Information Hiding Algorithm for H.264 Based on
Computer Society, the Malaysian National Computer
the motion estimation of quarter-pixel,” in Proc. IEEE
Confederation, and the Malaysian Mathematical Society.
International Conference on Future Computer and
He has published articles in 120 national and international
Communication (ICFCC), 2010.
journals and more than 200 conference proceedings
[24] Z. Ying, S. Wan-Chi, and S. Tingzhi, “Fast sub-pixel
papers. He is also an associate researcher and coordinator
motion estimation based on directional information
of
and adaptive block classification,” in Proc. IEEE 5th
Computational Science and Informatics, Institute of
International
Mathematical
Conference
on
Visual
Information
High
Speed
Machine
Science
[25] P.C. Shenolikar and S.P. Narote, “Different Motion
Estimation Approach,” in Proc. IEEE Internationa
National
Conference
on
Control,
Automation,
Communication and Energy Conservation, 4th-6th June
2009.
[26] H.264/AVC
Reference
Software.
Website:
http://iphome.hhi.de/suehring/tml/
Md Anwarul Kaium Patwary was born in Bangladesh. He
received his B.Sc in Software Engineering from Universiti
Utara Malaysia, 2009. Currently he is pursuing M.Sc in
Computer
Science
(by
research) at Department of
Communication
Technology
and
Faculty
Network,
Computer
Science
Information
of
and
Technology,
Universiti Putra Malaysia. His
research interests are in video
coding
and
compression,
parallel image and video processing, signal processing for
communication, parallel and GPU programming.
Mohamed Othman is a Professor at the Departmental of
Communication Technology and Network, and a Deputy
Dean (Research, Graduate Studies and Finance) of Faculty
of
Computer
Science
Information
Universiti
and
Technology,
Putra
the
(INSPEM),
Malaysia.
Engineering, pp 622 – 627, 2008.
at
Malaysia.
© 2010 JOT
http://sites.google.com/site/journaloftelecommunications/
Laboratory
Universiti
of
Putra
JOURNAL OF TELECOMMUNICATIONS, VOLUME 6, ISSUE 1, DECEMBER 2010
60
© 2010 JOT
http://sites.google.com/site/journaloftelecommunications/