Unidirectional Motion Estimation Technique With Full Search Algorithm For Frame Rate Up- Conversion Video.

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KEYNOTE Impact of Electromagnetic Fields on Current Ratings
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Design of Snubber Circuit and PI Control to Achieve Load Independent Output
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Didi Istardi, Andy Triwinarko

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Economic Cost Study of Photovoltaic Solar System for Hotel in Nusa
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I.A.D. Giriantari, I.N.S. Kumara, D.A. Santiari

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Elimination of Sympathetic Inrush Currents in Transformers Connected in
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Kartik P Basu, Naeem M Hanoon

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Investigation and Modelling of Sympathetic Inrush Due to Transformer
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H. Abdull Halim, B.T. Phung , J. Fletcher

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Load Flow and Supply Security Analysis of Power System in Tiga Nusa;
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The Design of Web Based Academic Information System in Universitas Sains
dan Teknologi Jayapura
Marla S.S. Pieter, Evanita V. Manullang

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Implementation Dedicated Sensing Receiver (DSR) in 3G - WiFi Offload
Setiyo Budiyanto, Muhammad Asvial, Dadang Gunawan

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Website Content Management Analysis of eGovernment in Bali Province
According to the Ministry of Communications and Information Guide (KOMINFO)
Gerson Feoh, Linawati, Ni Made Ary Esta Dewi Wirastuti

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Server Log Monitoring Based On Running Services
Dandy Pramana Hostiadi, Made Sudarma, I.B Alit Swamardika

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Performance Analysis of Packet Scheduling Algorithm for Video Service in
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Ida Nurcahyani, I Wayan Mustika, and Selo

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Study on Feasible Solution of Power Control in Cognitive Radio Networks
Norma Amalia, I Wayan Mustika, and Selo

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Visualization of Indonesian Translation of Quran Index
Suwanto Raharjo, Khabib Mustofa

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A Review : Identification of Avian Influenza Environmental Risk Factor using
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M. A. Padmasari, Linawati, N.M.A.E.D. Wirastuti

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Modelling and Numerical Simulation of Multiple One Way Gears Wave Energy
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Masjono, Salama Manjang, Zahir Zainuddin, Arsyad Taha

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ID 61

Comparison between RC Detector and High Frequency Current Transformer in
Measuring Partial Discharge in Liquid Insulation
Suwarno, Aulia

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Unidirectional Motion Estimation Technique With Full Search Algorithm For Frame
Rate Up-Conversion Video
I Made Oka Widyantara, Ni Putu Widya Yuniari, Linawati

ID 63

Modeling and Detection of High Impedance Faults
Huwei Wu, B.T. Phung, Daming Zhang, Jichao Chen

ID 75

Effect of the Distance of Partial Discharge Sensor to Partial Discharge Source on
the Attenuation of Partial Discharge induced Electromagnetic Wave in Gas Insulated
Switchgear
Umar Khayam, Zahrina Hafizhah

ID 77

New Designed Bowtie Antenna with Middle Sliced Modification as UHF Sensor for

Partial Discharge Measurement
Asep Andi Suryandi, Umar Khayam

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Demand and Thermal Aware Approach for Greener IaaS-Cloud Data-Centers
Nazi Tabatabaei Yazdi, Chan Huah Yong

ID 85

DC Motor Speed Control with Pulse Width Modulation (PWM) Method of Infrared
Remote Control Base on Microcontroller ATmega16
Putu Raka Agung, Syamsul Huda, I Wayan Arta Wijaya

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Electricity
Satya Kumara, D.P.D. Suparyawan, W.G Ariastina, W. Sukerayasa, I.A.D Giriantari

ORGANIZING COMMITTEE
General Chair:
Ida Ayu Dwi Giriantari




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Co-Chair:
W. G. Ariastina

General Secretary:
N. M. A. E. Dewi Wirastuti



Publication:
I M. Arsa Suyadnya

Secretariat:
I. B. Alit Swamardika
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 N. Budiastra
 I W. Sukerayasa

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 I N. Setiawan

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TECHNICAL PROGRAM COMMITTEE


Linawati, Ph.D (Indonesia – Chair)

 Prof. A Min Tjoa (Austria)
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 Prof. Suwarno (Indonesia)
 Dr. Tumiran (Indonesia)
 Dr. Lukito Edi Nugroho (Indonesia)
 Prof. Rudy Setiabudi (Indonesia)
 Prof. Dadang Gunawan (Indonesia)
 Dr. Hermawan (Indonesia)
 Prof. Gamantyo Hendrantoro (Indonesia)

 Prof. Rukmi Sari Hartati (Indonesia)
 Prof. Suprapta Winaya (Indonesia)
 Prof. M. Ashari (Indonesia)
 Dr. Asvial (Indonesia)
 Dr. I Wayan Mustika (Indonesia)
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ISBN : 978-1-4799-6126-9

Bali, 5 - 7 November 2014

ICSGTEIS 2014

Unidirectional Motion Estimation Technique With

Full Search Algorithm For Frame Rate UpConversion Video
I Made Oka Widyantara, Ni Putu Widya Yuniari, Linawati
Department Of Electrical Engineering
Udayana University
Denpasar-Bali, Indonesia
oka.widyantara@unud.ac.id, widyayuniari2010@gmail@com, linawati@unud.ac.id
technique which is adapted is called Frame Rate Up
Conversion Video (FRUC). FRUC is a technique inserting a
new frame (frame intermediate) into a sequences original
video to increase frame rate. This application is used in
processing video signal in order to overcome limitation of
transmission bandwidth and to increase temporal resolution.
FRUC algorithm applies Motion Compensation Interpolation
(MCI) method. MCI technique relies on a process which is
known Motion Estimation (ME). ME process for MCI
algorithm on FRUC is aiming to predict frame position as
motion vector (mv) which is nowadays is relying on reference
frame. The obtaining mv will be stored in decoder as an
information used for generate frame intermediate.
There is ME algorithm which is implemented to FRUC.
This ME method is done only on the based on backward
direction [1-3] which is uses a previous frame as a reference
frame by copying mv from previous frame to become mv for
frame intermediate. However, this method produces poor
video motion. In order to overcome this sort coming a
technique which is implemented linear combination technique
that is reference frame application on both side that is
backward and forward directions to give inform in inserting
frame intermediate.
Choi et al [4] purposed MCI method using Bilateral ME
technique. However this technique has a high complexity. To
reduce this complexity, Zhang et al [5] has purposed
Unidirectional method for FRUC algorithm. Algorithm of [13], [4-5] uses motion estimation with full search method with
one pixel accuracy which is known as Exhaustive Search (ES)
by adopting block based video coding. ES will search block
candidate on every block position in reference frame by
determining searching window[6]. Many researches has been
done in order to obtain the most accurate frame intermediate
with equality similar to original frame. There is searching
method which is development of Exhaustive Search method
that is a Full Search with a half pixel accuracy. This paper
purpose an application of motion estimation technique with
Full Search on FRUC algorithm. The target is to produce
frame intermediate which is inserted on decoder in order to get
the most accurate frame from the original video sequence.

Abstract— FRUC is a technique of inserting a new
frame (frame intermediate) into a sequence of original video
aiming at increasing frame rate. This technique is used in
the process on video signal to overcome the limitation
bandwidth transmission and temporal resolution. FRUC
algorithm applies Motion Compensation Interpolation (MCI)
technique. MCI technique relies on a process which is
known Motion Estimation. Motion Estimation process is
implemented by using a method searching. This technique is
aim to get frame intermediate on FRUC algorithm as
accurate as possible with low complexity. This research also
aim to realize the FRUC algorithm with the application of
Unidirectional motion estimation technique by the method of
Full Search.
This research is conducted to obtain the
performance of Unidirectional motion estimation technique
with Full Search method in inserting frame intermediate.
Experiment and analysis is implemented with frame rate
ratio 1:2. Full Search method consist of two methods, there
is Exhaustive Search and Half Pixel.
By comparing complexity Full Search on search area ±7
and ±15 pixel based on computation time it is revealed that
Exhaustive Search method has lower complexity compare to
Half Pixel method for insertion frame intermediate on
FRUC algorithm.

Keywords: FRUC, Motion Compensation Interpolation, Motion
Estimation, Full Search Method

I.

INTRODUCTION

Multimedia service which is adapting digital based
technology video has become a daily human needs. Many
digital technology devices which support different format and
usually needed to convert a format and frame rate. For
instance film which is produce in 24 frame per second (fps)
but on TV is displayed with 30 fps. There will be motion and
voices that is well visually perceived if displayed 30 fps. The

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II.

Bali, 5 - 7 November 2014

REVIEW

ICSGTEIS 2014

C. FULL SEARCH METHOD
Full Search involves finding a best match for a reference
block against all possible block locations within a specified
search window. In this research there is two FS methods, can
be defined as Exhaustive Search and Half Pixel.

A. FRAME RATE UP CONVERSION VIDEO (FRUC)
Increasing the frame rate, i.e. up-conversion, is done to
enhance the visual experience of video with low frame rate.
Video at 30 frames per second (fps) is clearly smoother than
video at 20 FPS, especially for video with much motion. Upconversion is performed by adding new frames to a video
from existing ones, as figure 1 illustrates.

1.

EXHAUSTIVE SEARCH METHOD
Exhaustive Search by adopting block based video coding.
ES will search block candidate on every block position in
reference frame by determining searching window[6]. In this
research location of searching for Exhaustive Search can be
processed by raster order technique,as figure 2 illustrates.

Figure 2 Exhaustive Search Method With Raster Order[6]
Figure 1 Mechanism Of FRUC [6]

ES with raster order technique can be defined as
1. Limit of search area is – p until p within search range
area.
2. Find location with one pixel step size as figure 2.
3. Find the lowest value of Mean Absolute Difference
(MAD) from all location of search range area.

B. MOTION COMPENSATION INTERPOLATION FOR
FRUC
FRUC method with MCI technique uses two consecutive
decoded frame as reference. In this research use MCI
technique with an Unidirectional ME algorithm. Consider two
consecutive decoded frames ft-1 dan ft+1 Let ft be the
intermediate frame, i.e., the frame to be interpolated. It can be
modeled as
ft(x,y)=

2.

HALF PIXEL SEARCH METHOD
Half Pixel method can be implemented with calculate a
half intensity value from mean value of integer pixel with
close position. Half Pixel model inserts new candidate from
Exhaustive Search process, as figure 3 illustrates.

1
1
1
1
1
f t −1 (x - Δx, y- Δy) + ft+1(x + Δx, y + Δy) +
2
2
2
2
2

ηt(x,y)
(1)
Where (x, y) and (Δx,Δy) are pixel location and
unidirectional mv, respectively. In this research to get Δx and
Δy based on Full Search Method.
The best of mv for generate frame intermédiate is the
lowest mean value from substract forward and backward side.
Therefore
(2)
Δv = (Δx,Δy) = arg min ρ(ft-1, ft+1; Δx, Δy)

2

3

B

D

Ket :
: Position of
integer pixel
: Position of
half pixel

Figure 3 Model Of Half Pixel Method [7]

Based on figure 3, to get value of frame position with half
pixel can be defined as
1 = (A+B+1) / 2
2= (A+C+1)/ 2
(5)
3= (A+B+C+D+1) / 4

Δx , Δy

x , y∈G

x , y∈G

1

C

where ρ() is a proper distance function. For unidirectional ME,
such as forward prediction, ρ() can be defined as
(3)
ρ(ft-1, ft+1; Δx, Δy) = Σ | ft-1(x,y)- ft+1(x+Δx, y+Δy)|
ρ() for backward prediction can be defined as
ρ(ft-1, ft+1; Δx, Δy) = Σ | ft+1(x,y) - ft-1(x+Δx, y+Δy)|

A

(4)

Where G is a pixel group, usually an image block.

III. IMPLEMENTATION
FRUC mechanism in this research is implemented
seen in figure 4 below with unidirectional ME by
implementing Exhaustive Search which is compared with Half

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ICSGTEIS 2014

Pixel search method. Therefor accuracy is known from its
method in frame intermediate insertion.
(a)

(b)

Figure 5 Example Frame Of Video Sequence
(a)Foreman dan (b)Hall Monitor

•
•

•
Figure 4 Model Implementation of FRUC [6]

B. COMPARISON SCHENARIO OF COMPENSATION
TIME
Compensation time is a period needed by search method to
get the best vector motion candidate in order to have a finished
frame reconstruction process. This compensation time is
affected by candidate search mv computations. The more
computation is perform the more time is needed with shows
the complexity in inserting frame intermediate. A search
method is consider efficient when it’s able to preserve
compensate time which is needed. In order to get efficient
compensation time the research uses equation 9 below[8].

Quality frame intermediate which is produced from
FRUC algorithm with Unidirectional ME that uses Exhaustive
Search and Half Pixel will be analyzed based on distorsion of
frame can be defined as Peak To Peak Signal To Noise Ratio
(PSNR). PSNR is an objective measurement often used an a
relative measurement of a frame estimation to reference frame.
In this study a frame resulted from estimation in decoder on
FRUC algorithm by Exhaustive Search method and Half Pixel
method is compared sequence frame with the frame from
original video sequence. PSNR is formulated is below.
⎡
⎢
PSNR = 10 log 10 ⎢
⎢1
⎢N
⎣

Efficiency = (1 −

⎤
⎥
255 2
⎥
∑i ∑j (Yref (i, j ) − Y prc (i, j ) )2 ⎥⎥
⎦

175 143
1
∑
∑ X ( i, j ) − Xˆ (i, j )
176 × 144 i = 0 i = 0

{

t ra
) *100%
t rb

(8)

Where:
tra = mean of compensation time search method which is
purposed
trb= mean of compensation time search comparing method

(6)
QCIF with 176 × 144 pixel format of frame video is
used,therefor MSE can be defined as.
MSE =

Number of frame for each sequence: Foreman video test =
400 frame and Hall Monitor video test = 300 frame.
Spatial dan temporal : Format test video used QCIF (176
x 144 piksel) with frame rate 30 fps. Comparison number
of frame which is used on FRUC 1:2.
Block size : 8 x 8 pixel with search range ±7 and ±15
pixel by vertical and horizontal.

2

}

(7)

IV. RESULTS AND DISCUSSION

Where X(i,j) is a pixel coordinate within frame from original

A. PSNR ANALYSIS METHOD OF FULL SEARCH
In figure 6 and 7 shows trace PSNR of Exhaustive Search
and Half Pixel on search area ±7 and ±15 pixel by using
Foreman video test.

sequence and Xˆ (i , j ) is a pixel coordinate within frame
intermediate.
A. COMPARISON SCHENARIO OF FRAME DISTORSION
Performance method of Exhaustive Search and Half Pixel
in inserting frame intermediate will be analyzed by evaluating
PSNR output and compare with PSNR the original frame
which is assumed store in buffer and finally the performance
is known. Distorsion frame is calculated from PSNR measure
based on equation 6. The test condition is applied in this
result.
• Video test: Foreman and Hall Monitor. This sequences
showed different type on show content. That is high
movement (Foreman) and slow movement (Hall Monitor).

Figure 6 Comparison PSNR Of Exhaustive Search and Half Pixel On
Search Area ±7 Pixel By Using Foreman Video Test.

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Figure 7 Comparison PSNR Of Exhaustive Search and Half Pixel On
Search Area ±15 Pixel By Using Foreman Video Test

ICSGTEIS 2014

Figure 9 Comparison PSNR Of Exhaustive Search and Half Pixel On
Search Area ±15 Pixel By Using Hall Monitor Video Test.

Based on PSNR trace in figure 6 and 7 it can be showed
that trend from Full Search method is fluctuated. It because of
many movement produced by Foreman video characteristic. In
general based on mean PSNR value resulted from foreman
video test, it known that performance of Half Pixel search
method is better in inserting frame intermediate in comparison
to Exhaustive Search. This result came from mean PSNR
value for searching area ±7 and ±15 from Half Pixel search is
higher than Exhaustive Search (0,13 and 0,02 dB
consecutively).
Specifically it can be shown that baseline of object
movement when there is not much movement, Exhaustive
Search method has better performance than Half Pixel method.
As PSNR value of insert frame intermediate the 2th up to 12th
of Exhaustive Search was higher than Half Pixel. It is because
that the accuracy increment on baseline movement is not
necessary and therefore it is not affectedly when applied.
Moreover on insertion frame intermediate 268th up 332 th, the
performance of Exhaustive is better than Half Pixel because
on 267th up to 333th frame sequence when object movement
start to occur with background change. The accuracy
increment of Half Pixel is unable to maximally perform.
Figure 8 and 9 showed test video Hall monitor, trace PSNR of
Exhaustive Search and Half Pixel on the search area ±7 and
±15.
Figure 8 and 9 below showed that trend of trace PSNR
comparison of Full Search was almost linear. This is an
accordance to Hall Monitor characteristic small movement.
Generally with Half Pixel search method, mean PSNR value
on search area ±7 and ±15 using Hall Monitor video test has
produced lower than Exhaustive Search (1,41 and 1,14 dB
consecutively). However mean PSNR value accomplish by
Half Pixel search method can be maintain when search area is
increase from ±7 to ±15 (34,13 dB). This finding showed that
Half Pixel is not effective it is used on video with low
movement as in Hall Monitor.

B. COMPLEXITY ANALYSIS OF FULL SEARCH METHOD
Complexity analysis in this study is obtain based on time
computation defined as the period needed in by compensation
process on every frame intermediate from its search method.
Compensation process is computation done by search method
to get the best mv candidate until from reconstruction process
finished. Therefore, the more computation is done by the
search method will have an impact to the necessary period is
needed. Before stepping to analyze to comparative algorithm
complexity we will describe comparative algorithm
computations that have latter complexity itself. Meanwhile
Half Pixel search method increases the accuracy from the
Exhaustive Search process. Does number of steps of
Exhaustive Search and Half Pixel will be equal.
For search range ±7 pixel with Exhaustive Search
method, block size condition k= N x N pixel, and frame size
= M x M pixel, therefore :
• Number of candidate mv = (2p +1)2
• Number of computation for each block = (2p + 1)2 N2
• Number of computation for each frame = (2p + 1)2
N2 x M2/N2 = M2 (2p + 1)2
Whereas for search range ±7 pixel with Half Pixel
method, block size condition k= N x N pixel, and frame size
= M x M pixel, therefore :
•
•
•

Number of candidate mv = ((2*(2p +1)-1)2
Number of computation for each block = ((2*(2p
+1)-1)2 N2
Number of computation for each frame = ((2*(2p
+1)-1)2 N2 x M2/N2 = M2 (2p + 1)2

For search range ±7 pixel with Exhaustive Search
method, block size condition k= 8x8 pixel, and frame size =
176 x 144 pixel, therefore :
• Number of candidate mv= (2p +1)2 = (2.7 + 1)2 =
225
• Number of computation for each block = (2p + 1)2 N2
= 225x 64 = 14.400
• Number of computation for each frame = M2 (2p +
1)2 = 225x176x144= 5.702.400
For search range ±7 pixel with Half Pixel method, block
size condition k= 8x8 pixel, and frame size = 176 x 144 pixel,
therefore :

Figure 8 Comparison PSNR Of Exhaustive Search and Half Pixel On
Search Area ±7 Pixel By Using Hall Monitor Video Test.

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•
•
•

Bali, 5 - 7 November 2014

Number of candidate mv = ((2*2p +1)-1)2 = ((2*14 +
1)-1)2 = 841
Number of computation for each block = ((2*2p +1)1)2 N2 = 841 x 64 = 53.824
Number of computation for each frame = M2((2*2p
+1)-1)2 = 841 x 25344 = 21.314.304

shows trace compensation time that is required by its method
with hall monitor video test.

Half Pixel search method produced accuracy increment
by a half of pixel and therefore computations by Half Pixel
search method is more than Exhaustive Search method. Using
the above formula it can be determine number of mv candidate
that is number a computations in one block and computations
in one frame in search area ±7 and ±15 it can be shown in
table I below.

Figure 11 Comparison Compensation Time Of Exhaustive
Search and Half Pixel Method On Search Area ±15Pixel By Using
Foreman Video Test

Trace compensation time which is obtained from
simulation Hall Monitor video test is shown in figure 12 and
13 below.

Tabel I Calculation Of Computation Full Search Method
Metode
Pencarian

Ukuran
daerah
pencarian

Jumlah
Kandidat
MV

Exhaustive
Search
Half Pixel

7
15
7
15

225
961
841
3721

Jumlah Komputasi
Per blok
14400
61.504
53.824
238.144

ICSGTEIS 2014

Per frame
5.702.400
24.355.584
21.314.304
94.305.024

Table I shows that Exhaustive Search and Half Pixel
algorithm is very much affected by search area size. Search
area size will affect number of mv vector candidate in which
the bigger the search area size the more candidate mv can be
produced. It is because that the wider search area it will result
in increase of number of test point. Based on calculation above
computation it is generally can be analyzed that time needed
for compensation process on search area ±7 and ±15 from
Half Pixel search method was higher than Exhaustive Search.
This because Half Pixel search method carry out search
process with increment of accuracy by Half Pixel from
Exhaustive Search process. This analysis is supported by value
of compensation trace time which is obtained from simulation
Foreman video test is shown in figure 10 and 11 below.
.

Figure 12 Comparison Compensation Time Of Exhaustive Search
and Half Pixel Method On Search Area ±7 Pixel By Using Hall
Monitor Video Test

Based on graph in figure 12 it is revealed that average of
time needed by Half Pixel method was 5,27s higher than
Exhaustive Search method. In order to increase search area
(±15 as shown by figure 13). It revealed that average time that
is needed by Half Pixel was 20,01 s higher than Exhaustive
Search method. This is because of process Half Pixel
computation is more frequent which is accuracy increment by
Half Pixel than Exhaustive Search method which is have an
impact on compensation time needed.

Figure 10 Comparison Compensation Time Of Exhaustive Search
and Half Pixel Method On Search Area ±7 Pixel By Using Foreman
Video Test.

Figure 13 Comparison Compensation Time Of Exhaustive Search
and Half Pixel Method On Search Area ±15 Pixel By Using Hall
Monitor Video Test

Figure 10 shows that compensation time needed by Half
Pixel search method is 2,37 s which is greater than Exhaustive
Search method. In order to increase search area size (±15 as
shown by figure 11) average of compensation time needed by
Half Pixel 11,8 was higher than Exhaustive Search method. It
is revealed that complexity of Exhausted Search method is
lower than Half Pixel search method. Figure 11 and 12 below

Based on the above explanation its concluded that size of
search area affects compensation time needed by Full Search
Method. The wider search area, the higher compensation time
is required. In order compare to Full Search method on the
search area ±7 and ±15, Exhaustive Search method is lower
than Half Pixel search method.

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V. RESULTS AND CONCLUSION

It is concluded that Half Pixel method with the accuracy
increment by Exhaustive Search method is more effective and
better performance for inserting frame intermediate on FRUC
while is implemented on video with high motion
characteristic. By comparing complexity Full Search by on
search area ±7 and ±15 based on computation time it is
revealed that Exhaustive Search method has lower complexity
compare to Half Pixel method for insertion frame intermediate
on FRUC algorithm.
.
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