Decoding Approach With Unsupervised Learning of Two Motion Fields For Improving Wyner-Ziv Coding of Video.

Research India Publications
International Journal of Applied Engineering Research
ISSN 0973-4562 Volume 10, Number 5 (2015)
http://www.ripublication.com
The International Journal of Applied Engineering Research (IJAER) is an international research journal, which publishes top-level work from all
areas of Engineering Research and their application including Mechanical, Civil, Electrical, Computer Science and IT, Chemical, Electronics,
Mathematics, Environmental, Education Geological etc. Researchers in all technology and engineering fields are encouraged to contribute
articles based on recent research. Journal publishes research articles and reviews within the whole field of Engineering Research, and it will
continue to provide information on the latest trends and developments in this ever-expanding subject.

Editorial Board Members
Zaki Ahmad
Department of Mechanical Engineering, KFUPM,
Box # 1748, Dhaharan 31261 Saudi Arabia
Shigeru Aoki
Department of Mechancial Engineering, Tokyo
Metropolitan College of Technology, 1-10-40
Higashi-Ohi, Shinagawa-ku, Tokyo 140-0011, Japan

Rajeev Ahuja
Physics Department,Uppsala University,Box 530,

751 21 Uppsala Sweden
Dimitris Drikakis
Aerospace Sciences Department, Cranfield
University, School of Engineering, Cranfield,
Befordshire, MK43 0AL, United Kingdom

Osama Badr
Mechanical Engineering Department, Qatar
University, P.O. Box 2713, Doha,Qatar
Fatma Abou-Chadi
Dept. of Electronics and Communications
Engineering,Faculty of Engineering, Mansoura
University, Egypt

Ching-Yao Chen
Dept. of Mechanical Engineering, National Yunlin
University of Science & Technology, University
Road, Touliu, Yunlin, Taiwan, 640 R.O.C.
Tariq Darabseh
Mechanical Engineering Department, P.O. Box 3030

Irbid-22110 Jordan
M. Venkata Ramana
Microscopy and Nano Tech Laboratory Dept. of
Metallurgical and Materials Engineering Indian
Institute of Technology Madras Chennai 600 036,
India
. B.T.F. Chung
Department of Mechanical Engineering, University
of Akron, Akron, Ohio 44325, USA
M.Y. Khalil
Nuclear Engineering Department, Faculty of
Engineering, Alexandria University, Alexandria
21544 Egypt
Naser S. Al-Huniti
Mechanical Engineering Department, University of
Jordan, Amman 11942, JORDAN
Kazuhiko Kudo
Laboratory of Micro-Energy Systems, Division of
Human Mechanical Systems and Design, Graduate
School of Engineering, Hokkaido University, Japan

Ihab Obaidat
Department of Physics, UAE University, PO Box
17551, Al-Ain, UAE
Huihe QIU
Department of Mechanical Engineering,The Hong
Kong University of Science and Technology, Clear
Water Bay, Kowloon Hong Kong
Allan Runstedtler
Natural Resources Canada, CANMET Energy
Technology Centre - Ottawa, 1 Haanel Drive
Ottawa, Ontario K1A 1M1 Canada
S.A. Soliman
Electrical Engineering Department, University of
Qatar, P. O. Box 2713 Doha Qatar

Marcelo J.S. De Lemos
Departamento de Energia - IEME, Instituto
Tecnologico de Aeronautica - ITA, 12228-900 Sao
Jose dos Campos - S.P. - Brazil .
Nihad Dib

Electrical Engineering Department,
P. O. Boc 3030, Irbid 22110 Jordan
Sayavur I. Bakhtiyarov
New Mexico Institute of Mining and Technology,
Mechanical Engineering Department, 122 Weir
Hall, 801 Leroy Place, Socorro, NM 87801-4796,
USA
F. Hayati
Faculty of engineering, Ajman university of Science
& Technology Network, Ajman, UAE
M.A.K. Jaradat
Department of Mechanical Engineering, Jordan
University of Science &Technology, Irbid 22110,
Jordan
Bashar El-Khasawneh
Industrial Engineering Department, JUST, P.O. Box
3030, Irbid 22110 Jordan
K.K. Pathak
Scientist & Advisor, Computer Simulation & Design
Group, Advanced Materials and Processes Research

Institute (CSIR), Bhopal 462026 (MP) INDIA
H.M. Omar
Department of Aerospace Engineering, KFUPM,
P.O. Box # 1794, Dhahran, 31261 Saudi Arabia
K. R. Rajagopal
Department of Mechanical Engineering,Texas A&M
University, 3123 TAMU, College Station TX 778433123, U.S.A
Ismail Shahin
Electrical and Computer Engineering Department,
University of Sharjah, P. O. Box 27272, Sharjah,
United Arab Emirates
Jinho Song
Thermal-hydraulics and Reactor Safety Research
Division, Korea Atomic Energy Research Institute,
P.O. Box, 105, Yusong, Taejon , 305-600, Korea
B.M. Vaglieco
Istituto Motori, via G.Marconi, 8-80125- Naples
Italy

A.S. Al-Harthy

Department of Civil, Surveying and Environmental
Engineering, University of Newcastle, Callaghan,
NSW 2308 Australia
Q. Chen
Department of Mechanics and Engineering science,
Peking University,Beijing 100871
Adel Taha Mohamed Abbas
Computer Aided Design & Manufacturing
Mechanical Engineering Department, College of
Engineering King Saud University Riyadh, Saudi
Arabia, 11421
Annette Bussmann-Holder
Max-Planck-Institute for Solid State Research,
Heisenbergstr. 1, D-70569 Stuttgart, Germany
S.Z. Kassab
Mechanical Engineering Department, Faculty of
Engineering, Alexandria University, Alexandria,
21544 Egypt,
Y.A. Khulief
Department of Mechanical Engineering, KFUPM

Box 1767, Dhahran, 31261, KSA
A. A. Mowlavi
Physics Department, School of Sciences,Tarbiat
Moallem University of Sabzever; P.O. box
397,Sabzevar, Iran.
A. A. Mohamad
Dept. of Mechanical and Manufacturing
Engineering
D. Ramkrishna
School of Chemical Engineering, Purdue University,
IN 47907-2100 USA

Bassam A. Abu-Nabah
Department of Aerospace Engineering and
Engineering Mechanics, College of Engineering, The
University of Cincinnati, USA
Guo-Xiang Wang
Department of Mechanical Engineering, The

Huimin Xie

Dept. of Engineering Mechanics, Tsinghua

Ashraf Shikdar
Department of Mechanical & Industrial
Engineering, S.Q. University,P.O Box 33, Al-Khod
123 Oman
H.H. El-Tamaly
Chairman of electrical engineering Dept.,Faculty of
Engineering, Elminia University, Egypt.
Dimitri V. Val
Dept. of Structural Engineering and Const. Manag.,
Faculty of Civil and Environmental Engineering,
Technion - Israel Institute of Technology, Haifa
32000, Israel
Wan Aizan Wan Abd Rahman
Department of Polymer Engineering , Universiti

University of Akron, AkronOH 44325-3903 USA

University, 100084 Beijing, China

Teknologi Malaysia , Skudai, Johor, Malaysia

Ahmed Sahin
Mechanical Engineering King Fahd University of
Petroleum and Minerals Dhahran 31261, Saudi
Arabia
M.R. Eslami
Department of Mechanical Engineering, Amirkabir
University of Technology, Hafez Ave. Tehran, 15914
Iran
Tachtouch Bourhan
Department of Thermofluid Science. King Fahd
University of Petroleum and Minerals. Dhahran
31261, Saudi Arabia
Fahd A. Alturki,
College of Engineering (Majmaah University),
Intelligent Systems And Control Engineering King
Saud University P. O. Box 800, Riyadh 11421, Saudi
Arabia

Mohammed Salifu
Department of Civil Engineering, Faculty of Civil and
Geomatic Engineering, College of Engineering
Kwame Nkrumah University of Science and
Technology (KNUST) University, Post Office Kumasi,
GHANA
Sellakkutti Rajendran
School of Mechanical and Aerospace Engineering,
Nanyang Technological University Nanyang
Avenue, Singapore
Mohammad Luqman
Chemical Engineering Department King Saud
University Chemical Engineering Department,
Riyadh, Saudi Arabia
Mohammad Valipour
Department of Irrigation and Drainage Engineering,
College of Abureyhan, University of Tehran,
Pakdasht, Tehran, Iran-1675755936

Samir Medik

Mechanical Engineering Department King Fahd
University of Petroleum and Minerals PO Box 155,
Dhahran, 31261, Saudi Arabia
Mohamed Younes
Mechanical Engineering Department, Faculty of
Engineering, UAE University, P.O. Box 17555, AlAin, UAE
Abdul Razak Rehmat
Department of Polymer Engineering , Universiti
Teknologi Malaysia ,Skudai, Johor, Malaysia

Meamer El Nakla
Mechanical Engineering Department King Fahd
University of Petroleum and Minerals P.O. Box
323, Dhahran 31261, KSA
Zulkifli Yusop
Water Research Alliance UTM, Skudai, Johor,
Malaysia

Kishorereddy
Electrical Engineering, Adama Science &
Technology University, Narayanapuram (v&p),
Sathupally (MD), Khammam (DT), Andhra Pradesh,
India
R. Manikandan
Department of ICT, School of Computing, SASTRA
Universtiy, Thanjavur, Tamil Nadu, India

Abdullah M. Al-Shaalan
EE department College of Engineering P.O. Box 800
King Saud University Riyadh-11421 Kingdom of
Saudi Arabia
Srinivas Mantha
School of Engineering & Technology and Professor ECE Department, Centurion University of
Technology and Management, R. Sitapur,
Uppalada, Paralakhemundi, Gajapati Dist, Orissa.
761 211 India
Giriprasath Gururajan
Bartlesville Technology Center,ConocoPhillips
Company Oklahoma, Bartlesville, USA.
Chee-Ming Chan
Faculty of Engineering Technology, Universiti Tun
Hussein Onn Malaysia, 86400 Batu Pahat, Johor,
Malaysia
Swapnadip De
Department of Electronics and Communication
Engineering (ECE), Meghnad Saha Institute of
Technology, Nazirabad, East Kolkata Township,
West Bengal, India.
K.B. Jayarraman
Computer science & engineering Dept., Professor /
head of the Deparment, Manakula Vinayagar
Institute of Technology, Kalitheerthalkuppam,
Madagadipet, Puducherry, India
Sushant K. Singh
Earth and Environmental Studies Department,
Montclair State University, New Jersey, USA, 1
Normal Avenue, Montclair State University , 358N
ML, Montclair, 07043, New Jersey, USA

M. A. Habib
Mechanical Engineering Department, King Fahd
University of Petroleum and Minerals.Dhahran
31261, Saudi Arabia.
Mir Iqbal Faheem
Dept. of Civil Engineering Deccan College of
Engineering & Technology Darussalam, Near
Nampally Hyderabad (AP) 500001 India
Damodar Maity
Civil Engineering Department Indian Institute of
Technology Associate Professor, Civil Engineering
Department Indian Institute of Technology,
Kharagpur, West Bengal, India - 721302. West
Bengal Kharagpur 721302 India
Ram Shanmugam
School of Health Administration Texas State
University -San Marcos University Drive, San
marcos, TX 78666, USA.
P. Rathish Kumar
Civil Engineering Department, National Institute of
Technology (NIT) Warangal-506 004, Andhra
Pradesh, India
Najm Obaid Salim Alghazali
Department of Civil Engineering, Babylon
University, Hilla, Babylon, Iraq

Shrikant Tiwari
Department of Computer Science & Engineering,
Faculty of Engineering & Technology (FET), Shri
Shankaracharya Technical Campus, Block No. 15/B,
Street No. 29, Sector-07, Bhilai Nagar, City: Bhilai,
District: Durg, Chattisgrah, India
Umashankar S
School of Electrical Engineering, VIT University,
Vellore, Tamilnadu, India

Contact Information:
For Subscription orders may be contact directly to publisher (address given below) using Journal Subscription Form or contact
your preferred subscription agent. All orders should be accompanied by payment. Please make checks payable to ("Research
India Publications"). Mail order with payment to:
Subscription Manager :
Research India Publications
Head office: B-2/84, Ground Floor,
Rohini Sector-16, Delhi-110089, INDIA
Email: subscription@ripublication.com
Phone No.: 91-11-27893171, 011-65394240
Fax No.: 91-11-27297815
For more information or assistance to place an order, please feel welcome to contact us again at subscription@ripublication.com

International Journal of Applied Engineering Research (IJAER)
ISSN 0973-4562 Volume 10, Number 5 (2015)
CONTENTS
Effect of Holding Time on Strength and Abrasion Resistance of Cu-Ni-Mn Alloyed Permanent Molded Toughened Austempered Ductile Iron For
Automotive Gear Applications
Uma T.R, Chandramohan G
11565-11574
Secure Video Channeling

. Mohamed abdaal taj.N , Lenin M, Venkateswaran.A, N.R.Raajan

11575-11580

A Study on The Sectors That Increase The Percentage of Carbon Dioxide Which Causes Global Warming Using Fuzzy Cognitive Maps
..
. B.Kumaresan, N.Vijayaraghavan
11581-11586
An Improved Trimmed Median Filter For The Restoration of Images Corrupted By High Density Impulse Noise
J Jezebel Priestley, V Nandhini ,V Elamaran

11587-11597

A Dual Face Detection Water Marking System Using Sf For Verifying Security

Bestley Joe.S

11599-11609

Application of Artificial Intelligence Techniques For The Prediction of Blood Glucose Concentration
.. S.Shanthi, P.Balamurugan, D.Kumar

11611-11631

Fuzzy RG-Super Open Set

. M.K. Mishra, Govindaswamy Indhumathi, D. Bindu

11633-11640

Design and Implementation of Fault Tolerant Energy Efficient Technique In Enhancing The Life Time of Sensor Nodes
. S.Saranyavisalini, M.Vadivel

11641-11648

Harmonic Reduction In Single Phase Induction Motor By Coating Sio2 and Al2o3 Nano Filler Mixed Enamel To The Stator Windings of The Single
Phase Induction Motor
.......................................... Mohanadasse. K, Dr. C. Sharmeela, D. Edison Selvaraj
11649-11659
FPGA Implementation of Rotated Local Gabor Xor Pattern For Hybrid Face Recognition System
. P. Kannan, R.ShanthaSelva Kumari and A. Sivanantha Raja

11661-11676

Flexural Behaviour of Self Compacting Concrete Slabs Using Conventional Steel Bars and Carbon Fibers
.. D. Benita M Hexton, S. Ajin

11677-11689

A Reliable Treatment of Analytic Functions

.. Saumya ranjan Jena, Arjun Singh

11691-11695

Pavithrasundarrajan

11697-11708

.. Richard Jegadeesan P

11709-11712

.. Sivakumar R. S

11713-11721

.. Abarna Subramanian

11723-11736

M. Shyamala Devi, Dr.C. Arun

11737-11748

Simulation of Different Diaphragm Structures For Optical MEMS Pressure Sensors and Its Comparison
T.Sripriya, Dr.V.Jeyalakshmi

11749-11754

An Overview of Concentrated Solar Power (CSP) Applications

Abel J Francis, Dr. V.K. Bupesh Raja

11755-11761

Decoding Approach With Unsupervised Learning of Two Motion Fields For Improving Wyner-Ziv Coding of Video
I Made Oka Widyantara

11763-11776

Estimation of Aquifer Parameter By Well Dilution Technique

B.Priyadharshini, Nandini

11777-11786

A Review on Image Processing Applications Used In Agriculture
. T. Senthil Murugan, K. Antony Kumar, K. A. Varunkumar, M. Thanjai Vadivel,V. Shanmuganathan

11787-11793

A FPGA Implementation of Novel Median Filter To Remove Impulse Noise

.. L.S.Usharani, Dr. P. Thiruvalarselvan

11795-11802

A. L.EscalinTresa, B.Dr. M. Sundararajan

11803-11810

. Kshitij. D. Palkar, Archith Rajan, Rajkumar E.R

11811-11821

Comparative Analysis of Different Brain Tumor Segmentation Methods
. Shruthi Gubbi, Apoorva Safai, Neha Anegondi , Rajkumar E.R

11823-11833

VLSI Implementation of 4X4 MIMO SC-FDMA Transceiver For Low Power Applications
... Dr.J.Raja, P.Mangayarkarsai, K.Moorthi

11835-11852

Area Efficient, Low Power, High Performance Cached FFT Processor For MIMO OFDM Application
........... Dr.J.Raja, P.Mangayarkarsai, K.Moorthi

11853-11867

Design and Simulation of Receiver Circuit For Eddy Current Flow Meter
English Language Teaching and Learning With Technology: A Study
Solving Non-Smooth Economic Load Dispatch Problem Using Self-Adaptive Cuckoo Search
An Experimental Investigation on Paper Concrete
A Survey on Secured Fuzzy Searching Techniques For Effective Data Utilization In Public Cloud

An Intelligent Repeated Objects Tracking on Video Sequences

..

Analysis of Seizure Detection Using Back Propagation Neural Network

Research on The Fmri Study of Chinese Speech Region of Interest Based on DIVA Model
. Shaobai Zhang, Yanlin Chen , Youyi Liu

11869-11880

Image Recognition By Majority Voting

. K.Komathi,M.Latha ,C.Swaraj Paul ,J.Senthil Kumar

11881-11887

Detection and Minimization of Harmonics Using Daubechies Wavelet
. Y. Srinivasa Rao, K.Naresh ,M. Kiran kumar, Mahaboob Shareef Syed

11889-11908

Application of Bat Algorithm To Optimize A SPARQL Query

R.Gomathi, D.Sharmila

11909-11918

Empirical Performance Analysis of Distributed Differential Evolution For Varying Migration Topologies
..
Meera Sanu1 G.Jeyakumar

11919-11932

Detection of Epilepsy Disorder Features By Eeg Using DWT

... A.Gracekumar, S.Manirathnam, R.Deepan, K.Narasimhan

11933-11940

Ontology Integration For Query Expansion and Semantic Filtering Using Word net For Information Retrieval
. J. Mannar Mannan, Dr. M Sundarambal

11941-11956

Implementation and Comparison of RSA and Triple DES Algorithm For Encryption and Decryption In Cloud Environment
... Rachna Jain, Dr. Sushila Madan and Dr. Bindu Garg

11957-11971

Student Attendance Management System Using Ldn and Face Recongnition Concept
G venkatesh, G jithesh, E V Manju, Dra V K Shanthi

11973-11977

Performance Analysis of Biorthogonal Wavelets For Edge Feature Extraction of X-Ray Images Based on Parameterized Filter Design
. P.M.K.Prasad, Prof.G Sasi Bhushana Rao, M N V S S Kumar
11979-11993
Timeslot Allocation and Management Scheme To Deal With Emergency Data In Wireless Body Area Network
Reema Goyal, Dr. H.S. Bhadauria, Dr. R. B. Patel, Dr. Devendra Prasad

11995-12008

Experimental Study on The Mechanical Properties of Natural Fibers Reinforced Hybrid Composite
.. Parandaman P, Jayaraman M

12009-12019

A Novel Accelerated Fuzzy Pi Controller Based Chopper Driven Pmdc Motor
.. Muthukrishnan S, Murugananth G, & Samidurai K

12021-12032

Harmonic Reduction For Adjustable Speed Drive With Reduced Switch Topology

.. J. Deepika, D. Uma

12033-12044

. P. EzhilMeera, D. Uma, K.Vijayarekha

12045-12055

Minimizing Makespan of Permutation Flow-Shop Scheduling Using New Heuristic Approach
. P. Dharmalingam, J. Maniraj, R. K. Suresh, R. Gopinath

12057-12071

A Survey of MIMO-Transceiver Designs In Wireless Communication Systems
... C. Manikandan, Dr. P. Neelamegam, A. Srivishnu, K. Gowtham Raj

12073-12078

Power Quality Improvement Using A Voltage Controlled Dstatcom

K. Sivapriya, Dr. S. Thiruvenkadam

12079-12091

Performance and Analysis of Z-Source Inverter

. R. Ramya and T.S. Sivakumaran

12093-12104

A Detailed Investigation of Crack and Failure Study of Steel Fibre Reinforced Concrete Beams
. R.Subashchandrabose, Dr.R.Venkatasubramani

12105-12112

An Efficient and Secure Dynamic Multi Dimensional Cloud Confidence

.. S.Kavinhariharasudhan, Dr.S.Saravanakumar

12113-12119

.. Dr. Monalisa Bal

12121-12132

Performance Analysis and Detection and Correction of Sink Hole Attack In MANET
. Sandeep Kumar Arora, Nisha Puri, Sanjeev Sharma

12133-12144

A Novel Approach In Increasing Power Quality Using Cascaded Multilevel Inverter Based Statcom
.. G.Sundar, R.Vinothini, M.Divya Menon

12145-12151

Tuning of A Conventional Diesel Engine Into A Low Compression Ratio Diesel Engine
.. P.Bridjesh, G. Arunkumar and S.Mohanamurugan

12153-12163

Efficiency Measurement of Detecting Object From Video

.. Saranu Kavya Pooja, Koganti Koundinya, J Hemamalini

12165-12175

Mr. C. Balaji , Mr. L. Lakshmanan

12177-12186

K Babul Reddy, A Santhi Mary Antony

12187-12196

Analysis of Various Controllers For Single Phase Shunt Active Filter

Neo Liberalism, Social Exclusion, Education Policy and Kiss Odisha

Agricultural Robocop Using Raspberry Pi
A Single Sensor Based PFC SEPIC Converter Fed BLDC Motor Drive For Fan Applications

Base Station Power Consumption Performance Improvement With Novel Reduction Methods And Optimum Utility Management
... Payel Giri, Sudhansu Sekhar Singh
12197-12212
Design and Implementation SEPIC Integrated KY Converter For SLIT-LAMP

.. Venkatanarayanan S, M.Saravanan

12213-12227

International Journal of Applied Engineering Research
ISSN 0973-4562 Volume 10, Number 5 (2015) pp. 11763-11776
© Research India Publications
http://www.ripublication.com

Decoding Approach With Unsupervised Learning of Two
Motion Fields For Improving Wyner-Ziv Coding of Video
I Made Oka Widyantara
Telecommunication System Lab., Department of Electrical Engineering, Udayana
University
Kampus Bukit Jimbaran, Badung, Bali, Indonesia, 80361
+62-0361-701533, oka.widyantara@unud.ac.id

Abstract
Wyner-Ziv video coding (WZVC) is a video coding paradigm allows
exploiting the source statistic, partially or totally, at the decoder to reduce the
computational burden at the encoder. Side information (SI) generation is a key
function in the WZVC decoder, and plays an important role in determining the
performance of the codec. In this context, this paper proposes decoding
approach with unsupervised learning of two motion fields to improve the
accuracy of generation of soft SI on WZVC codec. The method used in this
paper is based on the generalization of Expectation-Maximization (EM)
algorithm, in which the learning process of motion fields used the LowDensity Parity-Check (LDPC) decoder soft output values and two frames
previously decoded as initial SI. In this method, the decoder always updated
the accuracy of soft SI by renewing two motion fields iteratively. The goal is
to minimize the transmission of the bits required by the decoder to estimate
frame WZ. The experimental results show that the proposed codec WZVC
could improve performance rate-distortion (RD) and lower the bit
transmission compared to the existing WZVC.
Keywords: Wyner-Ziv Video Coding, Unsupervised learning, EM Algorithm,
Side Information, LDPC

Introduction
The mobile and wireless networks have developed a new application model in which
a lot of senders send data to a centre receiver, such as video surveillance application
and visual wireless sensor networks. Specifically, these applications need a video
encoder with low complexity and efficiency in compression. Along with this
requirement, WZVC offers a solution video coding with low complexity encoder, in
which the estimation process motion field at encoder is moved to decoder.

11764

I Made Oka Widyantara

WZVC was developed based on Information theories; they are Slepian-Wolf
theorem [1] and Wyner-Ziv [2]. The Slepian-Wolf (SW) theorem for lossless
compression states that it is possible to encode correlated sources independently and
decode them jointly, while achieving the same rate bounds which can be attained in
the case of joint encoding and decoding. The Wyner-Ziv (WZ) theorem extends the
SW one to the case of lossy compression when SI available at decoder. By using both
of them, the WZVC decoders are responsible for exploiting all (or most off) the
source statistics and, therefore, to achieve efficient compression.
The general scheme of WZVC classifies video frames into Key frame and WZ
frame. The Key frame is encoded using conventional video coding; therefore, the WZ
frame is encoded by channel coding principles, and is decoded by the SI of frames
that were decoded earlier. With this scheme, the complexity of the encoder can be
reduced but the decoder takes an additional burden to generate SI and Frame WZ
decoding.
To form WZ decoding, the decoder must perform SI generation process. The
quality of SI becomes an important indicator in the decoding process, because the
quality of the SI determines the upper limit of WZVC performance. SI can be
generated by motion compensated temporal interpolation/extrapolation (MCTI/E)
method, based on the previously encoded frame. In the motion estimation at the
decoder, the decoder does not have access to the current frame. This limits the
accuracy of the estimated motion vectors, so more bitrates are needed to reconstruct
the current frame. The difference in bitrate is stated to be the loss of video coding [3].
Several technical approaches of motion estimation for generating SI at the decoder
are described and their performances are compared by [4]. A common approach is
transform domain motion extrapolation/interpolation. For example, the first set of
motion extrapolation forms forward motion estimation from F(t-2) to F(t-1), then for
blocks in F(t), the motion vector of the co-located block in F(t-1) is used to obtain
correspondence compensated in F(t-1) frame. Without finding any information about
the current frame, the motion interpolation or extrapolation generally assumes that the
objects are moving at a constant speed so the motion vector already estimated reflects
the actual motion of the reference frame. This is a simple assumption and does not
correspond to the actual characteristics. As a result, the estimated SI does not match
the current frame.
To improve the quality of SI, many researchers propose different methods for
motion estimation in the decoder. These approaches include improved motion
iteratively, based on the results of the previous decoded. These approaches are further
categorized into two parts. In the first category [5]-[13], an initial estimate for SI
raised, then the first iteration of encoding the WZ will be formed based on initial SI.
In the first iteration, the decoder produces what is called a partially decoded frame. By
using these results, a motion restoration technique that is similar to the MCTI
techniques performed on the next iteration, in which the partially decoded frame is
used as a reference frame. In this way, the decoder has more information about the
frames to be estimated. The down side of this approach is the control of the bit-errorrate (BER) at the first iteration. If the BER is high, then the accuracy of motion

Decoding Approach With Unsupervised Learning of Two Motion Fields et.al.

11765

estimation based on the partially decoded frame will be worse than the initial SI.
Conversely, if the BER is low, the increase in RD performance becomes very small.
The second category is the SI generator techniques with motion learning
techniques, in which the generation of soft SI is performed by: (i) renewing the
motion field using a value-based LDPC decoder soft estimate of the EM algorithm
[14], and (ii) using the bands of transform that have been decoded [15,16]. Both of
these approaches use statistical information sources available during the decoding
process to iteratively improve the motion field. This approach can improve the RD
performance codec WZVC especially for the larger GOP measure, which is an
unusual thing in WZVC.
Specifically, the codec decoder WZVC-EM proposed by [14] using only the bit
stream of a WZ frame (the LDPC decoder soft output values) to study the motion
vectors with reference to one previous frame of reconstruction. Motion vectors relate
the correlation in the sequence of video frames, and become the unknown variables in
the decoder WZVC. Based only on the reconstruction of the previous frame as a
reference frame, unsupervised learning of one motion field of WZVC-EM codec
performance is limited by the quality of the SI frame. In the video sources that have
much temporal correlation, statistical information of current frame can be very much
different from the previous frame. Thus, the use of inaccurate information in the
estimation of current frame can degrade the performance of the WZVC-EM codec.
The use of more frames as a reference frame already available at the decoder can be
used as a solution to improve the accuracy of the soft SI generation.
In this paper, by using the framework [14] a new design where WZVC codec
decoder iteratively learning of unknown motion fields between the WZ frames and
multiple SI frames is proposed, where it should based on the generalization of the EM
algorithm [17]. Motion field compensated version of the multiple SI is used to
generate high-quality soft SI. The goal is to improve the efficiency of RD in frame
WZ coding.
This paper is organized as follows: section 2 describes unsupervised learning of
multiple motion fields based on generalization of EM algorithm. Section 3 describes
the details of the proposed modifications WZVC codec. Section 4 describes the
analysis of the performance of the RD experiment which has been done. Finally,
section 5 is the conclusion of this paper.

Generalized Em-Based Unsupervised Learning of Motion Field

In Figure 1, the current WZ frame (X) associated to multiple reference frames Ŷk
through multiple motion fields Mk, where k = 1,…., K. WZ frame (X) is encoded into
the syndrome bits (S) with LDPC encoder and subsequent the syndrome bits (S) is
gradually transmitted to the decoder through the feedback channel. In the decoder, a
scheme of unsupervised learning base on generalization of EM algorithm was made to
learning of multiple motion fields (Mk) iteratively. When Mk can be estimated
accurately, then the decoder can save the transmission bitrate to decode the WZ
frame. Generalisation of the EM algorithm to learn of Mk, can be explained as
follows;

11765

I Made Oka Widyantara

11766

Model
Based on reference [14], model of the decoder's posteriori probability distribution of
the source X based on the soft estimate decoder LDPC () parameter is as follows:

Papp {X}  P{X, }    i. j. X  i, j  

(1)

i, j

in which θ(i,j,) = Papp{X(i,j) = } is related to the soft estimate of X(i,j) with
luminance value {0,..,2d – 1}.
Decoder intends to calculate the posteriori probability distribution of Mk, as
follows:

papp {M k }  P M k | Yk , S ;
(2)

P M k  P Yk , S | M k ;









with the second step of Bayes. This form affirms a form of EM iterative solution.
Rate control
X

LDPC
Encoder

S

LDPC Decoder
(M-step)

θ

Reconstruction

^
X

Probability model
Generate soft
SI
P{Mi.j}

Block-based
motion estimator
(E-step)

Motion field
interpolation
P{Mu,v}

Ŷ

Figure 1: Scheme of unsupervised learning of motion fields through EM algorithm
E-step Algorithm
E-step updated to the estimated distribution on multiple motion fields (Mk) with
reference to the parameter of the soft estimate decoder LDPC (θ), in which θ is used
to help the motion estimation in order to improve a posteriori probability on Mk.
When the Mk estimation is done by the block-by-block motion vectors M(u,v), then
every block of θ(t-1) is compared to the collocated block of in each Yk, as well as all
those in a fixed motion search range around it. At iteration t, for a block of θ(u,v)(t-1)
with top left pixel located at (u,v), the distribution on the shift M(u,v) becomes updated
as below and normalized:

(t )
( t 1)
Papp
M (u ,v )k : Papp
M ( u ,v )k P Y( u ,v )k  M ( u ,v ) | M ( u ,v )k ;((ut ,v1))
(3)
k







 



M (u ,v )k  m1 ,......, mM k  1, 2,...., K
where m1,...,mM is the range of configuration of M(u,v)k with nonzero probability,
Ŷ(u,v)k + M(u,v)k is n  n blocks of Ŷk with the upper left pixel position which is placed on

Decoding Approach With Unsupervised Learning of Two Motion Fields et.al.

11767

{(u,v)+M(u,v)}, and P{Ŷ(u,v)k+M(u,v)k|M(u,v)k;θ(t-1)(u,v)} is the probability of observing
Ŷ(u,v)k+M(u,v)k generated through M(u,v)k of X(u,v) which is parameterized by θ(u,v) (t-1). All
the distributions on M(u,v)k are normalized; therefore, the summation becomes one.
Probability models iteratively updates the soft SI by blending information from
the pixels Ŷk corresponding to the motion field distribution that already repaired. This
procedure includes two stages: (i) motion field interpolation to improve the resolution
of the estimated motion field on a pixel-by-pixel (M(i,j)k,), and (ii) generation of soft
side information ( ). M(i,j)k is made by interpolating block-based motion field M(u,v)k
which has been repaired. In this paper, we propose a Lanczos interpolation technique
to improve the M(u, v)k into pixel resolution, which can reduce the complexity of
decoding codec WZVC [18].
Lanczos interpolation
This is based on the function of the 3-lobed Lanczos window as interpolation function
[19]. For a point (xD,yD) on the distribution of pixel-based motion field M(i,j)(xD,yD),
the interpolation algorithm using the distribution of block-based motion field
M(u,v)(xS,yS) in 36 blocks of the nearest neighbours of the point (xS,yS) is as follows:
xs 0  int( xs )  2;

xs1  xs 0  1;

xs 2  xs 0  2;

xs 3  xs 0  3;

xs 4  xs 0  4;

xs 5  xs 0  5;

ys 0  int(y s )  2;

ys1  ys 0  1;

ys 2  ys 0  2;

ys 3  ys 0  3;

ys 4  ys 0  4;

ys 5  ys 0  5;

(4)

where (xD,yD) are the coordinates of the pixel-based motion field M(i,j)(xD,yD), and
(xS,yS) are the coordinates calculated from the position of M(u,v)(xS,yS) which are
mapped to (xD,yD).
First, the probability distribution of M(u,v)(xS,yS) is interpolated along the x-axis to
generate 6 intermediate value distribution of motion field I(u,v)(xS,yS)k,, where k = 0, ..,
5.
I ( u ,v ) (x s , y s ) k   ai Papp M (u,v)  xsi , ysk  ,
5

0k 5

(5)

i 0

Then, the probability distribution of M(i,j)(xD,yD) is calculated by interpolating the
intermediate values along the y-axis distribution:

Papp M (i , j )  xD , yD    bk I u ,v  (x s , y s ) k
5

(6)

k 0

ai and bk are the coefficients which are expressed as:

ai  L  xs  xsi 
bk  L  ys  ysi 

(7)

and

11767

I Made Oka Widyantara

11768

L  x   sin c( x).Lanczos( x)
 sin( x) sin( x 3)
, 0 | x | 3

x 3
 x
 0
, 3 | x |


(8)

Soft SI generator
Soft SI generator are summing the estimation of each Ŷk block after being weighted
by Papp{Mi,j(xD,yD)}. Since there are as many as Ŷk which have to be weighted, then
soft SI generator selectively blends the aggregate in order to contribute to the
improvement of . In general, the probability that blended SI has a value of in pixel
(i, j) is:



 i, j,      Papp(t ) M (i , j )
K

(t )

mM

k 1 m  m1

K



 P M
mM

k 1 m  m1

(t )
app

( i , j )k

k


 m P X  i, j    | M (i , j )k  m, Yk

 


 m pZk   Yk ,m  i, j 

 




(9)

where PZk(z) is the probability mass function (pmf) of k as independent additive
noise Z. Ŷk,m is the reconstruction of the k frame which is compensated through m
motion configurations.
Equation (9) shows that the sum of the weights includes the whole shift version of
Ŷk.. When the entire distribution of M(i,j)k is obtained, the blending operation allows all
candidates of the partial shift to contribute to soft SI, (i, j)
Block candidates model
In a block-based estimation, a block model candidate of [20] is applied to the model
of the relationship of WZ frame with SI. In the context of the symbol-based encoding,
each block in WZ frame (which is parameterized by θ) consists of 2m level, which is
distributed simultaneously over {0,1,2,...,2m-1}. Each block of soft estimate decoder
LDPC (θu,v) is paired with M block candidate of each reference frame Ŷ(u,v)k + M(u,v)k.
Statistical dependence between θ and Ŷ(u,v)k + M(u,v)k is through the vector M(u,v)k =
(m1, m2,.., mM), respectively Laplacian distributed. When Mu,v = mi is known, then the
candidate block Ŷ(u,v)k[i,mi] are statistically dependent on block x[i] (which is
parameterized by θ) in accordance with:

Yˆ(u ,v ) k i, mi   x i   n i 

modulo 2m

(10)

in which the random vector n[i] has the same independent symbols as l

{0,1,2,..,2m-1}, with probability l. The symbol of all the other candidates, Ŷ(u,v)k[i,j
 mi] in this block is distributed at the same time through{0,1,2,., 2m-1} and
independent on x[i].
This candidate block model limits statistical dependency (soft matching) between
x[i] and Ŷ(u,v)k[i,mi] with Laplacian parameter (), so that:

Decoding Approach With Unsupervised Learning of Two Motion Fields et.al.

l 

l

 0   1  .....   2

11769
(11)

m

1

where,

 l2 
jika 0  l  2m1
exp   2  ,

 2 
l  
2
m
 exp   (2  l )  , jika 0  l  2m



2 2 



(12)

M-step Algorithm
In the M-step, soft estimate decoder LDPC (θ) is updated by LDPC decoder using soft
SI ( ) that has been generated with Equation (9), based on the joint bitplane LDPC
decoding method which is detailed in [14]. At iteration t, the value of θ is calculated
by:

 (t )  i, j ,   :  (t ) (i, j ,  )  g(t ) 
d

g 1

1


 g 1

1   
(t )
g

1


 g 0 

(13)

where g denote the gth in Gray mapping of luminance value
and 1[.] denote
indicator function. M-step also generates a hard estimate of for frame WZ by by
taking one most probable value for each pixel according to θ.
(i,j) = argmax θ(i,j, )

(14)

By iterating through the M-step and the E-step, the LDPC decoder requests more
syndrome bits if the estimates is not convergent. The algorithm terminates when the
hard estimate of yields syndrome which is identical to S.

Decoding Approach With Unsupervised Learning of Two Motion
Fields
Based on the scheme of EM generalization-based unsupervised learning of multiple
motion fields, the existing WZVC-EM codes are extended [14], from the decoding
with unsupervised learning of the one motion field into two motion fields. The
proposed transform domain WZVC codec architecture can be seen in Figure 2, works
as follows:
1. Input video sequence is divided into key frame (X) and WZ frame (Y), each is
encoded using intra JPEG and Wyner-Ziv. The 8 x 8 block-based Discrete
Cosine Transform (DCT) is applied to each WZ frame (Y) by the Wyner-Ziv
coding - and then quantize the transform coefficients into indices. The encoder
communicates these indices to the decoder using rate-adaptive LDPC through
the feedback channel.
2. The decoder makes soft SI ( ) for each key frame (X) by applying the method
of motion compensated frame SI interpolation, using the past (Ŷp) and future
(ŶF) decoded frames closest to X (adjacent key frames for GOP size 2). To

11769

I Made Oka Widyantara

11770

categorize the past and future decoded frames, we use the hierarchical coding
structure as proposed in [21], which means that the GOP size is greater than 2,
the entire decoded X is used to decode the remaining frames in the GOP.
3. Then, 8  8 DCT is applied to Ŷp and ŶF to transform the 8  8 blocks on
entire pixel shift which are then quantized into indices. In this way, indices of
each motion candidate, Ŷp and ŶF, available in the motion estimator to be
compared with the LDPC decoder soft estimate (θ), that is coefficient of the
index of X, and which is available in soft SI generator on probability models
for blended soft SI .
WZ Encoder

Rate control

WZ Decoder
θ

S

WZ frames

DCT

Quantizer

LDPC Encoder

LDPC Decoder

Reconstruction

IDCT

Decoded
WZ frames

X

X̂
Probability model P{M(i,j)2}
Soft SI generator
P{M(i,j)1}

Over-complete
transform
Key frames
Y

JPEG Encoder

P{M(u,v)2}
Motion field
interpolation

Block-based
motion estimator

P{M(u,v)1}

ŶF
ŶP

Frame
buffer

JPEG Decoder
Decoded Key frames

Figure 2: Domain transform WZVC codec architecture with unsupervised learning
two motion fields
4. Each block-based motion estimator updates the a posteriori probability
distribution of block-based motion field Papp{M(u,v)k} that is
Papp{M(u,v)1}between θ with Ŷp and Papp{M(u,v)2} between θ and ŶF using
Equation (3).
5. Lanczos interpolation upsample the Papp{M(u,v)1}and Papp{M(u,v)2} to the a
posteriori probability distribution of pixel-basis motion field Papp{M(i,j)1} and
Papp{M(i,j)2}.
6. Furthermore, the soft SI generator selectively chooses the best content of the
Ŷp and ŶF which are matched with X using Equation (9), with k = 1, 2.

Experiments
Test Conditions
The results of testing presented in this paper were obtained through the same
mechanism as in [14]. For the motion estimator and the probability models, the
motion search range is set to ± 5 each pixel horizontally and vertically. In the EM
algorithm in the decoder, the initial value of the Laplacian noise variance Z1 and Z2

Decoding Approach With Unsupervised Learning of Two Motion Fields et.al.

11771

are the same as in [14] and the initial distribution of both of the motion fields, M(u,v)1
and M(u,v)2 experimentally selected, such as:



(t )
Papp
M ( u ,v )k



  3 2 , if M  (0, 0)
u ,v
4


  43  801 if M u ,v  (0, ), (, 0)

2
1

  80  , otherwise

(15)

The RD performance is measured at four points, associated with the four JPEG
quantization scale factors of 0.5, 1, 2 and 4 [22]. As the WZVC codec performance
analysis in general, the luminance component of each frame is used to calculate the
bitrate and the peak-to-peak signal to noise ratio (PSNR).
RD Performance Evaluation
Proposed WZVC codec versus was existing WZVC codec
Figure 3 shows that the performance of the proposed WZVC codec is consistently
better than the existing WZVC codec. For GOP size 2, 4 and 8, the RD gain
respectively increase by 0.33 dB, 0.24 dB, 0.1 dB for Foreman and 0.25 dB, 0.3 dB,
0.2 dB for Carphone. This shows that selectively blending two probabilities a pixelbased motion fields to shift two SI frames (ŶP and ŶF), can improve the accuracy of
soft SI and lower transmission bitrate to estimate frame WZ.
Carphone, GOP = 2

Motion learning
Proposed method
Motion oracle
No compensation
JPEG

150

250

350
450
Rates (Kbps)

550

PSNR (dB)

PSNR (dB)

Foreman, GOP = 2

36
35
34
33
32
31
30
29
28

38
37
36
35
34
33
32
31
30
29

650

Motion learning
Proposed method
Motion oracle
No compensation
JPEG

100

200

300
400
Rates (Kbps)

500

PSNR (dB)

PSNR (dB)

Motion learning
Proposed method
Motion oracle
No compensation
JPEG

100

300
Rates (Kbps)

400

500

Carphone, GOP = 4

Foreman, GOP = 4

36
35
34
33
32
31
30
29
28

200

38
37
36
35
34
33
32
31
30
29

Motion learning
Proposed method
Motion oracle
No compensation
JPEG

100

600

11771

200

300
Rates (Kbps)

400

500

I Made Oka Widyantara

11772

Carphone, GOP = 8

Motion learning
Proposed method
Motion oracle
No compensation
JPEG

100

200

300
400
Rates (Kbps)

(a)

500

600

PSNR (dB)

PSNR (dB)

Foreman, GOP = 8

36
35
34
33
32
31
30
29
28

38
37
36
35
34
33
32
31
30
29

Motion learning
Proposed method
Motion oracle
No compensation
JPEG

50

150

250
350
Rates (Kbps)

450

550

(b)

Figure 3: RD performance at GOP = 2, 4 and 8, respectively for: (a) Foreman video
sequence, and (b) Carphone video sequence
Proposed WZVC codec versus alternative WZVC codecs
Figure 3 also shows the RD performance comparison between the WZVC codec and
that has proposed and the alternative WZVC codec. Alternative codec uses decoding
with motion oracle and no motion compensation [14]. Comparison with JPEG is also
conducted to provide a comparison of the performance of RD with conventional
coding standards. It was observed that the proposed WZVC codec produces better RD
performance compared to the motion oracle decoding, especially for Carphone video
sequence, reaching 0.3 dB at GOP size 8. This can be estimated that although the
motion oracle able to provide information about the motion in the motion estimator,
but the motion information provided was calculated with motion compensation
temporal interpolation (MCTI) in the encoder. So that, the motion information is not
true motion. When the temporal distance increases, the accuracy of the motion of
motion oracle is also declined.
Quality and Bitrate Temporal Evaluation
Figure 4 shows the comparison of the temporal with existing WZVC codec in PSNR
form and the total number of bits required for each WZ frame, using GOP size 4 and
JPEG quantization scale factor of 0.5 for Foreman and Carphone sequences. In
general, both WZVC codec produce the same video reconstruction (PSNR) but it is
different in regard to the rate required to estimate WZ frame. For Foreman, the
proposed WZVC codec consumes more bits in the middle of each frame in the GOP
structure, such as frame 18 that is between frame 16 and 20. However, as a whole, our
proposed method can lower the bitrate transmission respectively by 4.32% per frame
for Foreman and 5.46% per frame for Carphone.

Decoding Approach With Unsupervised Learning of Two Motion Fields et.al.
Foreman. GOP 4, Qf = 0.5
40

40

Bit per frmae (Kbit)

Bit per frmae (Kbit)

45

35
30

25
20
Existing WZVC WZ frames
Proposed WZVC WZ frames
Key frame (JPEG)

15
10

0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76 80 84 88 92 96
Frame number

Carphone, GOP4, Qf = 0.5

35
30
25

20
15
Existing WZVC WZ frames
Proposed WZVC WZ frames
Key frame (JPEG)

10
5

0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76 80 84 88 92 96
Frame number

37

40
PSNR (dB)

PSNR (dB)

11773

35
Existing WZVC WZ frames
Proposed WZVC WZ frames
Key frame (JPEG)

33

31

38
36

Existing WZVC WZ frames
Proposed WZVC WZ frames
Key frame (JPEG)

34
32

0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76 80 84 88 92 96
Frame number

0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76 80 84 88 92 96
Frame number

(a)

(b)

Figure 4: Bitrate and quality temporal evaluation on GOP size 4, and Q f = 0.5: (a)
Foreman video sequence, (b) Carphone video sequence
Decoding Complexity Evaluation
Encoding complexity is measured only from the decoder, because the codec scheme is
identical from the encoder side. Decoding complexity measurement is expressed as
the average of the EM iteration time per quadrant WZ Frame required by the decoder
to fulfil syndrome condition.
Table 1: Analysis of complexity for Foreman and Carphone sequences using Q f =
0.5, 1, 2, and 4 on the GOP 2, 4 and 8

GOP

2

4

Total decoding time (s)
Sequences WZVC
WZVC
proposed existing
Foreman
1881
1231
0.5
Carphone 1120
733
Foreman
1322
901
1
Carphone 775
497
Foreman
908
596
2
Carphone 553
336
Foreman
827
447
4
Carphone 425
241
Foreman
2068
1220
0.5
Carphone 1175
738
Foreman
1500
892
1
Carphone 841
507
Foreman
1045
597
2
Carphone 598
340
4
Foreman
1094
437
Qf

11773

Decoding
complexity
(%)
34.58
34.56
31.89
35.82
34.33
39.27
46.01
43.27
41.03
37.16
40.53
39.74
42.85
43.13
60.07

I Made Oka Widyantara

11774
Carphone
Foreman
0.5
Carphone
Foreman
1
Carphone
Foreman
2
Carphone
Foreman
4
Carphone

8

434
2678
1182
1607
842
1124
842
893
430

226
1228
711
904
488
610
488
433
213

47.98
54.15
39.85
43.78
42.08
45.67
42.08
51.50
50.47

Table 1 shows a comparison between the decoding complexity of the proposed
WZVC codec with the existing WZVC codec, respectively for Foreman and Carphone
video sequence. Based on the table, then the decoding complexity can be analyzed as
follows:
 Proposed WZVC decoder is 1.8 times more complex in average than the
existing WZVC decoder. Generation two motion fields in proposed WZVC
codec has improved the overall decoding complexity. In some literature,
efforts to improve the RD performance of WZVC codec also produces an
increase in the complexity of decoding up to 1.3 times and 12% (on a different
method) [13] [16].
 The complexity of the proposed decoding WZVC tends to increase the size of
the accretion GOP. This increase also occurred on the quantization scale
factor. The greater the temporal distance of SI frames in the GOP structure,
the more difficult the two motion fields estimation will be. In addition, the
larger the quantization scale factor, the lower the quality of the SI frame
(decoded JPEG) will be. In these conditions, the decoder requires more
number of iterations of EM to produce convergence syndrome in LDPC
decoder. This leads to the consumption of time in EM iteration becomes more.

Conclusion
This paper describes a WZVC codec that learning two motion fields in unsupervised
mode in the decoder. Proposed decoder with unsupervised learning of two field
motions generalize the framework of statistical estimation of the one motion field
decoder, based on a generalization of the EM algorithm. The proposed method can
improve the RD performance and bitrate saving compared with existing WZVC
codec.

Refrences
[1].

Slepian, D., and Wolf , J.K., 1973, ”Noiseless coding of correlated
information sources,‟ IEEE Transaction Information Theory, Vol. 19, pp.
471– 480.

Decoding Approach With Unsupervised Learning of Two Motion Fields et.al.
[2].

[3].

[4].

[5].

[6].

[7].

[8].

[9].

[10].

[11].

[12].

[13].

11775

Wyner, A.D., and Ziv, J., 1976, „The Rate-Distortion function for source
coding with side information at the decoder,” IEEE Transaction.
Information Theory, Vol. 22, pp. 1-10.
Li, Z., Liu, L., and Delp, E.J., 2007, “Rate Distortion Analysis of Motion
Side Estimation in Wyner–Ziv Video Coding,” IEEE Transaction Image
Processing, 16(1), pp. 98–113.
Brites, C. ., Ascenso, J., and Pereira, F., 2013, ”Side Information Creation
of Efficient Wyner-Ziv video Coding: Classifying and Reviewing,” Signal
Processing : Image Communication, 28, pp. 689-726.
Ascenso, J., Brites, C., and Pereira, F., 2005, “Motion compensated
refinement for low complexity pixel based distributed video coding,” Proc.
IEEE International Conference on Advanced Video and Signal Based
Surveillance (AVSS), pp. 593–598.
Artigas, X., and Torres, L., 2005, “Iterative generation of motion
compensated side information for distributed video coding”, Proc. IEEE
International Conference on Image Processing (ICIP), Vol. 1, pp. 833–836.
Adikari, A.B.B., Fernando, W.A.C., Arachchi, H.K., and Weerakkody,
W.A.R.J., 2006, “Sequential motion estimation using luminance and
chrominance information for distributed video coding of Wyner–Ziv
frames,” IEE Electronics Letters, 42(7), pp. 398-399.
Weerakkody, W.A.R.J., Fernando, W.A.C., Martinez, J., Cuenca, P., and
Quiles, F., 2007, ”An iterative refinement technique for side information
generation in DVC,” Proc. IEEE International Conference on Multimedia
and Expo (ICME), Beijing, China.
Ye, S., Ouaret, M., Dufaux, F., and Ebrahimi, T., 2008, ”Improved side
information generation with iterative decoding and frame interpolation for
distributed video coding,” Proc. IEEE International Conference on Image
Processing (ICIP), San Diego, CA, USA.
Bandem, M.B., Mrak, M., and Fernando, W.A.C., 2008, ”Side Information
Refinement Using Motion Estimation in DC Domain for Transform-based
Distributed Video Coding,” IET Electronic Letter, 44(16), pp. 965-066.
Bandem, M.B., Fernando, W.A.C., Martinez, J., and Cuenca, P., 2009,
”An Iterative Side Information refinement Technique for Transform
Domain Distributed Video Coding,” Proc. IEEE International Conference
on Multimedia and Expo (ICME), New York City, NY, USA.
Martins, R., Brit