Intelligent Multimedia Communication: Techniques and Applications

508 Pages·2010·13.372 MB·English

by  Paulo Nunes, Luis Ducla Soares (auth.), Chang Wen Chen, Zhu Li, Shiguo Lian (eds.)

ChangWenChen,ZhuLi,andShiguoLian(Eds.)
IntelligentMultimediaCommunication:TechniquesandApplications
StudiesinComputationalIntelligence,Volume280
Editor-in-Chief
Prof.JanuszKacprzyk
SystemsResearchInstitute
PolishAcademyofSciences
ul.Newelska6
01-447Warsaw
Poland
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ErickCantu´-Paz(Eds.) IntelligentMultimediaCommunication:Techniquesand
ParallelandDistributedComputationalIntelligence,2009 Applications,2010
ISBN978-3-642-10674-3 ISBN978-3-642-11685-8
ChangWen Chen,Zhu Li,and Shiguo Lian (Eds.)
Intelligent Multimedia
Communication: Techniques and
Applications
123
Prof.ChangWenChen Dr.ShiguoLian
ComputerScienceandEngineering FranceTelecomR&D(OrangeLabs)Beijing
UniversityatBuffalo 2ScienceInstituteSouthRd
TheStateUniversityofNewYork HaidianDistrictBeijing,100080
201BellHallBox602000 China
Buffalo,NY,14260-2000 E-mail:[email protected]
USA
E-mail:[email protected]
Prof.ZhuLi
DepartmentofComputing
HongKongPolytechnicUniversity
China
E-mail:[email protected]
ISBN 978-3-642-11685-8 e-ISBN 978-3-642-11686-5
DOI 10.1007/978-3-642-11686-5
Studiesin Computational Intelligence ISSN1860-949X
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Preface

Multimedia data are used more and more widely in human being's life, e.g., videocon-
ferencing, visual telephone, IPTV, etc. Nearly most of the applications need multime-
dia transmission techniques that send multimedia data from one side to another side
and keep the properties of efficiency, robustness and security. Here, the efficiency de-
notes the time cost of transmission operations, the robustness denotes the ability to
survive transmission errors or noises, and the security denotes the protection of the
transmitted media content. Recently, various intelligent or innovative techniques are
invented, which bring vast performance improvements to practical applications. For
example, such content transmission techniques as p2p, sensor network and ad hoc
network are constructed, which adaptively use the peers’ properties to improve the
network’s resources. Multimedia adaptation techniques can adjust the multimedia
data rate in order to compliant with the network’s bandwidth. Scalable encryption
techniques can generate the data stream that can be correctly decrypted after bit rate
conversion. Ubiquitous multimedia services make the user share any kind of content
anywhere.
To the best of our knowledge, few books focus on intelligent or innovative multi-
media transmission techniques. To access the latest research related to intelligent mul-
timedia transmission, we launched the book project where researchers from all over
the world provide the necessary coverage of the mentioned field. The primary objec-
tive of this project was to assemble as much research coverage as possible related to
the field by defining the latest innovative technologies and providing the most com-
prehensive list of research references.
The book includes sixteen chapters highlighting current concepts, issues and
emerging technologies. Distinguished scholars from many prominent research institu-
tions around the world contribute to the book. The book covers various aspects, in-
cluding not only some fundamental knowledge and the latest key techniques, but also
typical applications and open issues. For example, the covered topics include the
present and future video coding standards, stereo and multiview coding techniques,
free-viewpoint TV techniques, wireless broadcasting techniques, media streaming
techniques, wireless media transmission techniques and systems, and User-Generated
Content sharing.
The diverse and comprehensive coverage of multiple disciplines in the field of in-
telligent multimedia transmission will contribute to a better understanding of all top-
ics, research, and discoveries in this emerging and evolving field. Furthermore, the
contributions included in this book will be instrumental in the expansion of the body
of knowledge in this field. The coverage of this book provides strength to this
VI Preface
reference resource for both researchers and also decision makers in obtaining a
greater understanding of the concepts, issues, problems, trends, challenges and oppor-
tunities related to this field of study. It is our sincere hope that this publication and its
great amount of information and research will assist our research colleagues, all facul-
ties, their students, and our organizational decision makers in enhancing their under-
standing of this research field. Perhaps this publication will even inspire its readers to
contribute to the current discoveries in this immense field.

Prof. Chang Wen Chen
University at Buffalo,
The State University of New York, USA

Prof. Zhu Li
Hong Kong Polytechnic University, China

Dr. Shiguo Lian
France Telecom R&D (Orange Labs) Beijing,
China
Contents
Rate Control and Error Resilience for Object-Based Video
Coding
Paulo Nunes, Luis Ducla Soares .................................... 1
Representation and Coding Formats for Stereo and Multiview
Video
Anthony Vetro.................................................... 51
Present and Future Video Coding Standards
Jie Dong, King Ngi Ngan .......................................... 75
AVS Video Coding Standard
Wen Gao, Siwei Ma, Li Zhang, Li Su, Debin Zhao .................... 125
A Resolution Adaptive Video Compression System
Serhan Uslubas, Ehsan Maani, Aggelos K. Katsaggelos................. 167
Peer-to-Peer Streaming Systems
Yifeng He, Ling Guan ............................................. 195
Topology Construction and Resource Allocation in P2P Live
Streaming
Jacob Chakareski.................................................. 217
Intelligent Video Network Engineering with Distributed
Optimization: Two Case Studies
Ying Li, Zhu Li, Mung Chiang, A. Robert Calderbank ................. 253
Media Coding for Streaming in Networks with Source and
Path Diversity
Nikolaos Thomos, Pascal Frossard .................................. 291
Peer-Assisted Media Streaming: A Holistic Review
Yuan Feng, Baochun Li............................................ 317
X Contents
FTV (Free-Viewpoint TV)
Masayuki Tanimoto ............................................... 341
UGC Video Sharing: Measurement and Analysis
Xu Cheng, Kunfeng Lai, Dan Wang, Jiangchuan Liu .................. 367
Terrestrial Television Broadcasting in China: Technologies
and Applications
Wenjun Zhang, Yunfeng Guan, Xiaokang Yang, Weiqiang Liang ........ 403
Network Topology Inference for Multimedia Streaming
Xing Jin, S.-H. Gary Chan......................................... 425
Resolution-Improvement Scheme for Wireless Video
Transmission
Liang Zhou, Athanasios Vasilakos, Yan Zhang, Gabiel-Miro Muntean .... 443
Human-Centered Face Computing in Multimedia Interaction
and Communication
Yun Fu, Hao Tang, Jilin Tu, Hai Tao, Thomas S. Huang .............. 465
Author Index................................................... 507
Rate Control and Error Resilience for
Object-Based Video Coding
Paulo Nunes and Luis Ducla Soares
Abstract. The MPEG-4 audiovisual coding standard introduced the object-based video data
representation model where video data is no longer seen as a sequence of frames or fields, but
consists of independent (semantically) relevant video objects that together build the video
scene. This representation approach allows new and improved functionalities, but it has also
created new relevant problems in terms of typical non-normative parts of the standard, such as
rate control and error resilience, which need to be solved in order to successfully transmit ob-
ject-based video with an acceptable quality over networks that have critical bandwidth and
channel error characteristics, such as mobile networks and the Internet. To deal with the spe-
cific problems of object-based video coding, rate control demands two levels of action: 1) the
scene-level, which is responsible for dynamically allocating the available resources between the
various objects in the scene (i.e., between the different encoding time instants and the different
video objects to encode in each time instant), aiming at minimizing quality variations along
time and between the various objects in the scene; and 2) the object-level, which is responsible
for allocating the resources attributed to each object among the various types of data to code
(for that object), notably texture and shape, and for computing the best encoding parameters to
achieve the target bit allocations while maintaining smooth quality fluctuations. In terms of er-
ror resilience techniques, the object-based coding approach means that shape and composition
information also have to be taken into account for error resilience purposes, in addition to mo-
tion and texture data. To do this, at the encoder side, the coding of video objects is typically su-
pervised by a resilience configuration module, which is responsible for choosing the most ade-
quate coding parameters in terms of resilience for each video object. This is important because
the decoding performance will much depend on the protective actions the encoder has taken. At
the decoder side, defensive actions have to be taken. This includes error detection and error lo-
calization for each decoded video object, followed by independent object-level error conceal-
ment. Afterwards, advanced scene-level error concealment is also performed, which has access
to all the video objects in the scene and is used immediately before the final concealed video
scene is presented to the user. In this chapter, the most recent basics, advances and trends in
terms of rate control and error resilience for object-based video coding will be described.
1 Introduction
Some thirty years ago, digital video started to emerge. Back then, digital video was
just a digital representation of the corresponding analog video, in the sense that the
data model was the same. In fact, both analog and digital video consisted of a periodic
sequence of (rectangular) frames or fields and the sole conceptual difference between
the two models was the fact that in the analog representation each frame or field was
C.W. Chen et al. (Eds.): Intel. Multimedia Communication: Tech. and Appli., SCI 280, pp. 1–50.
springerlink.com © Springer-Verlag Berlin Heidelberg 2010
2 P. Nunes and L.D. Soares
made of a number of (analog) lines, whereas in the digital representation the frames or
fields corresponded to matrices of picture elements, also known as pixels (ITU-R
BT.601 1986). Nowadays, this type of digital video is commonly referred to as
“frame-based video”.
More recently, a different video data representation model has been introduced: the
object-based model. In this model, video data is no longer seen as a sequence of
frames or fields, but consists of several independent (semantically) relevant video ob-
jects that together build the video scene. Object-based video coding schemes can also
be called content-based video coding schemes because the representation entities in
the model — the objects — are now very close to the video content since by having
semantic value they can be subjected to semantically meaningful actions. This
representation approach allows, in addition to the advantages already provided by the
digital frame-based representation, new and improved functionalities in terms of in-
teractivity, coding efficiency and universal access since, for the first time, the content
is not only selectively processed but also independently represented, accessed, and
consumed. This video representation model was first introduced in a large scale with
the MPEG-4 standard (ISO/IEC 14496 1999).
With the advent of object-based video, new relevant problems have appeared in
various fields, since new types of data, such as the shape and the scene composition
information, have to be transmitted in addition to the motion and texture already used
in previous frame-based coding systems. Two very important fields that have been af-
fected are:
• Video coding rate control – Video coding rate control is understood here as the
mechanism responsible for efficiently controlling the video encoder in order to
guarantee that it meets the relevant constraints of the encoding framework, such as
channel, delay/buffering, complexity, and quality constraints. The challenge in
terms of video coding rate control is, therefore, to develop new methods that are
able to allocate the available coding resources among the various objects in the
scene using appropriate allocation criteria and constraining mechanisms. The ma-
jor objectives of a rate control mechanism, whatever the coding architecture, can,
therefore, be summarized as: i) regulation of the video encoder data rate accord-
ing to the application constraints; and ii) maximization of the subjective impact of
the decoded video. They involve long-term and short-term objectives. Long-term
objectives, handled at time periods of several pictures, deal with prevention of
buffer overflows and underflows (that may cause loss of data or non-optimal use of
the available resources) and preservation of coded data rate limits. Short-term ob-
jectives, handled at time periods of one or few pictures (or even short time periods,
such as a few macroblocks), deal with the minimization of picture quality varia-
tions (a key factor for maximizing the subjective impact of the decoded video) and
the stability of the encoder control.
• Video error resilience – Video error resilience is understood here as the set of
techniques that improve the capability of the video communication system to with-
stand channel errors and achieve an acceptable decoded video quality. To do this,
the error resilience of both encoder and decoder has to be improved and, therefore,
new techniques that can deal with object-based video are needed for both the en-
coder and decoder sides of the communication chain. This way, at the encoder, the