129,043 research outputs found
Multimedia data transmission for mobile wireless applications
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file.Title from title screen of research.pdf file viewed on (November 14, 2006)Includes bibliographical references.Vita.Thesis (Ph. D.) University of Missouri-Columbia 2005.Dissertations, Academic -- University of Missouri--Columbia -- Electrical engineering.In this dissertation, we first address robust multimedia data transmission for mobile application. The first topic is proxy-based handheld device access to live NASA satellite weather images. The second topic is a real time easy-to-use 3D volume visualization system on mobile handheld devices. We also address energy efficient transmission for mobile application. We proposed two image transmission schemes. The first scheme is a collaborative image transmission scheme. The second scheme is multiple bit stream image encoding and small fragment burst transmission system. Finally, we address the research of applying distributed source coding in image and video coding. We show that applying distributed source coding in multiple description image coding improves the error resilience, and our syndrome-based video encoding scheme provides low complexity video encoder that is very desirable for mobile wireless application
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Multimedia delivery in the future internet
The term “Networked Media” implies that all kinds of media including text, image, 3D graphics, audio
and video are produced, distributed, shared, managed and consumed on-line through various networks,
like the Internet, Fiber, WiFi, WiMAX, GPRS, 3G and so on, in a convergent manner [1]. This white
paper is the contribution of the Media Delivery Platform (MDP) cluster and aims to cover the Networked
challenges of the Networked Media in the transition to the Future of the Internet.
Internet has evolved and changed the way we work and live. End users of the Internet have been confronted
with a bewildering range of media, services and applications and of technological innovations concerning
media formats, wireless networks, terminal types and capabilities. And there is little evidence that the pace
of this innovation is slowing. Today, over one billion of users access the Internet on regular basis, more
than 100 million users have downloaded at least one (multi)media file and over 47 millions of them do so
regularly, searching in more than 160 Exabytes1 of content. In the near future these numbers are expected
to exponentially rise. It is expected that the Internet content will be increased by at least a factor of 6, rising
to more than 990 Exabytes before 2012, fuelled mainly by the users themselves. Moreover, it is envisaged
that in a near- to mid-term future, the Internet will provide the means to share and distribute (new)
multimedia content and services with superior quality and striking flexibility, in a trusted and personalized
way, improving citizens’ quality of life, working conditions, edutainment and safety.
In this evolving environment, new transport protocols, new multimedia encoding schemes, cross-layer inthe
network adaptation, machine-to-machine communication (including RFIDs), rich 3D content as well as
community networks and the use of peer-to-peer (P2P) overlays are expected to generate new models of
interaction and cooperation, and be able to support enhanced perceived quality-of-experience (PQoE) and
innovative applications “on the move”, like virtual collaboration environments, personalised services/
media, virtual sport groups, on-line gaming, edutainment. In this context, the interaction with content
combined with interactive/multimedia search capabilities across distributed repositories, opportunistic P2P
networks and the dynamic adaptation to the characteristics of diverse mobile terminals are expected to
contribute towards such a vision.
Based on work that has taken place in a number of EC co-funded projects, in Framework Program 6 (FP6)
and Framework Program 7 (FP7), a group of experts and technology visionaries have voluntarily
contributed in this white paper aiming to describe the status, the state-of-the art, the challenges and the way
ahead in the area of Content Aware media delivery platforms
Multiple-Tree Push-based Overlay Streaming
Multiple-Tree Overlay Streaming has attracted a great amount of attention
from researchers in the past years. Multiple-tree streaming is a promising
alternative to single-tree streaming in terms of node dynamics and load
balancing, among others, which in turn addresses the perceived video quality by
the streaming user on node dynamics or when heterogeneous nodes join the
network. This article presents a comprehensive survey of the different
aproaches and techniques used in this research area. In this paper we identify
node-disjointness as the property most approaches aim to achieve. We also
present an alternative technique which does not try to achieve this but does
local optimizations aiming global optimizations. Thus, we identify this
property as not being absolute necessary for creating robust and heterogeneous
multi-tree overlays. We identify two main design goals: robustness and support
for heterogeneity, and classify existing approaches into these categories as
their main focus
Compensating for motion estimation inaccuracies in DVC
Distributed video coding is a relatively new video coding approach, where compression is achieved by performing motion estimation at the decoder. Current techniques for decoder-side motion estimation make use of assumptions such as linear motion between the reference frames. It is only after the frame is partially decoded that some of the errors are corrected. In this paper, we propose a new approach with multiple predictors, accounting for inaccuracies in the decoder-side motion estimation process during the decoding. Each of the predictors is assigned a weight, and the correlation between the original frame at the encoder and the set of predictors at the decoder is modeled at the decoder. This correlation information is then used during the decoding process. Results indicate average quality gains up to 0.4 dB
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