Distributed Video Coding and Content Analysis for Resource Constraint Multimedia Applications

Distributed Video Coding and Content Analysis for Resource Constraint Multimedia Applications

Praveen Kumar (GRIET, India), Amit Pande (University of California Davis, USA), Ankush Mittal (College of Engineering Roorkee, India) and Abhisek Mudgal (Iowa State University, USA)
DOI: 10.4018/978-1-61350-107-8.ch011
OnDemand PDF Download:
List Price: $37.50


Video coding and analysis for low power and low bandwidth multimedia applications has always been a great challenge. The limited computational resources on ubiquitous multimedia devices like cameras along with low and varying bandwidth over wireless network lead to serious bottlenecks in delivering real-time streaming of videos for such applications. This work presents a Content-based Network-adaptive Video-transmission (CbNaVt) framework which can waive off the requirements of low bandwidth. This is done by transmitting important content only to the end user. The framework is illustrated with the example of video streaming in the context of remote laboratory setup. A framework for distributed processing using mobile agents is discussed with the example of Distributed Video Surveillance (DVS). In this regard, the increased computational costs due to video processing tasks like object segmentation and tracking are shared by the cameras and a local base station called as Processing Proxy Server (PPS).However, in a distributed scenario like traffic surveillance, where moving objects is tracked using multiple cameras, the processing tasks needs to be dynamically distributed. This is done intelligently using mobile agents by migrating from one PPS to another for tracking an individual case object and transmitting required information to the end users. Although the authors propose a specific implementation for CbNaVt and DVS systems, the general ideas in design of such systems exemplify the way information can be intelligently transmitted in any ubiquitous multimedia applications along with the use of mobile agents for real-time processing and retrieval of video signal.
Chapter Preview


The demand for distributed multimedia services such as distributed video surveillance (Valera & Velastin, 2005; Micheloni, Foresti, & Snidaro, 2003) and real-time remote laboratory (Mittal, Pande, & Kumar, 2010; Kikuchi, et al., 2004) are rapidly increasing. In addition, better and better quality of these services is expected. Although the technology is continuously progressing and pushing up the bandwidth limit and reducing the transmission and storage cost, the channel bandwidths and storage capabilities are limited and relatively expensive in comparison with the volume of multimedia data required in such applications. The rapid emergence of wireless networks and proliferation of networked digital video cameras have favorably increased the opportunity of developing a low cost Distributed Video Communication (DVC) framework for mobile and pervasive multimedia applications. Availability of network IP-based digital video camera improved accessibility for both collection and distribution of video data. Despite their significant advantages, IP cameras with wired networking suffered with limited flexibility and high installation cost. But the development of wireless infrastructure has provided great flexibility in connecting IP cameras to a wireless network with considerable reduction in infrastructure, maintenance and operational cost.

Building a DVC network is very demanding because of several challenges. The main concern is the limited bandwidth resource provided by the existing wireless and wired network technologies for continuous transmission of multimedia information from the guarded sites to the network head-end (Mahonen, 1999) (i.e. upstream transmission). The transmission of JPEG-compressed RGB color image sequence at a frame-rate of 5 fps, with a resolution of 512 x 512 x 3pixels and compression of 16% requires an upstream transmission rate of about 1.9 Mb/s (Sacchi & Regazzoni, 1999) which is very rarely available even in most advanced wireless or wired network infrastructures. Moreover, the upstream bandwidth available is generally much smaller than the downstream one. This necessitates a scalable and network aware video streaming from the server to the end users. Thus, the design of an efficient DVC framework addressing the dual-folded requirements of efficient compression and transmission over bandwidth constrained networks along with low power and computation overheads is desirable. A Content-based Network-adaptive Video-transmission (CbNaVt) framework is presented that addresses the dual trade-offs between computation and compression efficient requirements of the system by incorporating content based classification, rate-scalable compression, and network-aware transmission.

Figure 1 gives a brief conceptual overview of our DVC architecture. Being essentially a multimedia encoding framework, it includes a transform step, an encoding scheme and a network transmission scheme. The DVC system uses the virtues of content-based encoding to obtain a highly scalable and tagged Visual Object (VO) representation of the input video. Different VOs in the frame are classified individually. Several parameters about the VO are then obtained to yield a highly scalable structure. Discrete Wavelet transform (DWT) has been used as the transform step to add to this scalability. The transmission scheme is desired to parallel the network performance in terms of bandwidth and throughput. We obtain a set of Transmission Control (TC) parameters from the encoding scheme that characterize a VO. Performance feedbacks of the network such as estimation of network bandwidth are needed to select the required data throughput of the system. The data throughput of the system is thus decided by considering the current network parameters and the importance and needs of VO with respect to the user perception. This tradeoff has been presented in this chapter. Multimedia streaming requires high network bandwidth. While, in many cases, the user may have specific preferences, the multimedia coding schemes don't in general allow for content based compression and transmission.

Figure 1.

Conceptual overview of the CbNaVt framework

Complete Chapter List

Search this Book: