such as graceful degradation in lossy transmission environments as well as bit rate, format, and power adaptation. These functionalities provide enhancements to transmission and storage applications. SVC has achieved significant improvements in coding efficiency with an increased degree of supported scalability

Wireless visual sensor networks are potentially applicable for several emerging applications. Since the resource-limited restriction for a visual sensor node (VSN), efficient resource allocation for video compression is challenging. In this paper, a power-rate-distortion (PRD) model-based low-complexity

The problem of error control and concealment in video communication is becoming increasingly important because of the growing interest in video delivery over unreliable channels such as wireless networks and the Internet. This paper reviews the techniques that have been developed for error control

, scalability, and customizability. To address these issues, researchers are investigating a variety of innovative approaches. The most promising and intriguing ones are those based on the ability of moving code across the nodes of a network, exploiting the notion of mobile code. As an emerging research field

this article. Some further techniques which go somewhat beyond this model will also be discussed. [Begin Sidebar Inset Article #1] A History of Existing Visual Coding Standards

Distributed coding is a new paradigm for video compression,

theoretical guideline for performance optimization under limited resource constraints. Both the analytic and simulation results have shown the applicability of our video coding scheme for WMSNs. Index Terms—Low-complexity video coding, multiview video coding, resource-scalable video coding, power-rate-distortion

We propose a new scheme for content distribution of large files that is based on network coding. With network coding, each node of the distribution network is able to generate and transmit encoded blocks of information. The randomization introduced by the coding process eases the scheduling

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reduces the complexity of SVMs to O(n) in training and a constant in testing. In a number of image categorization experiments, we find that, in terms of classification accuracy, the suggested linear SPM based on sparse coding of SIFT descriptors always significantly outperforms the linear SPM kernel