With the advent of visual sensor networks (VSNs), energy-aware compression algorithms have gained wide attention. That is, new strategies and mechanisms for power-efficient image compression algorithms are developed, since the application of the conventional methods is not always energy beneficial. In this paper, we provide a survey of image compression algorithms for visual sensor networks, ranging from the conventional standards such as JPEG and JPEG2000 to a new compression method, for example, compressive sensing. We provide the advantages and shortcomings of the application of these algorithms in VSN, a literature review of their application in VSN, as well as an open research issue for each compression standard/method. Moreover, factors influencing the design of compression algorithms in the context of VSN are presented. We conclude by some guidelines which concern the design of a compression method for VSN. 1. Introduction Recent advances in microelectromechanical systems, wireless communication technology together with low-cost digital imaging cameras, have made it conceivable to build in an ad hoc way a wireless network of visual sensors (VSs), called visual sensor network (VSN). Inside a VSN, each VS node has the ability to acquire, compress, and transmit relevant frames to the base station, also called sink, through the path between the source and the sink; see Figure 1. Generally, the base station is defined as a powerful collecting information node located far away from the other (nonpowerful) nodes. Such networks have a myriad of potential applications, ranging from gathering visual information from harsh environment to monitoring and assisting elderly peoples [1]. Figure 1: Visual sensor network. Unlike classical wired networks and scalar data wireless sensor networks (WSNs), VSN faces new additional challenges. Compared to conventional wired networks, VSNs encounter more problems due to their inherent wireless nature and the resource constrained of VS. VSNs differ from their predecessor’s scalar WSN basically in the following points. (1) The nature and the volume of visual flows, which are pixel based, are quite different from simple scalar data manipulated by WSN, such as temperature or humidity. (2) VSN’s cameras have a restricted directional sensing field of view, which is not the case for scalar data sensor. (3) Contrary to WSN, important resources in memory, processing, and communication power are required for VS nodes to manipulate visual flows. (4) Energy-aware compression algorithms are mandatory to handle images, compared to
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