You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A Survey of Image Compression Algorithms for Visual Sensor Networks 
Chapter Title:  Introduction 
1. Abstract of Cited Paper (BIB001):  The availability of low-cost hardware such as CMOS cameras and microphones has fostered the development of Wireless Multimedia Sensor Networks (WMSNs), i.e., networks of wirelessly interconnected devices that are able to ubiquitously retrieve multimedia content such as video and audio streams, still images, and scalar sensor data from the environment. In this paper, the state of the art in algorithms, protocols, and hardware for wireless multimedia sensor networks is surveyed, and open research issues are discussed in detail. Architectures for WMSNs are explored, along with their advantages and drawbacks. Currently off-the-shelf hardware as well as available research prototypes for WMSNs are listed and classified. Existing solutions and open research issues at the application, transport, network, link, and physical layers of the communication protocol stack are investigated, along with possible cross-layer synergies and optimizations. 
2. Abstract of Cited Paper (BIB002):  A wireless sensor network with multimedia capabilities typically consists of data sensor nodes, which sense, for instance, sound or motion, and video sensor nodes, which capture video of events of interest. In this survey, we focus on the video encoding at the video sensors and the real-time transport of the encoded video to a base station. Real-time video streams have stringent requirements for end-to-end delay and loss during network transport. In this survey, we categorize the requirements of multimedia traffic at each layer of the network protocol stack and further classify the mechanisms that have been proposed for multimedia streaming in wireless sensor networks at each layer of the stack. Specifically, we consider the mechanisms operating at the application, transport, network, and MAC layers. We also review existing cross-layer approaches and propose a few possible cross-layer solutions to optimize the performance of a given wireless sensor network for multimedia streaming applications. 
3. Abstract of Cited Paper (BIB003):  Visual sensor networks have emerged as an important ::: class of sensor-based distributed intelligent systems, ::: with unique performance, complexity, and quality of service ::: challenges. Consisting of a large number of low-power camera nodes, ::: visual sensor networks support a great number of novel ::: vision-based applications. The camera nodes provide information ::: from a monitored site, performing distributed and collaborative ::: processing of their collected data. Using multiple cameras in the ::: network provides different views of the scene, which enhances ::: the reliability of the captured events. However, the large amount ::: of image data produced by the cameras combined with the ::: network's resource constraints require exploring new means ::: for data processing, communication, and sensor management. ::: Meeting these challenges of visual sensor networks requires ::: interdisciplinary approaches, utilizing vision processing, communications ::: and networking, and embedded processing. In this ::: paper, we provide an overview of the current state-of-the-art in ::: the field of visual sensor networks, by exploring several relevant ::: research directions. Our goal is to provide a better understanding ::: of current research problems in the different research fields of ::: visual sensor networks, and to show how these different research ::: fields should interact to solve the many challenges of visual sensor ::: networks. 
4. Abstract of Cited Paper (BIB004):  Wireless sensor networks are an important research area that has attracted considerable attention. Most of this attention, however, has been concentrated on WSNs that collect scalar data such as temperature and vibration. Scalar data can be insufficient for many applications such as automated surveillance and traffic monitoring. In contrast, camera sensors collect visual data, which are rich in information and hence offer tremendous potential when used in WSNs. However, they raise new challenges such as the transmission of visual data with high computational and bandwidth requirements in mainly low-power visual sensor networks. In this article we highlight the challenges and opportunities of VSNs. We discuss major research issues of VSNs, specifically camera coverage optimization, network architecture, and low-power visual data processing and communication, and identify enabling approaches in this area.

Literature Review:
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 BIB002 . 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 data scalar sensor where the compression is not required. Typically, compression is performed by exploiting data correlation and redundancy. In VSN, three scenarios of data redundancy are observed. First, redundancy between successive frames captured by the same sensor within an interval of time, which is known as interimage redundancy or temporal redundancy. Second, redundancy between neighboring sensors monitoring the same scene which is also called interimage redundancy. Finally, redundancy between neighboring pixel values of an image, called spatial redundancy. In case of color image, we note the existence of a fourth type of redundancy, called spectral redundancy. A few number of related review papers have been proposed in the literature BIB002 BIB001 BIB003 BIB004 BIB005 . An extensive survey of wireless multimedia sensor networks is provided in BIB002 , where the state of the art in algorithms and protocols at the application, BIB001 we introduce some notions behind the compressive sensing, and its possible application to VSN, (3) we provide a brief overview of each compression method, the advantages and shortcomings of their application in VSN, a literature review of their application in VSN, as well as an open research issue for each compression method, (4) we conclude by some guidelines which concern the design of a compression method for VSN. This paper is structured as follows. In Section 2, we discuss some requirements and characteristics of VSN, then we study the relationship between compression and transmission costs, and after that we suggest the classification of the main individual compression algorithms. In Section 3, we present the main idea behind DCT and some related compression algorithms in the context of VSN. The explanations of DWT, DWT-based schemes such as EZW, SPIHT, EBCOT, or SPECK, and their applications in VSN are presented in Section 4. The non-transform-based algorithms including vector quantization, Fractal compression, and their introduction in VSN is explained in Sections 5.1 and 5.2, respectively. The distributed source coding paradigm as well as some research works incorporating this paradigm in VSN is presented in Section 7. In Section 8, another paradigm called compressive sensing is presented, along with some applications in VSN context. Some guidelines for designing a compression scheme for VSN are presented in Section 9. Finally, we conclude this paper by Section 10.