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:  DCT-Based Methods for VSN. 
1. Abstract of Cited Paper (BIB001):  Wireless video-surveillance networks are gaining increasing popularity due to the number of applications they make possible. We address the problem of designing a wireless video-surveillance network so as to optimize its performance. In particular, we investigate the possible trade-offs between energy consumption and image quality. We provide simulation results showing that video compression can be very beneficial in reducing data transmission costs, provided that the energy cost of video compression is low. Moreover, we discuss the impact of compression on image delay. 
2. Abstract of Cited Paper (BIB002):  We study the problem of compression of videosurveillance sequences collected by a wireless sensor network. In particular, we propose a low-complexity coding framework based on change detection and JPEG-like compression of regions of interest, along with a suitable low-complexity change detection algorithm. We show that on typical videosurveillance sequences the performance of the proposed compression algorithm is similar to that of MPEG-2, at a much less computational cost. Energy profiling results on a TMS320VC5204 board validate the coder design for the proposed application. 
3. Abstract of Cited Paper (BIB003):  In this paper, we address the design of data processing and error control strategies for a wireless sensor network for video-surveillance applications, so as to optimize its performance in terms of energy consumption, information delivery delay, and information quality. First, we suggest a video coding strategy based on intelligent distributed processing, which yields very low power consumption. Then, we investigate the interactions between energy consumption, quality, and delay, and analyze the system performance when ARQ- and FEC-based error-control techniques are applied. As a result, we propose an optimal configuration for wireless video-surveillance networks which adapts to the radio channel state by effectively implementing FEC and ARQ techniques. 
4. Abstract of Cited Paper (BIB004):  An image sensor network platform is developed for testing transmission of images over ZigBee networks that support multi-hopping. The ZigBee is a low rate and low power networking technology for short range communications, and it currently uses IEEE 802.15.4 MAC and PHY layers. Both ZigBee networking (NWK) and IEEE 802.15.4 MAC layer protocols are implemented on a single M16C microprocessor. Transport layer functionalities such as fragmentation and reassembly are performed at the application layer, since the ZigBee NWK does not have a fragmentation support. The multiple access scheme is CSMA/CA, therefore only the best effort multi-hop transmission of JPEG and JPEG-2000 images are tested; observations and resulting statistics are presented, and open issues are discussed. 
5. Abstract of Cited Paper (BIB005):  Wireless sensor networks allow fine-grained monitoring of the environment. However, as sensors have physical limitations in energy, processing power, and memory, etc., techniques have to be developed to efficiently utilize the limited resource available in a sensor network. In this paper, we study the image transmission problem in sensor networks. Cameras are installed in various locations of a wide area to take images of targeted objects. These images have to be sent back to a centralized server, which may be very far away from the cameras. Therefore, the images have to traverse the sensors hop by hop to the server. As images usually contain a large amount of data, if they are sent individually, the communication overheads are huge. To reduce the overheads, we can pre-process the images in the sensors before sending them back to the server, but this preprocessing requires extra energy in the sensors. In this paper, we study how images can be efficiently transmitted through a sensor network. We aim at reducing the energy needed in transmitting the images while maintaining the quality of the combined image. 
6. Abstract of Cited Paper (BIB006):  The paper presents a methodology to reduce the energy consumption of visual sensor node in wireless sensor networks. This methodology, based on a balancing between processing and transmission tasks, can be applied in all those situations where subjective image based measurements are required. Considering that in a sensor node, the communication task is the most energy consumption, the proposed method tries to find the best compression ratio able to give a significant reduction of transmission time, returning adequate and objective image quality and compression time. Some results are presented applying the proposed methodology to the particular case study: the use of wireless visual sensors to the remote metering of water counters. 
7. Abstract of Cited Paper (BIB007):  Applications of wireless sensor networks have attracted a lot of attention recently. Cameras are installed in various locations of a wide area to capture images of targeted objects. Because of constraints in computational capability in these distributed cameras, it may not be feasible to analyze these images in the sensors but they have to be transmitted to a centralized server hop by hop through the sensor network. To reduce the energy used in transmission, the size of the images should be kept small by applying a large compression ratio, which may degrade image quality. This paper studies the tradeoff between image quality and energy consumption. We study the scenario that a number of camera-equipped sensors are taking pictures of the same object, and the pictures of adjacent cameras may overlap. We demonstrate that by allowing intermediate sensors to process the images and combine the overlapping portions, the total energy spent on transmission is reduced subject to a certain degradation in image quality. The tradeoff between image quality and energy consumption of different routing presents an important study on the practicability of visual sensor networks. 
8. Abstract of Cited Paper (BIB008):  Visual sensor networks are being increasingly employed as a tool for monitoring and surveillance of wide areas. Due to the relatively high power consumption characteristics of cameras, as well as their more stringent processing, storage, and communication requirements, it is important to carefully evaluate the different modes of operation of these systems, in order to devise energy–aware resource management policies. The ultimate goal is to deliver adequate application–level performance (e.g., maximize the probability of detecting events), yet maximally prolonging the system’s operational lifetime. In this paper we present an accurate power consumption analysis for the different elementary tasks forming the duty cycle of a visual sensor node in a wireless camera network testbed. We also present a number of different duty cycle configurations, and provide direct energy consumption measurements for each one of them. Based on the energy consumption characterization we conducted for the elementary visual sensing tasks, we explore the possibility of predicting the lifetime of a visual sensor network system. 
9. Abstract of Cited Paper (BIB009):  Energy-efficient image communication is one of the most important goals for a large class of current and future sensor network applications. This paper presents a quantitative comparison between the energy costs associated with 1) direct transmission of uncompressed images and 2) sensor platform-based JPEG compression followed by transmission of the compressed image data. JPEG compression computations are mapped onto various resource-constrained sensor platforms using a design environment that allows computation using the minimum integer and fractional bit-widths needed in view of other approximations inherent in the compression process and choice of image quality parameters. Detailed experimental results examining the tradeoffs in processor resources, processing/transmission time, bandwidth utilization, image quality, and overall energy consumption are presented. 
10. Abstract of Cited Paper (BIB010):  Wireless sensor networks are under active research for tracking systems, where camera nodes are installed in a large area to take images of a targeted object. In this paper, we consider the scenario where the sensors around the object of interest capture images of it. Since some sensors are in similar viewing directions, the images they capture likely exhibit certain levels of correlation among themselves. It is a waste of transmission energy if we blindly send all images to the sink without checking for redundancy. We develop a protocol for involved sensors to determine how to select and transmit the images to the mobile sink in an energy efficient manner. The simulation results show that our protocol can achieve a significant reduction in energy consumption while preserving most of the viewing directions

Literature Review:
The adoption of JPEG as a compression tool in VSN is not very beneficial in terms of power consumption  . This is due to the relatively complex coder, and precisely to the DCT stage which consumes at least 60% of the whole power encoder. Our preliminary studies on JPEG show the possibility of its application as a compression tool for VSN images at the cost of decreasing the network lifetime  . Another confirmation comes from BIB005 , where the authors show the possibility to integrate successfully JPEG as a compression scheme for VSN. In what follows, we briefly present the main DCT-based schemes for VSN. We begin this section by the work presented in BIB003 , where the authors study the problem of compression of video-surveillance frames collected by WSN. They use an algorithm to build a map of active regions within one frame, and, then, they encode these regions. In particular, each input frame is divided into blocks of 8 × 8 pixels. In order to decrease the complexity, only a subset of blocks in the frame are considered, and only a subset of the pixels in each block are classified in the order of their importance. These pixels are then examined for changes in comparison to the corresponding pixels in the reference frame. Only the difference is encoded using JPEG-like scheme. In particular, a fast integer DCT and Golomb-Rice codes are used, since they exhibit low complexity and less power dissipation. The authors in BIB010 suggest an energy-optimized approach ensuring that the JPEG computations utilize the minimum precision needed to obtain optimized DCT and quantization. To accomplish this, they develop a method that determines the optimum integer and fractional bit widths in the compression process which guarantees the required precision. Moreover, to be fast in computations, the authors use the LLM algorithm developed in BIB001 . We mention that the authors in BIB010 implement the JPEG computations in fixedpoint arithmetic instead of floating-point representation for energy considerations. Several experimentations are performed using various processors to measure the energy savings resulting from the precision optimization process. These processors are Atmel ATmega128, TI MSP430, TI TMS320C64x, and Analog Devices Blackfin ADSPBF533. The suggested method outperforms JPEG in terms of speed and energy. The authors observe that the Atmel ATmega128 consumes the highest energy compared to the processors under evaluation. The work in BIB002 investigates the trade-off between image quality and power consumption in wireless video-surveillance networks. To reduce computation energy dissipation, they use JPEG with integer DCT kernel instead of the commonly used floating-point DCT. Moreover, they discuss lightly the impact of compression on image delay using ARQ scheme implemented at the link layer BIB002 . In BIB004 , the same authors in BIB002 investigate the interactions between energy consumption, quality, and delay and analyze the system performance when ARQ and FEC-based error-control techniques are applied. As in BIB002 , they use JPEG with integer DCT to reduce computation energy dissipation. Precisely, they investigate ARQ and FEC as error-recovery techniques and propose an adaptive scheme, which selects the most appropriate error-control technique depending on the channel propagation conditions. An image sensor network platform was developed in BIB005 to show the feasibility of transmitting the compressed images over multi-hop sensor networks supporting ZigBee technology. The compression was performed using the standards JPEG and JPEG2000. The comparison between the two standards was performed only in terms of tolerance to bit errors and packet losses. The authors observed that JPEG2000 is more error-resilient than JPEG, while having the highest PSNR for the same compression ratio. Hence, they conclude that JPEG2000 is a more suitable standard for image compression in VSN in terms of packet losses. We highlight that the predominant design factor in VSN, that is, energy, was not considered, and their evaluation seems to be not practical for all VSN applications, especially outdoor applications. The authors in BIB008 study the trade-off between energy consumption and image quality when different routing paths are used. Particularly, they study the effect of the proximity of the sink on the energy overhead. For compression purpose, the authors use the standard JPEG. To control the compression rate, the quality level parameter is used. The higher the quality level, the better the image quality, but with a larger file size. To reduce the image quality the number of quantization levels is reduced. Contrary to BIB008 , where the authors study the trade-off between energy consumption and image quality, in BIB011 they deal with the trade-off between energy consumption and the covered viewing directions in VSN. A selective transmission protocol is developed to select and transmit images to the mobile sink in an energy efficient way. To do that, similarity score between images is computed and compared. To perform similarity between images, nodes perform feature extraction procedure on the captured images. To save transmission energy, only the image having the most number of feature points among the similar images will be transmitted. For compression purpose, the authors in BIB011 use the standard JPEG. As in BIB008 , the authors study the effect of the proximity of the sink on the energy overhead. The simulation results show that the protocol can achieve a significant reduction in energy consumption while preserving most of the views. Despite the saving in energy transmission, feature extraction and comparison seems to be energy consuming. Moreover, the authors do not compare the transmission with and without feature extraction. The aim of BIB006 is the reduction of the transmission energy through the selection of appropriate paths and appropriate compression of images. They use the standard JPEG in the compression stage. First, they demonstrate that the amounts of energy required in different forwarding paths are different. Then, they develop an algorithm to select a path that requires the least energy. The authors in BIB009 present an analysis of both power requirement and execution time of the basic tasks that compose a typical duty cycle of a camera node within a real VSN. For that reason, a Crossbow Stargate platform is used along with Logitech QuickCam Pro 4000 webcam. Among tasks considered in BIB009 , we cite acquisition and compression. The authors use JPEG as a compression standard to compress images or subimages. Each considered task has an associated power dissipation cost and execution time. Numerous interesting results are observed. For instance, the time needed to acquire and compress an image is 2.5 times larger than that of the transmission of the compressed image. The authors also show that the transmission and reception consume about the same amount of energy. Moreover, the power cost of analyzing an image, and compressing a subimage, is about the same as compressing the whole image. Another interesting work is presented in BIB007 , where the authors address the problem of reducing energy consumption. The authors' aim is to find the most efficient compression algorithm achieving the best compromise between the quality of the reconstructed image and the energy consumption. Their analysis is conducted from the measurements results of the current consumption for each state: standby, sensing, processing, connection, and communication. For that reason, several compression methods are considered, namely, JPEG, JPEG2000, SPIHT, and subsampling. They realize that the most appropriate compression methods are SPIHT, which gives the highest compression rate, and subsampling, which requires the smallest execution time. In the following section, we present the alternative solution to DCT, that is, DWT, which represents a promising technique for image compression.