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:  ISC Summary 
1. Abstract of Cited Paper (BIB001):  We propose a hardware accelerator IP for the Tier-1 portion of Embedded Block Coding with Optimal Truncation (EBCOT) used in the JPEG2000 next generation image compression standard. EBCOT Tier-1 accounts for more than 70% of encoding time due to extensive bit-level processing. Our architecture consists of a 16-way parallel context formation module and a 3-stage pipelined arithmetic encoder. We reduce power consumption by properly shutting down parts of the circuit. Compared with the known best design, we reduce 17% of the cycle count and reach a level within 5% of the theoretical lower bound. We have implemented the design in synthesizable Verilog RTL with an AMBA-AHB interface for SOC design. FPGA prototyping has been successfully demonstrated and substantial speedup achieved. 
2. Abstract of Cited Paper (BIB002):  The impact of using different lossy compression algorithms on the matching accuracy of fingerprint and face ::: recognition systems is investigated. In particular, we relate rate-distortion performance as measured in PSNR ::: to the matching scores as obtained by the recognition systems. JPEG2000 and SPIHT are correctly predicted ::: by PSNR to be the most suited compression algorithms to be used in fingerprint and face recognition systems. ::: Fractal compression is identified to be least suited for the use in the investigated recognition systems, although ::: PSNR suggests JPEG to deliver worse recognition results in the case of face imagery. JPEG compression performs ::: surprisingly well at high bitrates in face recognition systems, given the low PSNR performance observed. 
3. Abstract of Cited Paper (BIB003):  This paper presents a very low-memory wavelet compression architecture for implementation in severely constrained hardware environments such as wireless sensor networks (WSNs). The approach employs a strip-based processing technique where an image is partitioned into strips and each strip is encoded separately. To further reduce the memory requirements, the wavelet compression uses a modified set-partitioning in hierarchical trees (SPIHT) algorithm based on a degree-0 zerotree coding scheme to give high compression performance without the need for adaptive arithmetic coding which would require additional storage for multiple coding tables. A new one-dimension (1D) addressing method is proposed to store the wavelet coefficients into the strip buffer for ease of coding. A softcore microprocessor-based hardware implementation on a field programmable gate array (FPGA) is presented for verifying the strip-based wavelet compression architecture and software simulations are presented to verify the performance of the degree-0 zerotree coding scheme. 
4. Abstract of Cited Paper (BIB004):  This is nothing less than a totally essential reference for engineers and researchers in any field of work that involves the use of compressed imagery. Beginning with a thorough and up-to-date overview of the fundamentals of image compression, the authors move on to provide a complete description of the JPEG2000 standard. They then devote space to the implementation and exploitation of that standard. The final section describes other key image compression systems. This work has specific applications for those involved in the development of software and hardware solutions for multimedia, internet, and medical imaging applications.

Literature Review:
A brief summary is introduced in this section to show the best compression algorithms that possibly fit VSN requirements. Of the aforementioned discussed standards and algorithms, few of them could be a good candidate for VSN. The selection criterion is based mainly on the low power dissipated by a VS running one of compression algorithms in question, while having an adequate quality of the reconstructed image at the sink. The second criterion may be the low memory usage. It is difficult to say that one algorithm is less in power dissipation than another one without an evaluation on real testbed. Let us start this discussion by the non-transform-based algorithms such as fractals and VQ. The main drawback of fractal image compression is related to the encoding process which is extremely computationally intensive and time consuming. This is due to the hard tasks of finding all fractals during the partition step and the search for the best match of fractals. The authors in BIB003 compare fractals with other schemes and their impact on fingerprint and face recognition. They found poorer PSNR results with fractals compared to other methods such as JPEG, JPEG2000, SPIHT, and VQ, specially with low bit rate. More details can be found in BIB003 . The basic disadvantage of VQ is its complexity, which increases with the increasing of vector dimension. This comlexity may decrease the coding speed and increase the power dissipation of the decoder especially in power-constrained applications such as VSN. Another disadvantage of VQ is related to the design of a universal codebook for a large database of images, which requires an important memory and huge number of memory accesses. From the pravious discussion and some experiments BIB003 , DCT-and DWT-based methods seem to be a relatively less energy dissipation than VQ and fractals. Depending on the compression ratio and the image quality, one shall select between DCT or DWT methods. DCT exhibits an annoying blocking artifacts in low bit rate. For DWT-based methods, SPECK has proven its efficiency in terms of both simplicity and image quality measure, followed by SPIHT and EZW BIB001 . However, the EBCOT algorithm requires additional memory requirement, which increases the dissipated energy and the computational complexity. Precisely, EBCOT uses two tiers: Tier-1 and Tier-2 to code information, which needs long time processing and high power consumption BIB005 . More precisely, it is reported in BIB002 that Tier-1 accounts for more than 70% of encoding time due to extensive bitlevel processing, followed by the DWT transformation stage. From the viewpoint of hardware implementation, SPIHT is preferred over EBCOT coding BIB004 . After the examination of the main ISC compression schemes suggested in the literature, we present in the following section a small review on the distributed source coding (DSC) paradigm.