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: IMPLEMENTATION METHOD ON MEDICAL IMAGE COMPRESSION SYSTEM: A REVIEW 
Chapter Title:  Figure 12 
1. Abstract of Cited Paper (BIB001):  In order to solve the compute-intensive character of image processing, based on advantages of GPU parallel operation, parallel acceleration processing technique is proposed for image. First, efficient architecture of GPU is introduced that improves computational efficiency, comparing with CPU. Then, Sobel edge detector and homomorphic filtering, two representative image processing algorithms, are embedded into GPU to validate the technique. Finally, tested image data of different resolutions are used on CPU and GPU hardware platform to compare computational efficiency of GPU and CPU. Experimental results indicate that if data transfer time, between host memory and device memory, is taken into account, speed of the two algorithms implemented on GPU can be improved approximately 25 times and 49 times as fast as CPU, respectively, and GPU is practical for image processing. 
2. Abstract of Cited Paper (BIB002):  Convolution is one of the most important operators used in image processing. With the constant need to increase the performance in high-end applications and the rise and popularity of parallel architectures, such as GPUs and the ones implemented in FPGAs, comes the necessity to compare these architectures in order to determine which of them performs better and in what scenario. In this article, convolution was implemented in each of the aforementioned architectures with the following languages: CUDA for GPUs and Verilog for FPGAs. In addition, the same algorithms were also implemented in MATLAB, using predefined operations and in C using a regular x86 quad-core processor. Comparative performance measures, considering the execution time and the clock ratio, were taken and commented in the paper. Overall, it was possible to achieve a CUDA speedup of roughly 200× in comparison to C, 70× in comparison to Matlab and 20× in comparison to FPGA. 
3. Abstract of Cited Paper (BIB003):  The aim of this work is to develop, validate and preliminarily evaluate two graphic processing units (GPU) implementations of the OS-EM iterative image reconstruction algorithms for a clinical SPECT system. The Monte Carlo software SIMSET was used to simulate the data acquisition of a typical SPECT camera with a parallel-hole collimator for the Shepp-Logan phantom. CUDA was adopted for the GPU programming of the OS-EM algorithm with high complexity. For the first implementation, the system matrix used for reconstruction was pre-computed by Monte Carlo simulation. A major task in the GPU programming in this case was to efficiently access every element of the pre-stored system matrix and compute the projection and back-projection for each image update. In the second implementation, the system matrix was calculated on-the-fly. A rotation-based projector and backprojector was employed, which reduced the complexity for the GPU programming and can achieve higher efficiency theoretically than other methods of computing projector and back-projectors. We modeled the depth-dependent collimator detector response in the matrix computation. The results showed that, for both GPU implementations, there are seldom differences of images reconstructed by the GPU and CPU implementation of the OS-EM algorithm. In the first matrix computation strategy, an acceleration ratio of 31.9 was achieved by using the GPU implementation compared with the CPU implementation of the same algorithm. In the second strategy, the acceleration ratio was 33.9. The images reconstructed with the first strategy demonstrated higher quantitative accuracy due to the more accurate modeling of the system matrix. But the corresponding reconstructed time was more than the second strategy . We also applied the second GPU implementation of the OS-EM method to clinical SPECT patient cardiac data and achieved the reconstruction of a 64×64×64 image within 8 seconds. Compared with the FBP reconstruction image, the OS-EM reconstruction image had a better performance. In conclusion, GPU implementation of the OS-EM algorithm has a great potential in SPECT image reconstruction. 
4. Abstract of Cited Paper (BIB004):  In positron emission tomography (PET), 3D iterative image reconstruction methods have a huge computational burden. In this paper, we developed a list-mode image reconstruction method using graphics processing units (GPUs). Efficiency of acceleration for GPU implementation largely depends on the method chosen, where a reduced number of conditional statements and a reduced memory size are required. On the other hand, accurate system models are required to improve the quality of reconstructed images. Various accurate system models for conventional CPU implementation have been proposed, but these models basically require many conditional statements and huge memory size. Therefore, we developed a new system model which matches GPU implementation better. In this model, the detector response functions, which vary depending on each line of response (LOR), are pre-computed in CPUs and modeled by sixth-order polynomial functions in order to reduce the memory size occupied in GPUs. Each element of a system matrix is obtained on-the-fly in GPUs by calculating the distance between an LOR and a voxel. Therefore the developed system model enables efficient GPU implementation of the accurate system modeling with a reduced number of conditional statements and a reduced memory size. We applied the developed method to a small OpenPET prototype, in which 4-layered depth-of-interaction (DOI) detectors were used. For image reconstruction, we used the dynamic row-action maximum likelihood algorithm (DRAMA). Compared with a conventional model for GPU implementation, in which DRFs are given as a Gaussian function of fixed width, we saw no remarkable difference for DOI data, but for non-DOI data the proposed model outperformed the conventional at the peripheral region of the field-of-view. The proposed model had almost the same calculation time as the conventional model did. For further acceleration, we tried parallel GPU implementation, and we obtained 3.8-fold acceleration by using 4 GPUs. 
5. Abstract of Cited Paper (BIB005):  With the advances in understanding perceptual properties of the human visual system and constructing their computational models, efforts toward incorporating human perceptual mechanisms in video compression to achieve maximal perceptual quality have received great attention. This paper thoroughly reviews the recent advances of perceptual video compression mainly in terms of the three major components, namely, perceptual model definition, implementation of coding, and performance evaluation. Furthermore, open research issues and challenges are discussed in order to provide perspectives for future research trends. 
6. Abstract of Cited Paper (BIB006):  Now-a-days the medical image compression plays a major role in the image processing. Even though the technology is improvised but still it needs the storage space and efficient bandwidth utilization. This paper presents the compression of grey scale medical images. The proposed method uses a new transform called Ripplet Transform, a higher dimensional generalization of the curvelet transform used to represent the images or two dimensional signals at different scales and different directions and Huffman algorithm to encode significant coefficients. The main goal of this method is to improve the compression ratio and to minimize the mean square error. From the experimental results obtained it is proved that the compression ratio and the peak to signal noise ratio is achieved high for various Medical images.

Literature Review:
General block diagram of CABAC  For integration approach, shows a variety of architecture, algorithm, software, and hardware had been used. CUDA is a new software as a platform to the programming model for parallelizing in order to communicate with GPU. CUDA is created by NVIDIA and based on C language, where ISE based on VHDL and Verilog. GPU shows the performance of computing speed increased proportionally with increasing of image size. Meaning that GPU outperforming CPU approximately 25 and 49 times fast BIB002 . A-part from that, CUDA had revealed the best performance in execution time, a number of clock cycles and speedup in comparison to C, MATLAB and implementation of FPGAs BIB003 . Entropy coding is the last stage of image compression blocks, CABAC one of the familiar arithmetic codings had been used. It's used H.264/AVC standard with lossless compression techniques. Finding of research shows, it has offered 10-15% better compression results than obtained with the CAVLC. Another study by Alex et al. BIB001 proposed a technique to investigate and compare some of these basic operations (direct transformations, indirect transformations, and computation of partial derivatives) as follow: (1) recursive filter based implementations in MATLAB and C++ and (2) GPUaccelerated implementations in CUDA. All operations were compared to a variety of resolution levels on a 2-D panoramic image as well as a 3-D magnetic resonance (MR) image. Results indicated significant improvements in efficiency for the CUDA implementations. Their experiment was conducted using Intel Core i7-2600 CPU at 3.40GHz and 16GB of RAM. The GPU used was a NVIDIA Tesla C2070 with 4GB of global memory at 1494 MHz and a core clock of 1.147 GHz. Pingfan et al. [79] presented the technique of GPU implementation of the multi-frequency bi-planar inter-imaging (MBI) algorithm. In order to execute this technique, an efficient memory access arrangement and a balanced parallel thread assignment were required. Furthermore, GPU would provide threads as the parallel processing unit. Different sets of data were processed but threads concurrently shared the same instruction. Once MBI algorithm was implemented in GPU, every three element estimation operation would be assigned to one thread. Global memory was generally a major bottleneck issue for GPU implementations. In order to minimize this impact on the performance, a memory coalescing technique was proposed. The input data and the intermediate data were stored in the registers as opposed to reading them again from the global memory. The experiments were performed using Intel i7 quad-core 3.4GHz workstation equipped with GTX590 GPU. Hui et al. BIB004 implemented the OS-EM iterative image reconstruction algorithms for a clinical SPECT system. The Monte Carlo software SIMSET was used and CUDA was adopted for the GPU programming of the OS-EM algorithm with high complexity. Two BIB007 implementations in this experiment, accessed every element of the pre-stored system matrix and the system matrix was calculated on-the-fly. Finally, it was evident that, for both GPU implementations, there were no significant differences in images reconstructed by the GPU and CPU implementations of the OS-EM algorithm but for the FBP reconstruction image, the OS-EM reconstruction image had a better performance. The experiments were conducted on a system with Intel ® Core ™ i7 CPU and NVIDIA Tesla C1060 GPU. Shoko et al. BIB005 presented a method for parallelization. Ray-driven and voxel-driven methods were categories of voxel searching method, in the projection calculation. For the forward projection, most of the former time were used, while the latter was used in back-projection. However, for the listmode reconstruction in GPU implementation, by using the ray-driven method was smaller the calculation cost in the back projection rather than used voxel-driven method because the ray-driven method searched only the voxels traversed by the measured line of response (LORs). Therefore, to address these issues, the ray-driven method was implemented in both forward and back projections. The projection value of each LOR was calculated in each thread for the forward projection. For the back projection, the ray-driven was used, a memory access confliction had to be prevented because at the same time some threads may access the same voxel address. Finally, calculated voxels for each LOR were grouped by slice number and the calculations for grouped voxels were included in the same thread in the back projection. This evaluation was performed using 2.67GHz Intel Quad-Core Xeon CPU and NVIDIA Tesla C1060 GPU. In order to provide a brief snapshot of existing techniques, Figure 13 and Table 2 lists representative samples of 3-D medical image compression along with their key ideas. It can be observed that various elements and techniques are used in the three components, i.e., types of compression, implementation, and evaluation. In the following, existing work is extensively reviewed from the viewpoint of each of these three components. To our best knowledge, there has been no systematic subjective test conducted a comparison of the objective and subjective test BIB006 . In the table, we include only the cases where used data from medical imaging such as MRI, US, CT and X-Ray. This list justifies the significant amount of work that needs to be carried out on hardware development of 3-D medical imaging.