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:  CLASSIFICATION OF COMPRESSION METHODS 
1. Abstract of Cited Paper (BIB001):  Three dimensional MRI images which are power tools for diagnosis of many diseases require large storage space. A number of lossless compression schemes exist for this purpose. In this paper we propose a new approach for the compression of these images which exploits the inherent symmetry that exists in the 3D MRI images. A block matching routine is employed to work on the symmetrical characteristics of these images. Another type of block matching is also applied to eliminate the inter-slice temporal correlations. The obtained results outperform the existing standard compression techniques. 
2. Abstract of Cited Paper (BIB002):  Image compression has become one of the most important disciplines in digital electronics because of the ever growing popularity and usage of the internet and multimedia systems combined with the high requirements of the bandwidth and storage space. The increasing volume of data generated by some medical imaging modalities which justifies the use of different compression techniques to decrease the storage space and efficiency of transfer the images over the network for access to electronic patient records. This paper addresses the area of data compression as it is applicable to image processing. Here we are presented an effective algorithm to compress and to reconstruct digital imaging and communications in medicine (DICOM) images. Various image compression algorithms exist in today's commercial market. This paper outlines the comparison of compression methods such as JEPG, JEPG 2000 with SPIHT encoding on the basis of compression ratio and compression quality. The comparison of these compression methods are classified according to different medical images like MRI and CT. For JPEG based image compression RLE and Huffman encoding techniques are used by varying the bits per pixel. For JPEG 2000 based image compression SPIHT encoding method is used. The DCT and DWT methods are compared by varying bits per pixel and measured the performance parameters of MSE, PSNR and compression ratio. In JPEG 2000 method, compared the different wavelets like Haar, CDF 9/7, CDF 5/3 etc. and evaluated the compression ratio and compression quality. Also varied the decomposition levels of wavelet transform with different images. 
3. Abstract of Cited Paper (BIB003):  Compound image is combination of text, graphics and pictures. Compression is the process of reducing the amount of data required to represent information. It also reduces the time required for the data to be sent over the Internet or Web pages. Compound image compression is done on the basis of lossy and lossless compression. Lossy compression is a data encoding method that compresses data by discarding (losing) some data in the image. Lossless compression is used to compress the image without any loss of data in the image. Image compression is done using lossy compression and lossless compression. In this paper different techniques are used for compressing compound images. The performance of these techniques has been compared. 
4. Abstract of Cited Paper (BIB004):  In this research proposed to compressed medical images in single frame file format with two different techniques - JPEG and JPEG2000. The significant advantage of JPEG2000 over normal JPEG is that the error from JPEG2000 compression is smaller than the error from JPEG. Nevertheless, both methods share a similar mishap; when the compression ratio increases, they both generate more error than the processes on lower compression ratio do. What's more, the research proposed a neural network approach to image quality assessment. In particular, the neural network measures the quality of an image by predicting the mean opinion score (MOS) of human observers and using a set of key features extracted from the original and test images. Experimental results, using JPEG and JPEG2000 compressed images, show that the neural network outputs correlate highly with the MOS scores, and therefore, the neural network can easily serve as a correlate to subjective image quality assessment. The predicted MOS values have a linear correlation coefficient of 0.9543, a Spearman ranked correlation of 0.9591. 
5. Abstract of Cited Paper (BIB005):  Digital images play an important role in a wide range of medical applications. Several widespread technologies for digital imaging, such as Computed Tomography (CT), Magnetic Resonance (MR), etc., produce three-dimensional images. Data compression, is thus essential to reduce the volume of such images, permitting their efficient storing along with the improvement of the relative transmission time through Internet or any other ad-hoc systems, like Picture Archiving and Communication System (PACS), Tele-radiology, etc.. Since these images are often stored in system particularly vulnerable from the point of view of security, especially because they contain sensitive data, it is necessary to provide such images with a mechanism which ensures at least security against message forgery. In fact, an attack can be made by altering a medical image, and consequently, may alter the relative diagnosis. The purpose of this work is twofold, first we propose a low complexity approach for the compression of 3-D medical images, then, in order to limit the above defined potential attack, we proposed en efficient method to insert within each image an invisible digital watermark, during the compression process. In this way, we define a hybrid approach that handles simultaneously and efficiently both the compression that the security of three-dimensional images. We validate the proposed approach by showing test results. 
6. Abstract of Cited Paper (BIB006):  Abstract This paper presents a new near lossless compression algorithm for hyperspectral images based on distributed source coding. The algorithm is performed on blocks that have the same location and size in each band. Because the importance varies from block to block along the spectral orientation, an adaptive rate allocation algorithm that weights the energy of each block under the target rate constraints is introduced. A simple linear prediction model is employed to construct the side information of each block for Slepian–Wolf coding. The relationship between the quantized step size and the allocated rate of each block is determined under the condition of correct reconstruction with the side information at the Slepian–Wolf decoder. Slepian–Wolf coding is performed on the quantized version of each block. Experimental results show that the performance of the proposed algorithm is competitive with that of state-of-the-art compression algorithms, making it appropriate for on-board compression. 
7. Abstract of Cited Paper (BIB007):  Display Omitted A pseudo-distance technique (PDT2) for lossless compression of images is given.Parallelization of the technique is presented.The performance of the PDT2 in Hyper-Threading is evaluated. Data compression is a challenging process with important practical applications. Specialized techniques for lossy and lossless data compression have been the subject of numerous investigations during last several decades. Previously, we studied the use of the pseudo-distance technique (PDT) in lossless compression of color-mapped images and its parallel implementation. In this paper we present a new technique (PDT2) to improve compression gain of PDT. We also present a parallelized implementation of the new technique, which results in substantial gains in compression time while providing the desired compression efficiency. We demonstrate that on non-dithered images PDT2 outperforms PDT by 22.4% and PNG by 29.3%. On dithered images, PDT2 achieves compression gains of 7.1% over PDT and 23.8% over PNG. We also show that the parallel implementation of PDT2, while compromising compression less than 0.3%, achieves near linear speedup and utilization of Intel Hyper-Threading technology on supported systems improves speedup on average 18%. 
8. Abstract of Cited Paper (BIB008):  This paper addresses the problem of the lossless and near-lossless compression of hyperspectral images and presents two efficient algorithms based on distributed source coding, which perform the lossless compression by means of multilevel scalar codes. The proposed algorithms are implemented on the co-located blocks in the spectral orientation. A novel multiband spectral predictor is proposed to construct the side information of each block. The back-up side information is introduced for the second algorithm to recover the images when the original side information is corrupted by errors. The encoder only requires the transmission of the least significant bit (LSB) bit-planes to the decoder, and the number of bits is computed by the maximum error between the block and its side information. The proposed algorithms are also extended to near-lossless compression. The experimental results show that the proposed algorithms have a competitive compression performance with the existing distributed compression algorithms. Moreover, the proposed algorithms can provide low complexity and different degrees of error resilience, which is suitable for onboard compression.

Literature Review:
The rapid development of computer applications has come with the enormous increase of digital images manipulation's, mainly in the domain of multimedia, games, satellite transmissions and medical imaging. This situation has prompted more researchers on effective compression algorithms. The basic idea of image compression is to reduce the middle number of bits per pixel (bpp) necessary for image representation BIB003 -  . Image compression approaches can be divided into two (2) methods: lossless and lossy BIB005 - BIB008 . Lossy compression is a data encoding method that compresses data by discarding (losing) some data in the image  , BIB007 . Meanwhile, lossless compression is used to compress the image without any loss of data in the image BIB004 - BIB009 . A variety of methods has been developed for compressing images and videos. However, if a compression method does not take into account the contents of an image, important information may be lost due to the high compression. We can transfer images and videos more efficiently if we extract important regions for humans and make the compression rate of other regions as low as possible. The performances of several medical image compression techniques are presented in BIB003 . Three types of entropy encoding have been used, which include set partitioning in hierarchal trees (SPIHT) encoding, Huffman encoding and run length encoding (RLE). For a JPEG based image compression, the RLE and Huffman encoding techniques were used by varying the bit pixel. Meanwhile, for a JPEG 2000 based image compression, the SPIHT encoding method was used. SPIHT is a very effective and computationally simple technique for image compression. A-part from that, this method provides a way to generate consistent quality images at a lower bit rate compared to JPEG. In this work, the Cohen-Debaucheries-Feauveau Biorthogonal wavelet was used along with SPIHT to provide a high compression ratio and good resolution. Figure 1 shows a JPEG block diagram for full cycle compression system. In this implementation, DWT has become an important method for image compression. Moreover, wavelet based coding has provided substantial improvements in picture quality at high compression ratios, mainly due to the better energy compaction property of the wavelet transforms. The standard JPEG is only capable of providing excellent performances at rates of higher than 0.25 bits per pixel. In order to overcome this situation, the JPEG committee had initiated the development of another standard, commonly known as JPEG 2000. JPEG 2000 is a standard that was based on wavelet decomposition. To evaluate the compression performance, magnetic resonance image (MRI) data with pixel resolution 512×512 and in 8-bits was used, whilst the peak signal to noise ratio (PSNR) and compression ratio (CR) was deployed for objective assessments. Results have shown that for CT scan image JPEG compression method outperforms the PSNR and degree of compression than wavelet compression method. For a lower compression ratio, JPEG has yielded higher quality images than the wavelet. While the DCT based image JPEG coders perform very well at moderate bit rates, at higher compression ratios, image quality was degraded due to the artefacts resulting from the block-based DCT scheme. On the other hand, wavelet based coding has led to substantial improvements in picture quality at low bit rates because of the overlapping basis functions and better energy compaction property of wavelet transforms. SPIHT is the most efficient method with respect to compression ratio and PSNR value. With the increasing degree of compression SPIHT was capable of maintaining the image quality quite well. The encoding and decoding time had increased when the bit rate increased. The implementation of image compression technique of a new transformation method called the enhance DPCM transformation (EDT) was presented in BIB001 . The EDT is based on predictive models and more energy will be obtained by improving prediction ability. The proposed new transform method engine is illustrated as a block diagram in Figure 2 . Huffman encoding was chosen as the entropy encoder for this lossless compression method. The proposed algorithm was validated with non-medical images and real medical images. Results from the various test images have shown that this new compression method has improved the lossless ratio for JPEG and JPEG 2000 to approximately 7% and more than 3.5% respectively. BIB001 Another issue related to the compression methodology was reported in BIB006 . This work proposed a hybrid approach that can efficiently compress 3-D medical images and optionally embeds a digital invisible watermark inside them. Moreover, this proposed hybrid approach for the lossless compression of 3-D medical images, was based on the predictive coding model (PCM). A-part from that, transform domain techniques, usually permit the embedding of watermark string inside the whole image by using spectral coefficient, particularly the DCT and DWT. Two modalities of medical imaging have been used in this work; the CT, and the MRI. Additionally, this hybrid approach has enabled the efficient lossless compression of 3-D medical images while simultaneously embedding a digital invisible watermark string. Although hardware implementations were not discussed, interestingly, this work has provided significant information on the hybrid approaches for compression techniques. The proposed hybrid approach is illustrated as a block diagram in Figure 3 . Figure 3 Block diagram of hybrid approach BIB006 A new compression scheme based on symmetry in the field of prediction-error was reported in BIB002 . In order to provide an efficient method, both the intra and inter-slice correlations have been considered. The existing correlations in the slice and the third dimension redundancies between consecutive slices were respectively extracted for the intra-slice and inter-slice compression purposes. Generally, the main idea of this proposed method was to use the symmetrical nature of the brain to predict the pixels of half of the image, using the corresponding pixels in the other half of the image. Therefore, this proposed method has six stages of compression algorithm as shown in Figure 4 . These stages include the prediction stage; splitting of the result of the predictor; two separate block matching methods (symmetry-based and inter-slice); three-dimensional context modeling; regression; and entropy coding. Moreover, the implementation of the proposed compression algorithm; attached with the activity level classification model (ALCM), block matching and 3-D context modeling, has revealed that bit rates of the proposed method were better than for the JPEG-LS and JPEG-3D. Therefore, on average, this proposed algorithm has superiority over both standards.