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: Image Compression: A Survey 
Chapter Title:  Wavelet compression of chromosome images: 
1. Abstract of Cited Paper (BIB001):  An efficient image compression algorithm based on energy clustering and zero-quadtree representation (ECZQR) in the wavelet transform domain is proposed. In embedded coding, zeros within each subband are encoded in the framework of quadtree representation instead of zerotree representation. To use large rectangular blocks to represent zeros, it first uses morphological dilation to extract the arbitrarily shaped clusters of significant coefficients within each subband. The proposed encoding method results in less distortion in the decoded image than the line-by-line encoding method. Experimental results show that the algorithm is among the most efficient wavelet image compression algorithms. 
2. Abstract of Cited Paper (BIB002):  This paper describes the compression of grayscale medical ultrasound images using a recent compression technique, i.e., space-frequency segmentation (SITS). This method finds the rate-distortion optimal representation of an image from a large set of possible space-frequency partitions and quantizer combinations and is especially effective when the images to code are statistically inhomogeneous, which is the case for medical ultrasound images. We implemented a compression application based on this method and tested the algorithm on representative ultrasound images. The result is an effective technique that performs better than a leading wavelet-transform coding algorithm, i.e., set partitioning in hierarchical trees (SPIHT), using standard objective distortion measures. To determine the subjective qualitative performance, an expert viewer study was run by presenting ultrasound radiologists with images compressed using both SFS and SPIHT. The results confirmed the objective performance rankings. Finally, the performance sensitivity of the space-frequency codec is shown with respect to several parameters, and the characteristic space-frequency partitions found for ultrasound images are discussed. 
3. Abstract of Cited Paper (BIB003):  This paper proposes a new method for chromosome image compression based on an important characteristic of these images: the regions of interest (ROIs) to cytogeneticists for evaluation and diagnosis are well determined and segmented. Such information is utilized to advantage in our compression algorithm, which combines lossless compression of chromosome ROIs with lossy-to-lossless coding of the remaining image parts. This is accomplished by first performing a differential operation on chromosome ROIs for decorrelation, followed by critically sampled integer wavelet transforms on these regions and the remaining image parts. The well-known set partitioning in hierarchical trees (SPIHT) (Said and Perlman, 1996) algorithm is modified to generate separate embedded bit streams for both chromosome ROIs and the rest of the image that allow continuous lossy-to-lossless compression of both (although lossless compression of the former is commonly used in practice). Experiments on two sets of sample chromosome spread and karyotype images indicate that the proposed approach significantly outperforms current compression techniques used in commercial karyotyping systems and JPEG-2000 compression, which does not provide the desirable support for lossless compression of arbitrary ROIs.

Literature Review:
In this coding scheme for the chromosomes, ROIs lossless compression is mainly used during our examination and testing for comparison on the whole image. In second case with use of bit rate separately background of chromosomes are compressed lossless. Easy manageable performances for lossless compression in this section along with all results presented are described in terms of PSNR in decibel and b/p for lossy compression BIB004 . Predictive embedded zero tree wavelet: Predictive EZW wavelet coding is a zero tree based codec. The functional units for PEZW are zero trees for coding. Minimum complexity and good coding efficiency is gained by this algorithm BIB001 . ECZQR in the wavelet transform domain is a unique scenario that depends on energy clustering and zero quad tree representation is proposed. To use large rectangular blocks to represent zeros it first uses Morphological dilation to extract the arbitrarily shaped clusters of significant coefficients within each sub-band BIB003 BIB002 .