Image Compression: A Survey <s> IMAGE COMPRESSION USING CODING OF WAVELET COEFFICIENTS <s> We propose a wavelet-based image compression algorithm that achieves robustness to transmission errors by partitioning the transform coefficients into groups and independently processing each group using an embedded coder. Thus, a bit error in one group does not affect the others, allowing more uncorrupted information to reach the decoder. <s> BIB001 </s> Image Compression: A Survey <s> IMAGE COMPRESSION USING CODING OF WAVELET COEFFICIENTS <s> In this paper, we propose a wavelet-based image-coding scheme allowing lossless and lossy compression, simultaneously. Our two-layered approach utilizes the best of two worlds: it uses a highly performing wavelet-based or wavelet packet-based coding technique for lossy compression in the low bit range as a first stage. For the second (optional) stage, we extend the concept of reversible integer wavelet transforms to the more flexible class of adaptive reversible integer wavelet packet transforms which are based on the generation of a whole library of bases, from which the best representation for a given residue between the reconstructed lossy compressed image and the original image is chosen using a fast-search algorithm. We present experimental results demonstrating that our compression algorithm yields a rate-distortion performance similar or superior to the best currently published pure lossy still image-coding methods. At the same time, the lossless compression performance of our two-layered scheme is comparable to that of state-of-the-art pure lossless image-coding schemes. Compared to other combined lossy/lossless coding schemes such as the emerging JPEG-2000 still image-coding standard PSNR improvements up to 3 dB are achieved for a set of standard test images. <s> BIB002 </s> Image Compression: A Survey <s> IMAGE COMPRESSION USING CODING OF WAVELET COEFFICIENTS <s> A best-transform selection method and a selective partial embedding technique are proposed for use in a reversible embedded image compression system based on the embedded zerotree wavelet (EZW) coding scheme. Together, these approaches are shown to yield significantly better compression performance than a baseline system employing fully embedded EZW coding in conjunction with a single fixed transform, such as the S+P transform. <s> BIB003 </s> Image Compression: A Survey <s> IMAGE COMPRESSION USING CODING OF WAVELET COEFFICIENTS <s> Digital radiology places very high demands on the networking and digital storage infrastructure of hospitals. In addition to having quite stringent requirements on the quality of the images displayed to the radiologist, much of the technical challenge resides in the necessity of displaying desired images as rapidly as possible. We present an infrastructure for progressive transmission and compression of medical images, which can refine an initial image by increasing the detail information not only in scale-space, but also in coefficient precision. The approach is based on the embedded zerotree wavelet (EZW) algorithm. This algorithm offers a tremendous amount of flexibility in meeting the bandwidth and image quality constraints in a radiological imaging environment. We propose an application of the EZW algorithm in progressive medical image transmission in which it can specify and control both the resolution constraint and rate constraint. The presented method can provide a framework for lossy image compression, with performance far superior to those provided by the standard JPEG algorithm. Also due to the flexibility of the method we will show how any region of interest of an image can be sent progressively. <s> BIB004 </s> Image Compression: A Survey <s> IMAGE COMPRESSION USING CODING OF WAVELET COEFFICIENTS <s> A hybrid coding system that uses a combination of set partition in hierarchical trees (SPIHT) and vector quantisation (VQ) for image compression is presented. Here, the wavelet coefficients of the input image are rearranged to form the wavelet trees that are composed of the corresponding wavelet coefficients from all the subbands of the same orientation. A simple tree classifier has been proposed to group wavelet trees into two classes based on the amplitude distribution. Each class of wavelet trees is encoded using an appropriate procedure, specifically either SPIHT or VQ. Experimental results show that advantages obtained by combining the superior coding performance of VQ and efficient cross-subband prediction of SPIHT are appreciable for the compression task, especially for natural images with large portions of textures. For example, the proposed hybrid coding outperforms SPIHT by 0.38 dB in PSNR at 0.5 bpp for the Bridge image, and by 0.74 dB at 0.5 bpp for the Mandrill image. <s> BIB005
Embedded Zero Wavelet algorithms (EZW): This is robust image compression technique in which embedded nature of bit stream is maintained during the transmission. For this first wavelet coefficients are partition into S parts then code and quantize them one by one thus S embedded bit stream which are different from each other are created BIB001 . Coding that simultaneously offers lossless as well as lossy recovery. For optimizing the functionality of both coding methods, Division of two layers is recommended. For consumption of energy and decorrelation first coding layer is on a DWT that is produced by wavelet filter kernel choice in correct way. Using an adaptive wavelet packet integer algorithm remaining part of image is de-correlated in second layer BIB002 . Two techniques based on EZW coding were proposed to enhance the working of method. Using both methods one can make gains significant in compression performance can be highly achievable through a fixed transform. Coding resulting System outperforms a baseline system by using fully embedded EZW BIB003 . The approach relies upon EZW. In progressive medical image transmission EZW algorithm is known as "controlled and manageable resolution constraint and rate constraint" BIB004 . Hybrid coding: A hybrid coding system that uses a hierarchy of set partition in SPIHT and vector quantization VQ for image compression is presented. To form the wavelet trees the coefficient of the input image wavelet is rearranged from all the sub-bands of the same orientation that are composed of the corresponding wavelet coefficients. Wavelet trees grouped into two classes based on the amplitude distribution a simple tree classifier has been proposed. Each class of wavelet trees is encoded using an appropriate procedure, specifically either SPIHT or VQ BIB005 .