Image Compression: A Survey <s> LOSSLESS IMAGE COMPRESSION ALGORITHM <s> When applying predictive compression on image data there is an implicit assumption that the image is scanned in a particular order. Clearly, depending on the image, a different scanning order may give better compression. In earlier work, we had defined the notion of a prediction tree (or scan) which defines a scanning order for an image. An image can be decorrelated by taking differences among adjacent pixels along any traversal of a scan. Given an image, an optimal scan that minimizes the absolute sum of the differences encountered can be computed efficiently. However, the number of bits required to encode an optimal scan turns out to be prohibitive for most applications. In this paper we present a prediction scheme that partitions an image into blocks and for each block selects a scan from a codebook of scans such that the resulting prediction error is minimized. Techniques based on clustering are developed for the design of a codebook of scans. Design of both semiadaptive and adaptive codebooks is considered. We also combine the new prediction scheme with an effective error modeling scheme. Implementation results are then given, which compare very favorably with the JPEG lossless compression standard. > <s> BIB001 </s> Image Compression: A Survey <s> LOSSLESS IMAGE COMPRESSION ALGORITHM <s> Advanced medical imaging requires storage of large quantities of digitized clinical data. These data must be stored in such a way that their retrieval does not impair the clinician's ability to make a diagnosis. We propose a theory and algorithm for near lossless dynamic image data compression. Taking advantage of domain-specific knowledge related to medical imaging, medical practice and the dynamic imaging modality, a compression ratio greater than 80:1 is achieved. The high compression ratios are achieved by the proposed algorithm through three stages: (1) addressing temporal redundancies in the data through application of image optimal sampling, (2) addressing spatial redundancies in the data through cluster analysis, and (3) efficient coding of image data using standard still-image compression techniques. To illustrate the practicality of the algorithm, a simulated positron emission tomography (PET) study using the fluoro-deoxy-glucose (FDG) tracer is presented. Realistic dynamic image data are generated by virtual scanning of a simulated brain phantom as a real PET scanner. These data are processed using the conventional and proposed algorithms as well as the techniques for storage and analysis. The resulting parametric images obtained from the conventional and proposed approaches are subsequently compared to evaluate the proposed compression algorithm. The storage space for dynamic image data reduced by more than 95%, without loss in diagnostic quality. Therefore, the proposed theory and algorithm are expected to be very useful in medical image database management and telecommunication. <s> BIB002 </s> Image Compression: A Survey <s> LOSSLESS IMAGE COMPRESSION ALGORITHM <s> In general text compression techniques cannot be used directly in image compression because the model of text and image are different. Previously, a new class of text compression, namely, the block-sorting algorithm which involves Burrows and Wheeler (1994) transformation (BWT) gave excellent results in text compression. However, if we apply it directly in image compression, the result is poor. Surprisingly, good results can be obtained if we employ a prediction model such as the one defined in the JPEG standard before the BWT algorithm. Thus, the predictive model plays a critical role in the compression process. To further improve the compression efficiency, we use the gradient adjusted prediction (GAP). Experimental results show that the proposed method is better than lossless JPEG and some LZ-based compression methods. <s> BIB003 </s> Image Compression: A Survey <s> LOSSLESS IMAGE COMPRESSION ALGORITHM <s> LOCO-I (LOw COmplexity LOssless COmpression for Images) is the algorithm at the core of the new ISO/ITU standard for lossless and near-lossless compression of continuous-tone images, JPEG-LS. It is conceived as a "low complexity projection" of the universal context modeling paradigm, matching its modeling unit to a simple coding unit. By combining simplicity with the compression potential of context models, the algorithm "enjoys the best of both worlds." It is based on a simple fixed context model, which approaches the capability of the more complex universal techniques for capturing high-order dependencies. The model is tuned for efficient performance in conjunction with an extended family of Golomb (1966) type codes, which are adaptively chosen, and an embedded alphabet extension for coding of low-entropy image regions. LOCO-I attains compression ratios similar or superior to those obtained with state-of-the-art schemes based on arithmetic coding. Moreover, it is within a few percentage points of the best available compression ratios, at a much lower complexity level. We discuss the principles underlying the design of LOCO-I, and its standardization into JPEC-LS. <s> BIB004 </s> Image Compression: A Survey <s> LOSSLESS IMAGE COMPRESSION ALGORITHM <s> An efficient compression scheme for color-quantized images based on progressive coding of color information has been developed. Instead of sorting color indexes into a linear list struc- ture, a binary-tree structure of color indexes is proposed. With this structure, the new algorithm can progressively recover an image from two colors to all of the colors contained in the original image, i.e., a lossless recovery is achieved. Experimental results showed that it can efficiently compress images in both lossy and lossless cases. Typically for color-quantized Lena image with 256 colors, the algorithm achieved 0.5 bpp below state-of-the-art lossless com- pression methods while preserving the efficient lossy compression. Such a compression scheme is very attractive to many applications that require the ability of fast browsing or progressive transmis- sion, and if necessary, to exactly recover the original image. Index Terms—Color-quantized images, data compression, index sorting, progressive refinement. <s> BIB005 </s> Image Compression: A Survey <s> LOSSLESS IMAGE COMPRESSION ALGORITHM <s> The multiple array technique proposed in this paper is a lossless compression technique that involves no transformation, and achieves a very good compression ratio. This technique is based on forming three arrays to indicate and code the changes in the adjacent pixels. The arrays thus formed can be further compressed using Huffman coding. The computational complexity is greatly reduced thus producing faster compressions and decompressions. Due to the combination of simplicity and compression potential of this technique, the algorithm "enjoys the best of both worlds". This technique attains compression ratios similar to or superior than the lossless image compression standards presently available. Due to the high compression ratio, the average bits required to represent a pixel is greatly reduced. Thus, this algorithm is optimally suited for telemedicine having fast transmission rates. <s> BIB006 </s> Image Compression: A Survey <s> LOSSLESS IMAGE COMPRESSION ALGORITHM <s> Recently, several efficient context-based arithmetic coding algorithms have been developed successfully for lossless compression of error-diffused images. In this paper, we first present a novel block- and texture-based approach to train the multiple-template according to the most representative texture features. Based on the trained multiple template, we next present an efficient texture- and multiple-template-based (TM-based) algorithm for lossless compression of error-diffused images. In our proposed TM-based algorithm, the input image is divided into many blocks and for each block, the best template is adaptively selected from the multiple-template based on the texture feature of that block. Under 20 testing error-diffused images and the personal computer with Intel Celeron 2.8-GHz CPU, experimental results demonstrate that with a little encoding time degradation, 0.365 s (0.901 s) on average, the compression improvement ratio of our proposed TM-based algorithm over the joint bilevel image group (JBIG) standard [over the previous block arithmetic coding for image compression (BACIC) algorithm proposed by Reavy and Boncelet is 24%] (19.4%). Under the same condition, the compression improvement ratio of our proposed algorithm over the previous algorithm by Lee and Park is 17.6% and still only has a little encoding time degradation (0.775 s on average). In addition, the encoding time required in the previous free tree-based algorithm is 109.131 s on average while our proposed algorithm takes 0.995 s; the average compression ratio of our proposed TM-based algorithm, 1.60, is quite competitive to that of the free tree-based algorithm, 1.62 <s> BIB007
In this study lossless compression technique is proposed in order to obtain compression at high ratio (Fig. 5 ). As characteristics of image maximized local and global redundancy so it reduces redundancy at both local and global level. First divided image into blocks of different length and then depending on the characteristics of pixels in each block encode these pixels. Technique gives more efficiency with respect to other lossless algorithms and better result is achieving   . Lossless compression technique applied on medical imaginary redundancies in spatial and temporal domain in order to decrease size of image and it also reduces storage space without degrading image. Time needed for computation is also decrease. At first stage to exploit temporal redundancy in image data OSS is apply. Using cluster analysis redundancy in data I temporal domain is also removed. Standard technique for dynamic data BIB002 . Almost same or high compression ratio is achieved by LOCO-I then provided with state of art schemes depending on arithmetic code BIB004 . Lossless Predictive Burrows Wheeler Transformation (PBWT) and Gradient Adjusted Prediction (GAP) block sort algorithms which are useful in compression of textexual data. From implementing Gradient Adjusted Prediction predictor the performance can also be improved. From results achieved by the experiment the efficiency our proposed methods can also be checked BIB003 . The algorithm recommended is a better substitution for lossless. In case of real time imaginary obtained results are reasonable with exploitation of spatial redundancy. The implementation of the method is carried out in MATLAB. On MRI, CT scan, ultrasound and Mammogram experimental calculations are also applied. For future compression Multiple Array Technique we can use Huffman/Arithmetic coders and then transmission is done through the Wi-Fi media BIB006 . For training template from training model Lossless novel block is presented of error diffused images. Multi-template is constructed for representing texture features BIB007 . We examine approach which contain block division of the image and then from a codebook of scans each block is choose. Semi adaptive and adaptive codebooks design and use is also studied. We also match with the best obtained outcomes via standard JPEG BIB001 . A direct 3D lossless compression method depending upon region growing has been introduced. Its performance is the same as that of the corresponding 2D SLIC algorithm when it is applied to the same number of slices at worst case. In most practical cases dealing with medical images and examine remarkable efficiency by proposed method  . The coding procedure consisting two passes. It groups dots of 2*2 to cell where number of black dots is used to represent each cell and the black dots location in cell. In first step encoding is done of black dots in the cell and in 2 nd round position is encoded. Firstly we perform lossless compression Lossless compression in  3D lossless image compression based on region growing Better performance due to exploitation of redundancies Performance is the same as 2D SLIC algorithm when applied to the same num of slices Medical images -2 nd pass it can be refined to be lossless (Ng and Cheng 2003) . Incorporation of Progressive and near lossless technique is presented in a single framework. Proposed coder proved to be competent as experimental performance than conventional compression schemes BIB005 .