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In this letter, we propose a novel scheme for the compression of the bilevel region of interest (ROI) maps of hyperspectral images. The scheme can achieve high compression ratios by novel ways of partitioning the intricate regions of the image into smaller blocks. We first analyzed the effect of a low-order linear predictive model on the original image. The analysis showed that linear prediction results in lower-entropy residual images compared to the original images, which in turn helps in improving the compression efficiency. We then use the optimization algorithm that finds the best combination of scan directions for the nonzero blocks. It is shown to offer increasingly better compression with additional iterations. The simulation results on various data sets show that our scheme outperforms the international standard for binary image compression [joint bi-level image expert (JBIG2)].

Although there exist many researches on the compression of original non-encrypted binary images, few approaches focus on the compression of encrypted binary images. As binary images like contract, signature, halftone images are still used widely in practice, how to compress efficiently encrypted binary images in a lossy way deserves further exploration. To this end, this paper develops a lossy compression scheme for encrypted binary images by exploiting the Markov random field (MRF) model. Considering that the third-party in scenarios of cloud or distributed computing cannot access to the encryption key, we develop the concatenated down-sampling and LDPC-based encoding to perform the compression, in which four different down-sampling methods are designed to facilitate improving the quality of reconstructed image. In reconstruction, we first formulate the lossy reconstruction from the encrypted and compressed binary image as an optimization problem, and then build a joint factor graph involving the LDPC-decoding, decryption, and MRF to solve this optimization problem, in which the MRF is exploited to well infer pixels discarded in the down-sampling process. By adapting the sum-product algorithm (SPA) to the constructed joint factor graph for lossy reconstruction (JFG-LR) and running the adapted SPA on the JFG-LR, we thus recover the original binary image in a lossy way. By integrating the stream-cipher-based encryption, the down-sampling and LDPC-based compression, and the JFG-LR-involved reconstruction, we thus propose a new lossy compression scheme for encrypted binary images. Experimental results show that the proposed scheme achieves desirable compression efficiency, which is comparable to or even better than that of the JBIG2 with the original unencrypted binary image as input.

In this article, the researcher introduces a hybrid chain code for shape encoding, as well as lossless and lossy bi-level image compression. The lossless and lossy mechanisms primarily depend on agent movements in a virtual world and are inspired by many agent-based models, including the Paths model, the Bacteria Food Hunt model, the Kermack–McKendrick model, and the Ant Colony model. These models influence the present technique in three main ways: the movements of agents in a virtual world, the directions of movements, and the paths where agents walk. The agent movements are designed, tracked, and analyzed to take advantage of the arithmetic coding algorithm used to compress the series of movements encountered by the agents in the system. For the lossless mechanism, seven movements are designed to capture all the possible directions of an agent and to provide more space savings after being encoded using the arithmetic coding method. The lossy mechanism incorporates the seven movements in the lossless algorithm along with extra modes, which allow certain agent movements to provide further reduction. Additionally, two extra movements that lead to more substitutions are employed in the lossless and lossy mechanisms. The empirical outcomes show that the present approach for bi-level image compression is robust and that compression ratios are much higher than those obtained by other methods, including JBIG1 and JBIG2, which are international standards in bi-level image compression. Additionally, a series of paired-samples t-tests reveals that the differences between the current algorithms’ results and the outcomes from all the other approaches are statistically significant.

We consider lossy compression of a broad class of bilevel images that satisfy the smoothness criterion, namely, images in which the black and white regions are separated by smooth or piecewise smooth boundaries, and especially lossy compression of complex bilevel images in this class. We propose a new hierarchical compression approach that extends the previously proposed fixed-grid lossy cutset coding (LCC) technique by adapting the grid size to local image detail. LCC was claimed to have the best rate-distortion performance of any lossy compression technique in the given image class, but cannot take advantage of detail variations across an image. The key advantages of the hierarchical LCC (HLCC) is that, by adapting to local detail, it provides constant quality controlled by a single parameter (distortion threshold), independent of image content, and better overall visual quality and rate-distortion performance, over a wider range of bitrates. We also introduce several other enhancements of LCC that improve reconstruction accuracy and perceptual quality. These include the use of multiple connection bits that provide structural information by specifying which black (or white) runs on the boundary of a block must be connected, a boundary presmoothing step, stricter connectivity constraints, and more elaborate probability estimation for arithmetic coding. We also propose a progressive variation that refines the image reconstruction as more bits are transmitted, with very small additional overhead. Experimental results with a wide variety of, and especially complex, bilevel images in the given class confirm that the proposed techniques provide substantially better visual quality and rate-distortion performance than existing lossy bilevel compression techniques, at bitrates lower than lossless compression with the JBIG or JBIG2 standards.

The data reduction capability of image compression schemes is limited by the underlying compression technique. For applications with minor changes between consecutive frames, change coding can be used to further reduce the data. We explored the efficiency of change coding for data reduction in a wireless visual sensor network (WVSN). This paper presents an analysis of the compression efficiency of change coding for a variety of changes, such as different shapes, sizes, and locations of white objects in adjacent sets of frames. Compressing change frame provides a better performance compared with compressing the original frames for up to 95% changes in the number of objects in adjacent frames. Due to illumination noise, the size of the objects increases at its boundaries, which negatively affects the performance of change coding. We experimentally proved that the negative impact of illumination noise could be reduced by applying morphology on the change frame. Communication energy consumption of the VSN is dependent on the data that are transmitted to the server. Our results show that the communication energy consumption of the VSN can be reduced by 27%, 29%, and 46% by applying change coding in combination with JBIG2, Group4, and Gzip_pack, respectively. The findings presented in this paper will aid researchers in enhancing the compression potential of image coding schemes in the energy-constrained applications of WVSNs. SMART (Smarta system och tjänster för ett effektivt och innovativt samhälle)

This paper presents an efficient new image compression and decompression methods for document images, intended for usage in the pre-processing stage of an OCR system designed for needs of the ?Nikola Tesla Museum? in Belgrade. Proposed image compression methods exploit the Run-Length Encoding (RLE) algorithm and an algorithm based on document character contour extraction, while an iterative scanline fill algorithm is used for image decompression. Image compression and decompression methods are compared with JBIG2 and JPEG2000 image compression standards. Segmentation accuracy results for ground-truth documents are obtained in order to evaluate the proposed methods. Results show that the proposed methods outperform JBIG2 compression regarding the time complexity, providing up to 25 times lower processing time at the expense of worse compression ratio results, as well as JPEG2000 image compression standard, providing up to 4-fold improvement in compression ratio. Finally, time complexity results show that the presented methods are sufficiently fast for a real time character segmentation system.

The DjVu file format and image compression techniques are widely used in the archival of digital documents. Its key ingredients are the separation of the document into fore- and background layers and a binary switching mask, followed by a lossy, transform-based compression of the former and a dictionary-based compression of the latter. The lossy compression of the layers is based on a wavelet decomposition and bit truncation, which leads, in particular at higher compression rates, to severe compression artifacts in the standard decompression of the layers. The aim of this paper is to break ground for the variational decompression of DjVu files. To this aim, we provide an in-depth analysis and discussion of the compression standard with a particular focus on modeling data constraints for decompression. This allows to carry out DjVu decompression as regularized inversion of the compression procedure. As particular example, we evaluate the performance of such a framework using total variation and total generalized variation regularization. Furthermore, we provide routines for obtaining the necessary data constraints from a compressed DjVu file and for the forward and adjoint transformation operator involved in DjVu compression.

We propose an efficient framework of reversible data hiding to preserve compatibility between normal printing and printing with a special color ink by using a single common image. The special color layer is converted to a binary image by digital halftoning and losslessly compressed using JBIG2. Then, the compressed information of the binarized special color layer is reversibly embedded into the general color layer without significant distortion. Our experimental results show the availability of the proposed method in terms of the marked image quality., Comment: 2 pages

We propose a reversible data hiding scheme for printing with special color inks. The special color layer is first converted to the binary image by the digital halftoning process and losslessly compressed with JBIG2. After the preprocessing, the special color layer is embedded into the general color layer. Consequently, both normal color printing and printing with special color inks can be implemented by the single embedded image. Our experimental results show that the information amount of the special color layer is highly decreased by the digital halftoning process and the image compression process using JBIG2, and the embedded image quality is not visually degraded.

Background DICOM standard does not have modules that provide the possibilities of two-dimensional Presentation States to three-dimensional (3D). Once the final 3D rendering is obtained, only video/image exporting or snapshots can be used. To increase the utility of 3D Presentation States in clinical practice and teleradiology, the storing and transferring the segmentation results, obtained after tedious procedures, can be very effective. Purpose To propose a strategy for preserving interaction and mobility of visualizations for teleradiology by storing and transferring only binary segmented data, which is effectively compressed by modern adaptive and context-based reversible methods. Material and methods A diverse set of segmented data, which include four abdominal organs (liver, spleen, right, and left kidneys) from 20 T1-DUAL and 20 T2-SPIR MRI, liver from 20 CT, and abdominal aorta with aneurysms (AAA) from 19 computed tomography-angiography datasets, are collected. Each organ is segmented manually by expert physicians, and binary volumes are created. The well-established reversible binary compression methods PNG, JPEG-LS, JPEG-XR, CCITT-G4, LZW, JBIG2, and ZIP are applied to medical datasets. Recently proposed context-based (3D-RLE) and adaptive (ABIC) algorithms are also employed. The performance assessment has been presented in terms of the compression ratio that is a universal compression metric. Results Reversible compression of binary volumes results with substantial decreases in file size such as 254 to 2.14 MB for CT-AAA, 56.7 to 0.3 MB for CT-liver. Moreover, compared to the performance of well-established methods (i.e., mean 76.14%), CR is observed to be increased significantly for all segmented organs from both CT and MRI datasets when ABIC (95.49%) and 3D-RLE (94.98%) are utilized. The hypothesis is that morphological coherence of scanning procedure and adaptation between the segmented organs, that is, bi-level images, contributes to compression performance. Although the performance of well-established techniques is satisfactory, the sensitivity of ABIC to modality type and the advantage of 3D-RLE when the spatial coherence between the adjacent slices are high results with up to 10 times more CR performance. Conclusion Adaptive and context-based compression strategies allow effective storage and transfer of segmented binary data, which can be used to re-produce visualizations for better teleradiology practices preserving all interaction mechanisms.

The demand for large-scale image data grows increasingly fast, resulting in a need for efficient image compression. This work aims at improving the lossless compression of binary images. To this end, we propose the use of a tree-based optimization algorithm, which searches for the best partitions of the input image into non-uniform blocks, and for the best combination of scan directions, with the goal of achieving more efficient compression of the resulting sequences of intervals between successive symbols of the same kind. The tree-based search algorithm searches for the best grid structure for adaptively partitioning the image into blocks of varying sizes. Extensive simulations of this search algorithm on various datasets demonstrated that we can achieve significantly higher compression on average than various standard binary image compression methods such as the JPEG 2000 and JBIG2 methods.

Nowadays poor security, low transmission and storage efficiency of images have become serious concerns. In order to improve the situation, this paper put forward a new image lossless compression joint encryption algorithm based on chaotic map with all original information intact. The lossless compression uses SPIHT(Set Partitioning in Hierarchical Trees) encoding method based on integer wavelet transform, and encrypt multiple rounds in the process of wavelet coefficients and SPIHT coding applying many kinds of chaotic maps. Experimental results show that the compressed file size is about 50 % of the original file size, which achieves relatively good lossless compression ratio. Besides, the encryption method passes many security tests, such as sensitivity test, entropy test, autocorrelation test, NIST SP800---22 test. There is a high application value in the medical field and the national security department whose image files require a relatively high quality.

Binary MQ arithmetic coding is widely used recently as a basic entropy coder in multimedia coding system. MQ coder esteems high in compression efficiency to be used in JBIG2 and JPEG2000. The importance of arithmetic coding is increasing after it is adopted as an unique entropy coder in HEVC standard. In the binary MQ coder, arithmetic approximation without multiplication is used in the process of recursive subdivision of range interval. Because of the MPS/LPS exchange activity happened in MQ coder, output byte tends to increase. This paper proposes an enhanced binary MQ arithmetic coder to make use of a lookup table for AQe using quantization skill in order to reduce the deficiency. Experimental results show that about 4% improvement of compression in case of JBIG2 for bi-level image compression standard. And also, about 1% improvement of compression ratio is obtained in case of lossless JPEG2000 coding. For the lossy JPEG2000 coding, about 1% improvement of PSNR at the same compression ratio. Additionally, computational complexity is not increasing.

Nowadays, the volume of the data is increasing with time which generates a problem in storage and transfer. To overcome this problem, the data compression is the only solution. Data compression is the science (or an art) of representing information in compact form. This is an active research area. Compression is to save the hardware storage space and transmission bandwidth by reducing the redundant bits. Basically, lossless & lossy are two types of data compression technique. In lossless data compression, original data is similar to decompressed or decoded data, but in lossy technique is not same. In this paper, Study lossless image compression technique. The purpose of image compression is to maximum bandwidth utilization and reduces storage capacity. This technique is beneficial to image storage and transfer. At the present time, Mostly image compression research have focused on the wavelet transform due to better performance over another transform. The performance is evaluated by using MSE & PSNR. DWT, quantization, Arithmetic, Huffman coding and DCT techniques are briefly introduced. After decompression, the quality of image is evaluated using PSNR parameter between original & decoded image. Compression ratio (CR) parameter is calculated to measure how many times image compressed.

Recent lossless still image compression formats are powerful tools for compression of all kind of common images (pictures, text, schemes, etc.). Generally, the performance of a compression algorithm depends on its ability to anticipate the image function of the processed image. In other words, a compression algorithm to be successful, it has to take perfectly the advantage of coded image properties. Astronomical data form a special class of images and they have, among general image properties, also some specific characteristics which are unique. If a new coder is able to correctly use the knowledge of these special properties it should lead to its superior performance on this specific class of images at least in terms of the compression ratio. In this work, the novel lossless astronomical image data compression method will be presented. The achievable compression ratio of this new coder will be compared to theoretical lossless compression limit and also to the recent compression standards of the astronomy and general multimedia.

Image compression can be done in two ways namely lossy or lossless. Lossy methods are used for expected results like photographs where some negligible loss of data is tolerable. Rapid growth of medical science such as ehealth and telemedicine requires lossless Image compression. The correlation and redundancy which exists across different medical images are considered to achieve better compression ratio. . Better compression results in less storage and less overhead while transmission on the network. A memory- assisted hybrid compression technique is proposed. The approach is motivated by using a combination of methods, lossy and lossless. The original image is compressed using PCA algorithm and then using lossless coding methods. PCA is used to find the correlation among the similar medical images. PCA algorithm is used with only some principal components.

In this article, the researcher develops an image coding technique which is based on the wolf-sheep predation model. In the design, images are converted to virtual worlds of sheep, routes and wolves. Wolves in this model wander around searching for sheep while the algorithm tracks their movement. A wolf has seven movements which capture all the directions of the wolf. In addition, the researcher introduces one extra move of the wolf the purpose of which is to provide a shorter string of movements and to enhance the compression ratio. The first coordinates and the movements of the wolf are tracked and recorded. Then, arithmetic coding is applied on the string of movements to further compress it. The algorithm was applied on a set of images and the results were compared with other algorithms in the research community. The experimental results reveal that the size of the compressed string of wolf movements offer a higher reduction in space and the compression ratio is higher than those of many existing compression algorithms including G3, G4, JBIG1, JBIG2 and the recent agent-based model of ant colonies.

Image compression plays a crucial role in medical imaging allowing efficient storage and transmissions by reducing the amount of data required to represent the digital image. The main goal is to achieve higher compression ratios and minimum degradation in quality. To decrease the storage space, the use of different compression techniques is justified by some medical imaging modalities generate the volume that of data which will be increasing. Different medical images like X-ray angiograms, magnetic resonance images, Ultrasound and computed Tomography are used in the medical image compression techniques. In medical applications it is required to conserve the diagnostic validity of the image requires the use of lossless compression methods, producing low compression factors. For medical data, lossless compression is preferred to the greater gains of lossy compression, in the interest of accuracy. . A set of experiment has been performed for the analysis of the proposed work on the several DICOM medical images and it has been observed that the DWT, DCT and Huffman coding has higher compression ratio than the hybrid model. The proposed method gives better quality of image that includes high PSNR and CR as well as low MSE. The proposed medical "DICOM images compression scheme" is based on Hybrid DWT, DCT and Huffman coding techniques.

Nowadays, compression of binary image is vital due to enormous amount of images that are being processed and stored in a wide range of applications such as text, maps, fingerprint etc. In this study, we proposed a new method for lossless compression of binary images. The general principle of image compression algorithms is transforming binary digits into a new one that specifies the same information but with fewer digits. We compared our proposed method with Run-Length method which is one of lossless compression methods for binary images, and the compression results shown that our method is successful in lossless compression binary images.

The inherent noise in the observed (e.g., scanned) binary document image degrades the image quality and harms the compression ratio through breaking the pattern repentance and adding entropy to the document images. In this paper, we design a cost function in Bayesian framework with dictionary learning. Minimizing our cost function produces a restored image which has better quality than that of the observed noisy image, and a dictionary for representing and encoding the image. After the restoration, we use this dictionary (from the same cost function) to encode the restored image following the symbol-dictionary framework by JBIG2 standard with the lossless mode. Experimental results with a variety of document images demonstrate that our method improves the image quality compared with the observed image, and simultaneously improves the compression ratio. For the test images with synthetic noise, our method reduces the number of flipped pixels by 48.2% and improves the compression ratio by 36.36% as compared with the best encoding methods. For the test images with real noise, our method visually improves the image quality, and outperforms the cutting-edge method by 28.27% in terms of the compression ratio., Comment: CVPR 2017

The world is increasing the need of converting into e-documents from paper based documents. While converting into e-documents, the technology in a position has to provide a large amount of memory space to store the same data's. In order to manage the huge memory, the documents are compressing before store. In connection with the above points, in this paper, we have proposed a novel lossless compression scheme of pixel based technique for compressing the grayscale images. This proposed scheme can be classified into two different processes. One is compression process before store the image and other one is decompression while retrieving the same image back. The significant of this technique is that, the image pixels positions are interchanging as much as possible within the image itself. Due to these processes, the proposed technique can be achieved the high complexity, However, it's not an easy task to retrieve the same image by third parties. In addition, the pixel based technique has obtained good quality of the reconstructed image, it has taken minimum execution time for compression and decompression processes, 60% of the original image can be reduced during the compression process and an error rate is minimum, it mean that near to zero. Finally, the proposed compression technique has provided a lossless compression for given grayscale input images.

We propose a method for binary object classification to make lossy compression of document images. The double-pass dictionary formation consists in a Pattern Matching & Substitution (PM&S) algorithm that firstly makes a classification of all objects using Tanimoto distance, whereas the second pass consists of a classification of just the patterns chosen on the first pass in dictionary formation. On each of the two passes is performed the refinement of the generated classes choosing the best representative member as pattern. We tested our dictionary formation method on a compression system that uses 3OT chain code to codify the chosen patterns and the Paq8l archiver to compress the resulting string. The compression ratios applied over the eight CCITT test binary images benchmarks, at 200 and 600 dpi, are better compressed against state of the art and standard methods. Comparing with JBIG2 we obtained 27% better compression level at 200 dpi and 65% at 600 dpi, comparing with DjVu's JB2 we obtained 6% better compression level at 200 dpi and 35% at 600 dpi and comparing with 3OT-Paq8l we obtained 38% better compression at 200 dpi and 30% at 600 dpi.

A progressive transmission and decoding nearly lossless compression algorithm is proposed. The image data are grouped according to different frequencies based on DCT transform, then it uses the JPEG-LS core algorithmtexture prediction and Golomb coding on each group of data, in order to achieve progressive image transmission and decoding. Experimentation on the standard test images with this algorithm and comparing with JPEG-LS shows that the compression ratio of this algorithm is very similar to the compression ratio of JPEG-LS, and this algorithm loses a little image information but it has the ability of the progressive transmission and decoding.

In this study, the authors investigate the use of multi-scale recurrent pattern matching paradigm for lossless image compression. The multi-scale multidimensional parser (MMP) algorithm is a successful implementation of this paradigm for lossy image compression, and can naturally perform lossless compression since it was first derived from a Lempel–Ziv lossless scheme. However, neither its recently adopted coding tools had been adapted for lossless coding nor a thorough analysis of its performance had been carried out. In this work, the authors evaluate MMP's lossless compression capability, proposing modifications for some of its predictions modes, as well as the inclusion of an adaptive prediction mode based on least squares. The residual information is also coded with well-known techniques used in lossless compression. Experimental results for MMP show that the algorithm achieves a good performance for images such as computed generated graphics and scanned documents, whereas keeping a competitive performance for natural images. Since the algorithm's structure is exactly the same for lossless and lossy compression, the obtained results suggest that MMP is able to achieve a high compression performance for a wide range of images and rates, from lossy to lossless, without any prior analysis of the image to be coded.

This paper proposes improvements to the reversible hiding technique proposed in by Hong et al. in 2012. The proposed technique is based on the characteristics of the human visual on neighboring pixel values to obtain the average for calculating the minimum value of just noticeable difference (JND). The JND is then used to determine the suitable embedding level to decrease image distortion. Hong et al.’s method will first shift pixels that caused overflow and underflow after finding the suitable embedding level. The values of the shifted pixel will be recorded in a location map the size of an image. The binary location map was JBIG2 compressed and embedded with the secret information. In the location map, pixels not shifted were recorded as 0. Therefore, many pixels were recorded. These resulted in the compressed length to be longer which affects the payload size. Experimental results showed that our proposed method decreases the size of the location map, payload is increased but the image quality is maintained similar to Hong et al.’s method.

Image compression is part of digital image processing that use different operation for reduction of image size. Image compression is done by using different compressing approaches that are lossless and lossy compression. In this paper lossless compression approach has been used to get higher compression ratio. In this paper a new approach has been purposed that is used for image compression on the basis of fitness value of a single DCT block of the image data. Local best and global best values for different DCT blocks has been measured using pollination based optimization.

We present a novel lossless image compression algorithm. It achieves better compression than popular lossless image formats like PNG and lossless JPEG 2000. Existing image formats have specific strengths and weaknesses: e.g. JPEG works well for photographs, PNG works well for line drawings or images with few distinct colors. For any type of image, our method performs as good or better (on average) than any of the existing image formats for lossless compression. Interlacing is improved compared to PNG, making the format suitable for progressive decoding and responsive web design.

In this work, we evaluate the performance of several lossless compression methods on binary images simulated by a statistical model. This work was motivated by our previous study on compression of regions-of-interest maps in hyperspectral images, where a biased run-length coding method was found to provide good compression on just a few binary images with most pixels being in the background. In order to discover the trend on the compression performance, we introduced a modified Ising model of Markov random fields by Metropolis, which can generate a large number of binary images iteratively, so that we can study how the compression ratios of the above method change gradually when the contents of the images get varied slightly with iterations. Simulation results showed that the biased run-length coding method significantly outperformed the arithmetic code and JBIG2 method. The study also gave rise to a dual biased run-length method, which can provide further compression gains on images with more foreground objects.

The main objective of image compression is to diminish the number of bits required to represent an image by eliminating the spatial and spectral redundancies. Image compression is classified as lossy and lossless compression. Lossy compression reduces the size of a file by removing redundant information. Whereas, in the lossless compression there won't be any loss of information upon the extraction of original image from the compressed image. The aim of this paper is to do a comparison between two latest works in the image compression namely, An Efficient DCT-Based Image Compression System Based on Laplacian Transparent Composite Model and An Innovative Lossless Compression Method for Discrete-Color Images. From the analysis, it is observed that on average, An Efficient DCT-Based Image Compression System Based on Laplacian Transparent Composite Model reduces the compression rate by 25% in the case of images, compared to JBIG2. It is also observed that this approach is better suited for traditional images like Lena and Goldhill while An Innovative Lossless Compression Method for Discrete-Color Images is better suited for charts and maps.

While one can achieve very large size reduction on a hyperspectral image dataset by preserving only some regions-of-interest (ROI's), the bi-level map that describes the locations of the ROI pixels tend to defy efficient compression due to the somewhat “random” nature of ROI pixel locations. To this end, we proposed a novel method for lossless compression of these ROI maps. In this method, we first partitioned a bi-level map into equally sized blocks. We then converted the bi-level pixels within each block into a block symbol. Based on the observation that the most probable blocks tend to contain either all zeros or all ones, we chose to run-length code these most probable block symbols before applying Huffman code in order to achieve high compression, whereas we applied a separate Huffman code on other less probable block symbols. Thus this biased run-length coding method differs from conventional approaches where all symbols are run-length coded. Tests on NASA's AVIRIS dataset showed that the proposed method could provide significant improvements over various bi-level image compression techniques (including JBIG2 and lossless JPEG 2000) on the ROI maps.

A lot of images and photos are being produced today using our personal devices such as cameras, smart phones and tablets. Taking many shots is becoming more frequent due to costless processing of pictures production. In this paper, we are investigating compression of a group of similar photos or images all together instead of separate compressions. In our technique, we considered applying blurring over the original images in order to gain better compression ratio. Moreover, subimages are classified to form a non redundant codebook that helps producing index file with smaller size than the original images. Experimental results showed competitive compression achievements compared to peer techniques such as JPEG and PNG.

The image compression has always been one of the competitive fields of studies. It aims to represent an image in a minimal size in bytes without compromising the image quality. Our method; the numbering combinations method, consists on calculating all the possible combinations of the pixels and associate an index to our image through all the mentioned combinations. This index is a unique identifier of the image. However, with real images, the index of an image is a big number and it is difficult to manipulate it or to talk about image compression. The idea is to implement our algorithm as an image compression technique and test it in transmission to judge its performances.

Problem statement: Identifying the new lossless image compression alg orithm for high performance applications like medical and satellite imaging; a high quality lossless image is most important when reproduction which leads to classify the data for decision making. Approach: A new lossless hybrid algorithm based on simple selective scan order with Bit Plane Slicing method is presented for lossless Image compression of limited bits/pixel images, such as medical images, satelli te images and other still images common in the world. Efficient coding is achieved by run length and modified Huffman coding. This approach is combined with efficient selective scan order for entire image in one pass through. Results: The new hybrid algorithm achieves good compression rate, compared to the existing schemes of coding with dif ferent test images. Conclusion: Compared to the existing standards JPEG-LS and CALIC, the compression rate is reduced with our proposed algorithm for different standard test images.

Due to their abilities to succinctly capture features at different scales and directions, wavelet-based decomposition or representation methods have found wide use in image analysis, restoration, and compression. While there has been a drive to increase the representation ability of these methods via directional filters or elongated basis functions, they still have been focused on essentially piecewise linear representation of curves in images. We propose to extend the line-based dictionary of the beamlet framework to one that includes sets of arcs that are quantized in height. The proposed chordlet dictionary has elements that are constrained at their endpoints and limited in curvature by system rate or distortion constraints. This provides a more visually natural representation of curves in images and, furthermore, it is shown that for a class of images the chordlet representation is more efficient than the beamlet representation under tight distortion constraints. A data structure, the fat quadtree and an algorithm for determining an optimal chordlet representation of an image are proposed. Codecs have been implemented to illustrate applications to both lossy and lossless low bitrate compressions of binary edge images, and better rate or rate-distortion performance over the JBIG2 standard and a beamlet-based compression method are demonstrated. Highlights? The chordlet dictionary has elements that are quantized by endpoints and in curvature. ? The chordlet framework provides a visually natural representation of curves in images. ? The chordlet is more efficient than the beamlet for images with large-scale curved edges. ? The chordlet provides better performance over the JBIG2 standard for a class of images.

Lossless image compression is a class of image compression algorithms that allows the original image to be perfectly reconstructed from the compressed image. This undertaking shows another lossless color image compression algorithm based on pixel prediction and arithmetic coding. Lossless squeezing of a Red, Green and Blue (RGB) picture is carried out by first decorrelating utilizing Reversible Color Transform (RCT). The got Y part is then compacted by a conventional lossless grayscale picture clamping strategy. The chrominance image is encoded utilizing Arithmetic coding and pixel prediction system. By utilizing RCT the forecast lapse is characterized and arithmetic coding is connected to the mistake signal. The packed image and encoded picture is joined to structure a lossless compacted RGB picture. It is demonstrated that this system diminishes the bit rates contrasted and JPEG 2000 and JPEG-XR. With a specific end goal to further lessen the bit rate the compression strategies and the pixel prediction strategy can be altered for better execution. Keywords-Lossless image compression, Pixel Prediction, Arithmetic Coding, RCT, Huffman coding

minimizations of the storage space and transmission time are the two most important riding factors in image compression for telemedicine. Keeping this in view this paper intend to focus on a comparative investigation of three near lossless image compression technique, NLIC (near lossless image compression), SPIHT with DWT (Discrete Wavelets Transform), RLE (Run Length Encoding) with DCT (Discrete Cosine Transform). These techniques are analyzed and tested on various type of square state of art photographic images and medical images. The performance evaluation parameters like PSNR (Peak Signal to Noise Ratio), CR (Compression Ratio), RMSE (Root Mean Square Error), Computational time (CT) are calculated to evaluate the performance of mentioned near lossless image compression techniques.

In this paper, a co-design method for JPEG2000 video compression system using DSP and FPGA is presented. By profiling the complexity of JPEG2000 algorithm, it is noticed that a MQ-coder is the most complex part. Thus, we implement the MQ-coder on FPGA for the parallel processing using VHDL to reduce the complexity. In order to verify the performance of the MQ-coder, JBIG2 standard test vector and images are used. The experimental results show that the proposed MQ-coder enhances the processing time approximately 3 times compared with the previous software MQ-coder.

This paper presents a bi-level image compression method based on chain codes and entropy coders. However, the proposed method also includes an order estimation process to estimate the order of dependencies that may exist among the chain code symbols prior to the entropy coding stage. For each bi-level image, the method first obtains its chain code representation and then estimates its order of symbol dependencies. This order value is used to find the conditional and joint symbol probabilities corresponding to our newly defined Markov model. Our order estimation process is based on the Bayesian information criterion (BIC), a statistically based model selection technique that has proved to be a consistent order estimator. In our experiments, we show how our order estimation process can help achieve more efficient compression levels by providing comparisons against some of the most commonly used image compression standards such as the Graphics Interchange Format (GIF), Joint Bi-level Image Experts Group (JBIG), and JBIG2.

Binary wavelet transform (BWT) has several distinct advantages over the real wavelet transform (RWT), such as the conservation of alphabet size of wavelet coefficients, no quantization introduced during the transform and the simple Boolean operations involved. Thus, less coding passes are engaged and no sign bits are required in the compression of transformed coefficients. However, the use of BWT for the embedded grayscale image compression is not well established. This paper proposes a novel Context-based Binary Wavelet Transform Coding approach (CBWTC) that combines the BWT with a high-order context-based arithmetic coding scheme to embedded compression of grayscale images. In our CBWTC algorithm, BWT is applied to decorrelate the linear correlations among image coefficients without expansion of the alphabet size of symbols. In order to match up with the CBWTC algorithm, we employ the gray code representation (GCR) to remove the statistical dependencies among bi-level bitplane images and develop a combined arithmetic coding scheme. In the proposed combined arithmetic coding scheme, three highpass BWT coefficients at the same location are combined to form an octave symbol and then encoded with a ternary arithmetic coder. In this way, the compression performance of our CBWTC algorithm is improved in that it not only alleviate the degradation of predictability caused by the BWT, but also eliminate the correlation of BWT coefficients in the same level subbands. The conditional context of the CBWTC is properly modeled by exploiting the characteristics of the BWT as well as taking advantages of non-causal adaptive context modeling. Experimental results show that the average coding performance of the CBWTC is superior to that of the state-of-the-art grayscale image coders, and always outperforms the JBIG2 algorithm and other BWT-based binary coding technique for a set of test images with different characteristics and resolutions.

In some scripts, especially the Farsi/Arabic script, letters normally attach together and produce many different patterns, some of which are fully or partially similar. Detecting such patterns and exploiting them to reduce the library size, has a rather great effect on the compression ratio. In this paper, a lossy/lossless compression method is proposed for bi-level printed text images in archiving applications. For this, we propose a new 1-D pattern matching technique in the chain coding domain that uses the proposed technique of detecting the repetitive sub-signals in order to detect the fully or partially similar patterns. Experimental results show that the compression performance of the proposed method is considerably better than those of the existing bi-level printed text image compression methods as high as 1.8-4.2 times in the lossy case and 1.6-3.8 times in the lossless case at 300dpi.

Problem statement: Nowadays a large number of various medical images are generated from hospitals and medical centers with sophisticated image acquisition devices, the movement toward digital images in radiology presents the problem of how to conveniently and economically store, retrieve and transmit the volume of digital images. Thus digital image data compression is necessary in order to solve this problem. So in a wide range of medical applications such as disease diagnostic and during the compression process, the loss of information is unacceptable; hence medical images are required to be at high resolution as possible. Instead of lossy compression with relatively high compression ratio, mathematical lossless compression methods are favored in this field. Approach: In this study, an efficient new lossless image coding algorithm using a simple technique was presented. Our coding algorithm was based on pixel redundancy reduction by formulating two matrices only, which were Gray Scale Matrix (GSM) and Binary Matrix (BM). These matrices had been used for coding and decoding processes. Results: Results showed that the maximum compression ratio achieved using the proposed method was 4:1, which was more efficient than the present lossless techniques, moreover the computational complexity is greatly simplified; therefore producing very fast coding and decoding. Conclusion: This algorithm was most suitable for those images where lossy compression was avoided such as medical images used for teleradiology and other telemedicine purposed and it can be applied to other medical modalities.

Problem statement: Image processing applications were drastically inc reasing over the years. In such a scenario, the fact that, the digit al images need huge amounts of disk space seems to be a crippling disadvantage during transmission and storage. So, there arises a need for data compression of images. Approach: This study proposed a novel technique called binar y merge coding for lossless compression of images. This met hod was based on spatial domain of the image and it worked under principle of Inter-pixel redund ancy reduction. This technique was taken advantage of repeated values in consecutive pixels positions. For a set of repeated consecutive values only one value was retained. Results: The proposed binary merge coding achieved the compression rate of the brain image was 1.6572479. Comparatively, it is 100% more than the compression rate achieved by standard JPEG. Conclusion/Recommendations: This technique was simple in implementation and required no additional memory area. The experimental results of binary merge coding were compared with standard JPEG and it showed that, the binary merge coding improved compression rate compared to JPEG. The same algorithm can be extending to color images. This algorithm can also used for lossy comp ression with few modifications.

Lossless image compression has long been recognised as an important need for several applications such as medical imaging, storage of critical IR image sequences, and remote sensing. In this paper, a simple, fast and easy to realisable-on-hardware lossless video compression algorithm is proposed that is well-suited for IR imageries. Context-based median predictor is used for prediction of reference pixels. Three neighboring pixels are used as context for prediction. Inter-frame coding is performed by encoding the redundant pixels in an efficient way, using 1-bit code. Finally, the arithmetic coder is used as entropy coding. The proposed algorithm is able to operate in image compression and video compression mode. The proposed Median Predictor based Lossless Video Compression (MPLVC) algorithm is compared with Joint Pictures Experts Group-Lossless (JPEG-LS) and Fast and Efficient Lossless Image Compression System (FELICS) for compression performance. The results demonstrate that proposed algorithm is superior in encoding rate with added advantage in simplicity and ease in realization on hardware.Defence Science Journal, 2009, 59(2), pp.183-188, DOI:http://dx.doi.org/10.14429/dsj.59.1507