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The editorial board of JRTIP met in Brussels in April 2014 where they were attending the SPIE Conference on RealTime Image and Video Processing. The editorial board will have its next meeting in San Francisco in February 2015 where the same conference will take place as part of the SPIE Electronic Imaging Symposium. In the recently held editorial meeting (see Fig. 1), various items related to the status of the journal were discussed; a summary of these items is provided below. To address the backlog of printed versions of the accepted manuscripts that appear on Online First, we are pleased to state that the number of printed pages per issue will be increased to 200 pages per issue starting in 2015 for a total page count of 800 pages annually. It is hoped that the backlog situation is eased as a result of this page count increase. We will revisit this issue in 2015 to see whether further actions would be needed to resolve this issue. The journal performance numbers that have become available for the year 2013 clearly reflect the elevated recognition level of JRTIP. In 2013, similar to 2012, the acceptance rate of manuscripts submitted to JRTIP has remained at about 25 %. This is an indication that our review process is conducted in such a way that only quality papers are published in JRTIP. It is worth noting that the usage factor (UF) of JRTIP, which is the ratio of the number of paper downloads over the number of papers for a 2-year period, has increased by about 75 % from 2011 to 2012. With the substantial increase in the number of downloads to 34,000? in 2013 from 25,500? in 2012, it is expected that the 2013 UF would increase significantly. We would like to particularly thank the Associate Editors and the Springer Editorial Office for the major roles they have played in increasing these numerical indicators while maintaining the quality of published papers. Here, it is worth pointing out that the third issue in 2014 will be allocated to two special issues that are scheduled to appear together. These special issues are named ‘‘Algorithms and Architectures for Real-time Multi-Dimensional Image Processing’’ and ‘‘Convergence of Real-Time 3D Imaging and Optical Communications’’. This regular issue of JRTIP includes six papers. The first paper by Juliet et al. presents a compound image segmentation algorithm based on the mixed raster content (MRC) model (foreground/mask/background). After segmenting a compound image into different classes, each class is transformed to the MRC model according to its properties. The foreground and the background layers are compressed using JPEG 2000 while the mask layer is compressed using JBIG2. It is shown that this MRC compression achieves a higher subjective quality and coding efficiency than the state-of-the-art compression algorithms, such as JPEG, JPEG 2000 and H.264/AVC-I. The second paper by Bartovsky et al. introduces an additional, intra-operator level of parallelism for dilation/ erosion operations. Realized in a dedicated hardware, for rectangular structuring elements with programmable size, the implementation allows obtaining real-time performances for these operators that were previously unachievable. Low latency and memory requirements are the other main attributes of this parallelism. The third paper by Gaceb et al. describes a document classification approach based on physical layout features N. Kehtarnavaz (&) University of Texas at Dallas, Richardson, USA e-mail: kehtar@utdallas.edu

This study proposes a method which can generate high quality inverse halftone images from halftone images. This method can be employed prior to any signal processing over a halftone image or the inverse halftoning used in JBIG2. The proposed method utilizes the least-mean-square (LMS) algorithm to establish a relationship between the current processing position and its corresponding neighboring positions in each type of halftone image, including direct binary search, error diffusion, dot diffusion, and ordered dithering. After which, a referenced region called a support region (SR) is used to extract features. The SR can be obtained by relabeling the LMS-trained filters with the order of importance. Moreover, the probability of black pixel occurrence is considered as a feature in this work. According to this feature, the probabilities of all possible grayscale values at the current processing position can be obtained by the Bayesian theorem. Consequently, the final output at this position is the grayscale value with the highest probability. Experimental results show that the proposed method offers better visual quality than that of Mese-Vaidyanathan's and Chang et al's methods in terms of human-visual peak signal-to-noise ratio (HPSNR). In addition, the memory consumption is also superior to Mese-Vaidyanathan's method.

To present a pseudo lossless compression which modifies the noise component of the bit data to enhance the compression without affecting image quality? The hypothesis behind the study is that the bit data contaminated by noise can be manipulated without affecting image quality. The compression method comprises of three steps: (1) to estimate the noise level for each pixel, (2) to identify those bits contaminated by noise and replace them with zero, (3) to perform a lossless data compression on the processed image. The compression ratios are 3.10, 5.24, and 6.60 for CT, MRI, and digitized mammograms respectively, for the new method which shows a 36.8%, 62.7%, and 125% increase for the three data sets than original data. The processed images are evaluated by two image enhancing techniques: window/level and zoom. They are indistinguishable from original images. The proposed method demonstrates an improvement more than 40% in compression ratio than original image without deterioration in image quality. The qualities of processed images are the same as compared with those images by loosy JPEG2000 image compression at compression ratio around 10.

Binary images (Bi-level images; Bi-Color images, Two-tone images or Monochrome images) have been widely used in image processing, such as facsimile, letters, map archives, digital libraries, fingerprint databases, and those documents that could not be recognized by OCR. In order to reduce the storage space of binary images, there are many researchers attempted to propose more efficient algorithms for binary image compression. In general, they can be divided into two kinds of methods which are ‘lossless’ and ‘lossy’. In this research, we base on a lossy image compression method, to propose a compression Algorithm called “Skip-Line Encoding with Threshold” (SLETA). Our results of experiments show that SLETA not only achieve high compression ratio but also keep good recognizability: encoded images are not difficult to recognize after decompressions.

Information explosion along with the tremendous growth in software and hardware technology are moving industries, educational institutions, organizations towards "paperless" environment. This change has a direct impact on the amount of information digitized, thus increasing the need for efficient storage mechanisms. Digitized information mostly consist of compound images, which are a combination of different data types like text, graphics, line art, photographs, etc. Layer based compression is one of the frequently used solution to compress these kind of images. This paper proposes a novel layer based compound image compression technique. Care is taken to produce lossless compression for text part of the image and to improve the quality of the image as a whole after decompression. Experimental results were conducted to analyze the performance of the proposed compressor.

In this paper we propose a method of compression which is both lossy and lossless. The decision regarding what amount of image will be compressed in lossy manner, and what in lossless, depends on the information obtained by the histogram of the image. Another important decision parameter is bit rate of the image. Together these two parameters enable us to divide the image in two parts for different types of compression. The results show that the difference between original and decompressed images is visually negligible. The PSNR and SSIM are comparable to JPEG. . . .

We give a new paradigm for lossless image compression, with four modular components: pixel sequence, prediction, error modeling and coding. We present two new methods (called MLP and PPPM) for lossless compression, both involving linear prediction, modeling prediction errors by estimating the variance of a Laplace distribution, and coding using arithmetic coding applied to precomputed distributions. The MLP method is both progressive and parallelizable. We give results showing that our methods perform significantly better than other currently used methods for lossless compression of high resolution images, including the proposed JPEG standard. We express our results both in terms of the compression ratio and in terms of a useful new measure of compression efficiency, which we call compression gain.

The JBIG coding standard like the G3 and G4 facsimile standards defines a method for the lossless (bit-preserving) compression of bi-level (two-tone or black/white) images. One advantage it has over G3/G4 is superior compression, especially on bi-level images rendering greyscale via halftoning. On such images compression improvements as large as a factor of ten are common. A second advantage of the JBIG standard is that it can be parameterized for progressive coding. Progressive coding has application in image databases that must serve displays of differing resolution, image databases delivering images to CRT displays over medium rate (say, 9.6 to 64 kbit/s) channels, and image transmission services using packet networks having packet priority classes. It is also possible to parameterize for sequential coding in applications not benefiting from progressive buildup. It is possible to effectively use the JBIG coding standard for coding greyscale and color images as well as bi-level images. The simple strategy of treating bit-planes as independent bi-level images for JBIG coding yields compressions at least comparable to and sometimes better than the JPEG standard in its lossless mode. The excellent compression and great flexibility of JBIG coding make it attractive in a wide variety of environments.

This paper proposes a hybrid approach to compression. It incorporates lossy as well as lossless compression. Various parts of image are compressed in either way, depending on the amount of information held in that part. This scheme consists of two stages. In the first stage the image is filtered by a high pass filter to find the areas that have details. In the second stage, an effective scheme based on Huffman coding and adaptive interpolation is developed to encode the original image. With this algorithm, a good compression ratio is obtained, while, PSNR and SSIM are better to that of other methods available in literature. In other words, the newly proposed algorithm provides an efficient means for image compression.

In most image transmission applications, the requirement of a short transmission time (and correspondingly low bit rate) often precludes the use of a numerically lossless compression algorithm at the outset. However, in certain applications, it may be necessary to send a lossless version of the image at some later stage of the transmission. Such an application might exist in the medical field, where two physicians are discussing a possible patient referral from remote locations. One of the physicians may wish to transmit a digital radiograph over the phone line, and in the interest of a short transmission time, a lossy (but high quality) version of the image is sent. If the referral is accepted, the remaining difference (residual) image required to perfectly reconstruct the original image could be sent, perhaps at the conclusion of the telephone conversation. In general, a lossy plus lossless residual encoding scheme consists of the following steps: - Generate a low bit rate image through the use of an efficient lossy compression scheme. - Form a residual by computing the difference between the lossy reconstruction and the original image. - Encode the residual using an appropriate lossless technique.

The impact of using different lossless compression algorithms when compressing biometric iris sample data from several public iris databases is investigated. In particular, we relate the application of dedicated lossless image codecs like lossless JPEG, JPEG-LS, PNG, and GIF, lossless variants of lossy codecs like JPEG2000, JPEG XR, and SPIHT, and a few general purpose compression schemes to rectilinear iris imagery. The results are discussed in the light of the recent ISO/IEC FDIS 19794-6 and ANSI/NIST-ITL 1-2011 standards and the IREX recommendations.

A simple lossy-to-lossless bit-plane coding of still images is presented to integrate several functionality extensions including selective tile partitioning, progressive transmission, ROI transmission, accuracy scalability, and others. The mean squared error between the original image and a decoded image at any progression level is known prior to encoding/decoding. The proposed bit-plane codec is competitive with JPEG-LS and JPEG 2000 in the lossless compression of 8-bit grayscale and 24-bit color images. The codec outperforms the existing standards in 8-bit color-quantized image compression.

Image compression is the application of data compression techniques to two-dimensional digital images I(x, y) , to reduce the redundancy of the image data for storage or transmission in an efficient form. Image compression can be classified into two categories: lossless or lossy. Lossless compression, which achieves smaller compression ratios than lossy compression, mainly takes advantage of the image contents containing a non uniform probability distribution for a variable-length representation of the image pixels. Such images include technical drawings, icons or comics, and high-value contents such as medical imagery or image scans made for archival purposes. However, lossy compression methods, especially when they achieve a very high compression ratio, can introduce compression artifacts. Nevertheless, lossy compressions are especially suitable for natural images, such as photos, in applications where a minor (sometimes imperceptible) loss of fidelity is acceptable when it is desirable to achieve a substantial reduction in bitrate. Most of the stateof-the-art image compression standards use a combination of lossy and lossless algorithms to achieve the best performance. The Joint Photographic Experts Group (JPEG) [1], a discrete cosine transform(DCT)-based technique, is the most widely used standardized lossy image compression mechanism; it was designed for compressing either full-color or gray-scale images of natural, real-world, scenes. It works well for photographs, naturalistic artwork and similar material if the compression ratio is about 20 : 1, which is much better than the 4 : 1 compression ratio provided by a lossless compression method such as the Graphics Interchange Format (GIF) [2]. However, it does not work so well for lettering, simple cartoons, or line drawings.

This paper describes a lossless compression method for medical images that produces an embedded bit-stream, allowing progressive lossy-to-lossless decoding with L-infinity oriented rate-distortion. The experimental results show that the proposed technique produces better average lossless compression results than several other compression methods, including JPEG2000, JPEG-LS and JBIG, in a publicly available medical image database containing images from several modalities.

This paper proposes a novel grayscale image compression approach using the binary wavelet transform (BWT) and context-based arithmetic coding, namely the context-based binary wavelet transform coding algorithm (CBWTC). In our CBWTC, in order to alleviate the degradation of predictability caused by the BWT and eliminate the correlation within the same level subbands, three highpass wavelet coefficients at the same location are combined to form an octave symbol and then encoded with a ternary arithmetic coder. The conditional context of the CBWTC is properly modeled by exploiting the properties of the BWT as well as taking the advantages of non-causal adaptive context modeling. Experimental results show that the coding performance of the CBWTC is better than that of the state-of-the-art grayscale image coders except for images containing rich texture, and always outperforms the JBIG2 algorithm and other BWT-based binary coding technique.

Most of speech and image data compressed by lossy compression whose decompressed data are different from the original ones. Here, the different between the decompressed data and the original ones cannot be recognized by most of people. Lossless image compression, which gives exactly the same decompressed data as the original ones, is necessary for medical image, art work image, and satellite image, which are frequently processed by computers now. This paper proposes lossless image compression by prediction coding whose frequency table operation is based on PPM(Prediction by Partial Match). The prediction algorithm for the proposed method is based on that for CALIC, an existing lossless image compression method; and the difference between the predicted value and the actual one is encoded by PPM-based compression method. In this compression method, initial values in the frequency table and frequency table operation method are modified to achieve efficient compression ratio. Computer simulation says that the compression ratio of the proposed method is better than that of CALIC by about 0.07 bit/pixel.

A novel template design algorithm is presented for lossless compression of color halftone images. First, we extract the line pattern in the neighbor local region of each bi-level pixel. Then, the representative line pattern is evaluated from these obtained lines, using the least square error minimization. According to the evaluated line pattern and two design constraints, therefore, the context template is shaped. With the designed template, finally, each color channel image is compressed by a context-based binary arithmetic encoder. Based on the adaptiveness of the template to the input image, the proposed templates yield better compression performance than the conventional JBIG templates, which saves 35% of the JBIG bitstream.

This paper is intended to present a lossless image compression method based on multiple-tables arithmetic coding (MTAC) method to encode a gray-level imagef. First, the MTAC method employs a median edge detector (MED) to reduce the entropy rate off. The gray levels of two adjacent pixels in an image are usually similar. A base-switching transformation approach is then used to reduce the spatial redundancy of the image. The gray levels of some pixels in an image are more common than those of others. Finally, the arithmetic encoding method is applied to reduce the coding redundancy of the image. To promote high performance of the arithmetic encoding method, the MTAC method first classifies the data and then encodes each cluster of data using a distinct code table. The experimental results show that, in most cases, the MTAC method provides a higher efficiency in use of storage space than the lossless JPEG2000 does.

Publisher Summary This chapter focuses on the lossy compression component of the Joint Photographic Experts Group (JPEG) and the JPEG2000 standards. Joint Photographic Experts Group lossy compression entails an irreversible mapping of the image to a compressed bitstream, but the standard provides mechanisms for a controlled loss of information. Lossy compression produces a bitstream that is usually much smaller in size than that produced with lossless compression. The JPEG standard has proved to be a tremendous success over the past decade in many digital imaging applications. However, as the needs of multimedia and imaging applications evolved in areas such as medical imaging, reconnaissance, the Internet, and mobile imaging, it became evident that the JPEG standard suffered from shortcomings in compression efficiency and progressive decoding. This led the JPEG committee to launch an effort in late 1996 and early 1997 to create new image compression standard. The intent was to provide a method that would support a range of features in a single compressed bitstream for different types of still images such as bi level, gray level, color, multicomponent—in particular multispectral—or other types of imagery.

The Mixed Raster Content (MRC) ITU document compression standard (T.44) specifies a multi-layer representation of a compound document. It is expected that higher compression can be achieved if more efficient compression standards are used to compress each layer. In this paper we present an MRC compound document codec that uses H.264/AVC operating in INTRA mode to encode Background/Foreground layers and JBIG2 to encode the binary Mask layer. We also present a threshold-based layer segmentation algorithm and an iterative data-filling algorithm for redundant regions. The result is an unrivaled performance for compressing compound documents as demonstrated by our experiments.

In this paper, a near lossless medical image compression scheme combining JPEG-LS with cubic spline interpolation (CSI) is presented. The CSI is developed to subsample image data with minimal distortion and to achieve image compression. It has been shown in literatures that the CSI can be combined with the transform-based image compression algorithm to develop a modified image compression codec, which obtains a higher compression ratio and a better subjective quality of reconstructed image than the standard transform-based codecs. This paper combines the CSI with lossless JPEG-LS to form the modified JPEG-LS scheme and further makes use of this modified codec to medical image compression. By comparing with the JPEG-LS image compression standard, experimental results show that the compression ratio increased over 3 times for the proposed scheme with similar visual quality. The proposed scheme reduces the loading for storing and transmission of image, therefore it is suitable for low bit-rate telemedicine application. The modified JPEG-LS can reduce the loading of storing and transmitting of medical image.

Automatic summarization of JBIG2 coded textual images is discussed. Compressed images are partially decompressed to compute relevant features. The feature extraction method is free from using any character recognition module. Summary sentences are ranked. Experiment considers documents in Indic scripts that lack in having any efficient OCR systems. Script independent aspect of the approach is highlighted through use of two most popular Indic scripts. Sentence selection efficiency of about 61% is achieved when judged against man-made summarization. A nonparametric (distribution-free) rank statistic shows a correlation coefficient of 0.33 as a measure of the (minimum) strength of the associations between sentence ranking by machine and human.

We discuss a new approach for lossy compression of bilevel images based on Markov random fields (MRFs). The goal is to preserve key structural information about the image, and then reconstruct the smoothest image that is consistent with this information. The image is compressed by losslessly coding the pixels in a square grid of lines and adding bits when needed to preserve structural information. The decoder uses the MRF model to reconstruct the interior of each block bounded by the grid, based on the pixels on its boundary, plus the extra bits provided for certain blocks. The idea is that, as long as the key structural information is preserved, then the smooth contours of the block having highest probability with respect to the MRF provides acceptable reconstructions. We propose and consider objective criteria for both encoding and evaluating the quality and structure preserving properties of the coded bilevel images. These include mean-squared error, MRF energy (smoothness), and connected components (topology). We show that overall, for comparable mean-squared error, the new approach provides perceptually superior reconstructions than existing lossy compression techniques at lower encoding rates.

We present general and unified algorithms for lossy/lossless coding of bilevel images. The compression is realized by applying arithmetic coding to conditional probabilities. As in the current JBIG standard the conditioning may be specified by a template. For better compression, the more general free tree may be used. Loss may be introduced in a preprocess on the encoding side to increase compression. The primary algorithm is a rate-distortion controlled greedy flipping of pixels. Though being general, the algorithms are primarily aimed at material containing half-toned images as a supplement to the specialized soft pattern matching techniques that work better for text. Template based refinement coding is applied for lossy-to-lossless refinement. Introducing only a small amount of loss in half-toned test images, compression is increased by up to a factor of four compared with JBIG. Lossy, lossless, and refinement decoding speed and lossless encoding speed are less than a factor of two slower than JBIG. The (de)coding method is proposed as part of JBIG2, an emerging international standard for lossless/lossy compression of bilevel images.

This paper presents the block arithmetic coding for image compression (BACIC) algorithm: a new method for lossless bilevel image compression which can replace JBIG, the current standard for bilevel image compression. BACIC uses the block arithmetic coder (BAC): a simple, efficient, easy-to-implement, variable-to-fixed arithmetic coder, to encode images. BACIC models its probability estimates adaptively based on a 12-bit context of previous pixel values; the 12-bit context serves as an index into a probability table whose entries are used to compute p/sub 1/ (the probability of a bit equaling one), the probability measure BAC needs to compute a codeword. In contrast, the Joint Bilevel Image Experts Group (JBIG) uses a patented arithmetic coder, the IBM QM-coder, to compress image data and a predetermined probability table to estimate its probability measures. JBIG, though, has not get been commercially implemented; instead, JBIG's predecessor, the Group 3 fax (G3), continues to be used. BACIC achieves compression ratios comparable to JBIG's and is introduced as an alternative to the JBIG and G3 algorithms. BACIC's overall compression ratio is 19.0 for the eight CCITT test images (compared to JBIG's 19.6 and G3's 7.7), is 16.0 for 20 additional business-type documents (compared to JBIG's 16.0 and G3's 6.74), and is 3.07 for halftone images (compared to JBIG's 2.75 and G3's 0.50).

Digital image compression (reduction of the amount of numeric data needed to represent a picture) is widely used in electronic storage and transmission devices. Few studies have compared the suitability of the different compression algorithms for dermatologic images. We aimed at comparing the performance of four popular compression formats, Tagged Image File (TIF), Portable Network Graphics (PNG), Joint Photographic Expert Group (JPEG), and JPEG2000 on clinical and videomicroscopic dermatologic images. Nineteen (19) clinical and 15 videomicroscopic digital images were compressed using JPEG and JPEG2000 at various compression factors and TIF and PNG. TIF and PNG are "lossless" formats (i.e., without alteration of the image), JPEG is "lossy" (the compressed image has a lower quality than the original), JPEG2000 has a lossless and a lossy mode. The quality of the compressed images was assessed subjectively (by three expert reviewers) and quantitatively (by measuring, point by point, the color differences from the original). Lossless JPEG2000 (49% compression) outperformed the other lossless algorithms, PNG and TIF (42% and 31% compression, respectively). Lossy JPEG2000 compression was slightly less efficient than JPEG, but preserved image quality much better, particularly at higher compression factors. For its good quality and compression ratio, JPEG2000 appears to be a good choice for clinical/videomicroscopic dermatologic image compression. Additionally, its diffusion and other features, such as the possibility of embedding metadata in the image file and to encode various parts of an image at different compression levels, make it perfectly suitable for the current needs of dermatology and teledermatology.

In order to store digital image data in a pel-by-pel representation a huge amount of storage capacity is necessary. For example a DIN A4 page scanned with a resolution of 300 dpi (dots per inch) needs about 1.1 Mbyte storage capacity in binary representation. This paper deals with various methods to compress binary image data. The focus is to discuss their suitability for archiving usual business documents made up of text, graphics and halftone segments. The two-dimensional CCITT group 4 recommendation is compared with an adaptive prediction algorithm and a Classified Pel-Pattern (CLAP) method. For the latter a very efficient systematic code is given. The Classified Pel-Pattern algorithm achieves the best data compression but at the cost of high complexity. Compressing documents scanned with a resolution of 300 dpi the measured compression ratio is between 1.3–2.5 (halftone pictures) and 50 (line art). Considering only text and graphic documents the recommendation of the CCITT is 5–10% worse but complexity is significantly less. However, it is found that this methods show unsatisfactory results applied to halftone pictures.

Absolutely lossless compression technique has been recently proposed for medical image compression, It is a hybrid of lossless and lossy compression for medical images using difference image(1). It is a multi step process in which we have used lossy coding followed by difference image coding. The combination of two compression ratios is resultant. In this paper we analyze statistics associated with difference image. The main objective of this work is to exploit some statistical measures for a difference image to compress it losslessly. Difference image statistics plays a vital role in our technique to get maximum compression ratios. Proposed scheme is simple, computationally economical and can achieve higher compression ratios than existing standard lossless compression techniques and also meets the legal requirement of medical image archiving.

The redundancy in digital image coding can be classified into two categories: local and global. The algorithm proposed in this paper is based on these two characteristics. In this image compression scheme, the image is segmented into variable size blocks coded by three different coding schemes depending on the characteristics exhibited by the pixels within the block.

Lossless image compression has long been recognized as an important need for several applications such as medical imaging, storage of critical IR image sequences and remote sensing. In this paper, we propose a simple, fast and easy to realizable-on-hardware lossless video compression algorithm 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. Interframe coding is performed by encoding the redundant pixels in an efficient way, using one bit code. Finally the arithmetic coder is used as entropy coding. The proposed algorithm is able to operate in image compression mode as well as video compression mode. The proposed MPLVC (Median Predictor based Lossless Video Compression) algorithm is compared with JPEG-LS (Joint Pictures Experts Group-Lossless) and FELICS (Fast and Efficient Lossless Image Compression System) 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.

A lossless compression of an error-diffused image by lossless coding of entropy-minimized multi-level image, which can produce given halftone image at the receiver side, is studied. Through the study, block-type error diffusion is introduced to consider the two-dimensional entropy, and both of entropy-based prioritisation method and prediction-error-based prioritisation method in inverse halftoning procedure are proposed and compared. The result shows that the restored image by proposed methods has about 40% less entropy than that of the original multi-level image, but 50-70% larger than that of halftone image. However through the simulation of pseudo high resolution images, it was expected that the proposed method could even show less entropy than that of the bi-level image in higher resolution cases.

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.

A hypergraphs-based image representation is already used for 2D lossless image compression. In this paper we extend this hypergraph representation on 3D-image and we add an alpha-tolerance. This extended representation conducts to a generalisation of the HLC lossless compression algorithm (A. Bretto and and L. Gllllbert, 2005) for near-lossless 3D-image compression. We present an algorithm performing that compression and we give some experimental results proving its efficiency. This paper is a detailled version of the poster (L. Gillibert and A. Bretto, 2006)

In this paper, a modified medical image compression algorithm using cubic spline interpolation (CSI) is presented for telemedicine applications. The CSI is developed in order to subsample image data with minimal distortion and to achieve compression. It has been shown in literatures that the CSI can be combined with the JPEG algorithms to develop a modified JPEG codec, which obtains a higher compression ratio and a better quality of reconstructed image than the standard JPEG. However, this modified JPEG codec will lose some high-frequency components of medical images during compression process. To minimize the drawback arose from loss of these high-frequency components, this paper further makes use of bit-plane compensation to the modified JPEG codec. The bit-plane compensation algorithm used in this paper is modified from JBIG2 standard. Experimental results show that the proposed scheme can increase 20~30% compression ratio of original JPEG medical data compression system with similar visual quality. This system can reduce the loading of telecommunication networks and is quite suitable for low bit-rate telemedicine applications.

This paper provides an overall description of new image compression technology, Xena, and the strengths in its lossless compression capability and speed in comparison to JPEG2000 and JPEG_LS, the world standards in the continuous tone image compression field. Xena has achieved an extremely high compression speed over 20 times faster than that of JPEG2000, while the compression capability remains almost the same.

Data in medical images is very large and therefore for storage and/or transmission of these images, compression is essential. A method is proposed which provides high compression ratios for radiographic images with no loss of diagnostic quality. In the approach an image is first compressed at a high compression ratio but with loss, and the error image is then compressed losslessly. The resulting compression is not only strictly lossless, but also expected to yield a high compression ratio, especially if the lossy compression technique is good. A neural network vector quantizer (NNVQ) is used as a lossy compressor, while for lossless compression Huffman coding is used. Quality of images is evaluated by comparing with standard compression techniques available.

Several popular lossless image compression algorithms were evaluated for the application of compressing medical infrared images. Lossless JPEG, JPEG-LS, JPEG2000, PNG, and CALIC were tested on an image dataset of 380+ thermal images. The results show that JPEG-LS is the algorithm with the best performance, both in terms of compression ratio and compression speed

The JBIG2 binary image encoder dramatically improves compression ratios over previous encoders. The effectiveness of JBIG2 is largely due to its use of pattern matching techniques and symbol dictionaries for the representation of text. While dictionary design is critical to achieving high compression ratios, little research has been done in the optimization of dictionaries across stripes and pages. In this paper we propose a novel dynamic dictionary design that substantially improves JBIG2 compression ratios, particularly for multi-page documents. This dynamic dictionary updating scheme uses caching algorithms to more effciently manage the symbol dictionary memory. Results show that the new dynamic symbol caching technique outperforms the best previous dictionary construction schemes by between 13% and 46% for lossy compression when encoding multi-page documents. In addition, we propose a fast and low-complexity pattern matching algorithm that is robust to substitution errors and achieves high compression ratios.

We present a method for detecting steganography using plusmn1 embedding. The method uses a lossless compression technique to compress the last two bitplanes in an effort to model the image structure where the data may be hidden. A small number of statistics are then computed using the model and fed into a support vector machine to classify detection results. Results presented are obtained using k-fold cross-validation method using a large set of never compressed grayscale images. Detection results show improvement over current plusmn1 steganalysis methods.

The paper proposes a method of compressing floating-point images of arbitrary precision. The concept of floating point images is used frequently in such areas as high dynamic range imaging, where pixel data stored as 8 or 12-bit integers are insufficient. The compression scheme presented in the paper organizes the floating point data in a manner such that already existing compression algorithms such as JPEG or Zlib compression may be used once the data re-organization has taken place. The paper compares the result to a popular (but restrictive) form of image compression, openEXR, and shows significant gains over this format. Furthermore, the compression scheme presented is scalable to deal with floating point images of arbitrary precision.

A method of encoding digital medical mammograms is presented in this paper. Based on the JPEG2000 coding framework and embedded with an advanced perceptual distortion measure, the proposed coder identifies and removes visually insignificant/irrelevant information, so as to achieve perceptually lossless quality. Compared with the state-of-the-art JPEG compliant LOCO lossless coder and JPEG2000 lossless coder, current results have shown compression ratio gains in bit-rate without any loss of visual fidelity.

We present a context-weighting algorithm that adaptively weights in real-time three-context models based on their relative accuracy. It can automatically select the better model over different regions of an image, producing better probability estimates than using either one of these models exclusively. Combined with the previously proposed block arithmetic coder for image compression (BACIC), the overall performance is slightly better than JBIG for the eight CCITT business-type test images, outperforms JBIG by 13.8% on halftone images, and by 17.5% for compounded images containing both text and halftones. Furthermore, users no longer need to select models as in JBIG and BACIC to get the better performance.

Pre-/post- processing method is well-know and very effective approach for improving the compression of image. In this paper, we provide a survey and analysis of the recursive pre-/post- methods, and then proposed a new generalized recursive pre-/post- processing framework for near lossless image compression. The proposed framework is then applied to design several new recursive pre-/post- processing methods, and experimental results show that the proposed new recursive methods can perform outside of the existing methods.

In this chapter, EHW is applied to the lossless image compression, and it is implemented in a chip. The current international standard for bi-level image coding, JBIG2-AMD2, is modified by the proposed method to achieve high compression ratios, where compression parameters are optimized by the enhanced genetic algorithm (GA). The results of computer simulations show a 171% improvement in compression ratios with the proposed method compared to JBIG2 without optimization. The experiment shows that when the method is implemented by hardware with an evolvable hardware chip, the processing speed is dramatically faster than execution with software. This chapter also de-scribes activities concerning ISO standardization to adopt part of the technology used in this method to the JBIG2 standard.

Medical images are very important for diagnostics and therapy. However, digital imaging generates large amounts of data which need to be compressed, without loss of relevant information, to economize storage space and allow speedy transfer. In this research three techniques are implemented for medical image compression, which provide high compression ratios with no loss of diagnostic quality. Different image modalities are employed for experiments in which X-rays, MRI, CT scans, Ultrasounds and Angiograms are included. The proposed schemes are evaluated by comparing with existing standard compression techniques like JPEG, lossless JPEG2000, LOCOI and Huffman Coding. In a medical image only a small region is diagnostically relevant while the remaining image is much less important. This is called Region of Interest (ROI). The first approach compresses the ROI strictly losslessly and the remaining regions of the image with some loss. In the second approach an image is first compressed at a high compression ratio but with loss, and the difference image is then compressed losslessly. Difference image contain less data and is compressed more compactly than original. Third approach exploits inter-image redundancy for similar modality and same part of human body. More similarity means less entropy which leads to higher compression performance. The overall compression ratio is combination of lossy and lossless compression ratios. The resulting compression is not only strictly lossless, but also expected to yield a high compression ratio. These techniques are based on self designed Neural Network Vector Quantizer (NNVQ) and Huffman coding. Their clever combination is used to get lossless effect. These are spatial domain techniques and do not require frequency domain transformation. An overall compression ratio of 6-14 is obtained for images with proposed methods. Whereas, by compressing same images by a lossless JPEG2K and Huffman, compression ratio of 2 is obtained at most. The main contribution of the research is higher compression ratios than standard techniques in lossless scenario. This result will be of great importance for data management in a hospital and for teleradiology.

A new lossy-to-lossless quality-progressive method for image coding is presented. This method is based on binary tree decomposition and context-based arithmetic coding. Experimental results obtained with the eighteen 8-bit ISO images show that the proposed method attains an average lossless compression gain of 7.9% with respect to JPEG2000. Compared to JPEG-LS, the compression gain is 3.1%, but this compression standard does not allow lossy-to-lossless decoding.

An adaptive low complexity lossless segment (context) based color image compression method is proposed by this paper. First, adaptive context based dividing for an image is conducted to find optimal fixed length codes that are used for storing relative values of every pixel in each segment of the image and the length for each segment by a modified greedy algorithm. Then a modified Huffman coding is applied for the result of the first compression. The complexity of the first compression for the developed method is O(n). The developed method was tested by a typical set of natural images. For some type of images, the compression ratios are very high

Palette reordering is a well-known and effective approach for improving the compression of color-indexed images. The performance of most of the existing palette reordering methods has been evaluated using the compression rates attained by image coders which are based on predictive or transform coding technology, such as JPEG-LS or JPEG2000. In this paper, we show that image coding technology based on bit-plane coding, such as the one used by JBIG, provides a competitive performance in this class of images. Also, we present a palette reordering algorithm, adapted for bit-plane based encoders, that gives good results and is faster than Memon's or the modified Zeng's methods, the two most effective palette reordering algorithms.

Four dimensional (4D) medical data are sequences of volumetric images captured in time. These data sets are typically very large in size and demand a great amount of resources for storage and transmission. In this paper, we present a lossless compression technique for 4D medical images which is based on the H.264/AVC video coding standard. Our lossless compression technique efficiently exploits spatial and temporal redundancies between 2D image slices and 3D images in 4D medical images and eliminates any concerns regarding the effects of compression on image quality for diagnostic purposes. Performance evaluations have shown that the proposed compression technique outperforms current 4D compression methods by 70%.