Over the last decade, arithmetic coding has emerged as an important compression tool. It is now the method of choice for adaptive coding on multisymbol alphabets because of its speed, low storage requirements, and effectiveness of compression. This article describes a new implementation of arithmetic coding that incorporates several improvements over a widely used earlier version by Witten, Neal, and Cleary, which has become a de facto standard. These improvements include fewer multiplicative operations, greatly extended range of alphabet sizes and symbol probabilities, and the use of low-precision arithmetic, permitting implementation by fast shift/add operations. We also describe a modular structure that separates the coding, modeling, and probability estimation components of a compression system. To motivate the improved coder, we consider the needs of a word-based text compression program. We report a range of experimental results using this and other models. Complete source code is available.

The MANTIS MultimodAl system for NeTworks of In-situ wireless Sensors provides a new multithreaded cross-platform embedded operating system for wireless sensor networks. As sensor networks accommodate increasingly complex tasks such as compression, aggregation and signal processing, preemptive multithreading in the MANTIS sensor OS (MOS) enables micro sensor nodes to natively interleave complex tasks with time-sensitive tasks, thereby mitigating the bounded buffer producer-consumer problem. To achieve memory efficiency, MOS is implemented in a lightweight RAM footprint that fits in less than 500 bytes of memory, including kernel, scheduler, and network stack. To achieve energy efficiency, the MOS power-efficient scheduler sleeps the microcontroller after all active threads have called the MOS sleep() function, reducing current consumption to the µA range. A key MOS design feature is flexibility in the form of cross-platform support and testing across PCs, PDAs, and different micro sensor platforms. Another key MOS design feature is support for remote management of in-situ sensors via dynamic reprogramming and remote login.

We explore the use of compression methods to improve the middleware-based exchange of information in interactive or collaborative distributed applications. In such applications, good compression factors must be accompanied by compression speeds suitable for the data transfer rates sustainable across network links. Our approach combines methods that continuously monitor current network and processor resources and assess compression effectiveness, with techniques that automatically choose suitable compression techniques. By integrating these techniques into middleware, there is little need for end user involvement, other than expressing the target rates of data transmission. The resulting network- and user-aware compression methods are evaluated experimentally across a range of network links and application data, the former ranging from low end links to homes, to wide-area Internet links, to high end links in intranets, the latter including both scientific (binary molecular dynamics data) and commercial (XML) data sets. Results attained demonstrate substantial improvements of this adaptive technique for data compression over non-adaptive approaches, where better compression methods are used when CPU loads are low and/or network links are slow, and where less effective and typically, faster compression techniques are used in high end network infrastructures. Index terms-- communication lines, compression I.

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Most data that is inherently discrete needs to be compressed in such a way that it can be recovered exactly, without any loss. Examples include text of all kinds, experimental results, and statistical databases. Other forms of data may need to be stored exactly, such as images---particularly bilevel ones, or ones arising in medical and remotesensing applications, or ones that may be required to be certified true for legal reasons. Moreover, during the process of lossy compression, many occasions for lossless compression of coefficients or other information arise. This paper surveys techniques for lossless compression. The process of compression can be broken down into modeling and coding. We provide an extensive discussion of coding techniques, and then introduce methods of modeling that are appropriate for text and images. Standard methods used in popular utilities (in the case of text) and international standards (in the case of images) are described. Keywords Text compression, ima...

Despite the enormous growth in storage capacity in recent years, the search for fast and effi-cient text compression algorithms continues. As processor speed is increasing at a higher rate than disk access time is decreasing, there is now even more reason to store information in a compressed form than there was previously. Prediction by Partial Matching (PPM), first published in 1984, was a significant step forward in the quest for efficient text compression. The Burrows Wheeler transform (BWT), introduced ten years later, has been the next significant breakthrough; its best implementations rank along-side those of PPM. In most BWT implementations, transformed text is converted to a string of ranks with a move-to-front (MTF) or similar mechanism before being compressed. Ranks are then encoded with an Order-  model or a hierarchy of such models, with some substrings of repeated ranks encoded as run lengths. Although these rank based methods perform very well, the transfor-mation to MTF numbers blurs the distinction between individual symbols and is a possible cause of ineffectiveness. Instead of relying on symbol ranking, we examine the problem of modelling the transformed text as a sequence of segments with iid symbols, using three different techniques.

Suppose that $S$ is a string of length $m$ drawn from an alphabet of $n$ characters, $d$ of which occur in $S$. Let $P$ be the relative frequency distribution of characters in $S$. We present a new algorithm for dynamic coding that uses at most \(\lceil \lg n \rceil 1\) bits to encode each character in $S$

Compression of digital images has been a topic of research for many years and a number of image compression standards has been created for different applications. The role of compression is to reduce bandwidth requirements for transmission and memory requirements for storage of all forms of data. While todaymorethanever before new technologies provide high speed digital communications and large memories, image compression is still of major importance, because along with the advances in technologies there is increasing demand for image communications, as well as demand for higher quality image printing and display. In this work we focus on some key new technologies for image compression, namely wavelet based image coders. Wavelet coders apart from offering superior compression ratios have also very useful features such as resolution scalability,i.e. they allow decoding a given image at a number of different resolutions depending on the application. We start by presenting in a simple manner a collection of tools and techniques

Traditional compression techniques do not look deeply into the morphology of languages. This can be less critical in languages like English where most of the sequences are illegal according to the grammatical rules of the language, for example, zx, bv or qe; hence the morphology can add a little information that can be beneficial for the compression algorithm. However, this negligence can be a significant flaw in languages like Hebrew where the grammatical rules allow much more freedom in the sequences of letters and, except tet after gimel, any pair is legal; hence compressing without taking the morphological rules into account can yield a poorer compression ratio. This article suggests a tool that optimizes the Burrows-Wheeler algorithm which is an unaware morphological rules compression method. It first preprocesses a Hebrew text file according to the Hebrew conjugation rules, and, after that, it provides the Burrows-Wheeler algorithm with this preprocessed file so that can be compressed better. Experimental results show a significant improvement.

The use of genetic programming for adaptive text compression