This paper surveys a variety of data compression methods spanning almost forty years of research, from the work of Shannon, Fano and Huffman in the late 40's to a technique developed in 1986. The aim of data compression is to reduce redundancy in stored or communicated data, thus increasing effective data density. Data compression has important application in the areas of file storage and distributed systems. Concepts from information theory, as they relate to the goals and evaluation of data compression methods, are discussed briefly. A framework for evaluation and comparison of methods is constructed and applied to the algorithms presented. Comparisons of both theoretical and empirical natures are reported and possibilities for future research are suggested. INTRODUCTION Data compression is often referred to as coding, where coding is a very general term encompassing any special representation of data which satisfies a given need. Information theory is defined to be the study of eff...

We describe the implementation of a data compression scheme as an integral and transparent layer within a full-text...

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...

. Given an alphabet \Sigma = fa1 ; : : : ; ang with a corresponding list of positive weights fw1 ; : : : ; wng and a length restriction L, the length-restricted prefix code problem is to find, a prefix code that minimizes P n i=1 w i l i , where l i , the length of the codeword assigned to a i , cannot be greater than L, for i = 1; : : : ; n. In this paper, we present an efficient implementation of the WARM-UP algorithm, an approximative method for this problem. The worst-case time complexity of WARM-UP is O(n log n +n log wn ), where wn is the greatest weight. However, some experiments with a previous implementation of WARM-UP show that it runs in linear time for several practical cases, if the input weights are already sorted. In addition, it often produces optimal codes. The proposed implementation combines two new enhancements to reduce the space usage of WARM-UP and to improve its execution time. As a result, it is about ten times faster than the previous implementat...

The spread of computing has led to an explosion in the volume of data to be stored on hard disks and sent over the Internet. This growth has led to a need for "data compression", that is, the ability to reduce the amount of storage or Internet bandwidth required to handle this data. This paper provides a survey of data compression techniques. The focus is on the most prominent data compression schemes, particularly popular.DOC,.TXT,.BMP,.TIF,.GIF, and.JPG files. By using different compression algorithms, we get some results and regarding to these results we suggest the efficient algorithm to be used with a certain type of file to be compressed taking into consideration both the compression ratio and compressed file size.

The minimum redundancy prefix code problem is to determine, for a given list W ={w 1 , ..., w n } of n positive symbol weights, a list L =#` 1 ;:::;` n # of n corresponding integer codeword lengths such that P i=1 2 ,` i # 1 and P i=1 w i ` i is minimized. Let us consider the case where W is already sorted. In this case, the output list L can be represented by a list M =#m 1 ;:::;m H #, where m ` , for ` = 1;:::;H, denotes the multiplicity of the codeword length ` in L and H is the length of the greatest codeword. Fortunately, H is proved to be O(min{log(1/p 1 ), n}), where p 1 is the smallest symbol probability, given by w 1 = P i=1 w i . In this paper, we present the F-LazyHuff and the E-LazyHuff algorithms. F-LazyHuff runs in O(n) time but requires O(min{H&sup2, n}) additional space. On the other hand, E-LazyHuff runs in O(n log(n/H)) time, requiring only O(H) additional space. Finally, since our two algorithms have the advantage of not writing at the input buffer during the code calculation, we discuss some applications where this feature is very useful.

In this paper we study the adaptive prefix coding problem in cases where the size of the input alphabet is large. We present an online prefix coding algorithm that uses O(σ 1/λ+ǫ) bits of space for any constants ε> 0, λ> 1, and encodes the string of symbols in O(log log σ) time per symbol in the worst case, where σ is the size of the alphabet. The upper bound on the encoding length is λnH(s)+(λln 2+2+ǫ)n+O(σ 1/λ log 2 σ) bits. 1