A. Mo#at, R. M. Neal, and I. H. Witten. Arithmetic coding revisited. ACM Transactions on Information Systems, 16:256--294, 1998.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....highly unbalanced. On other hand, arithmetic coding tends to be slow, generally does not produce a prefix code, needs to indicate the end of the sequence and has poor error resistance. The arithmetic coding implementation used in this work was made available by Alistair Moffat, and was based on [20]. Decompression is achieved by applying the inverse stages of the compression method, in the reverse order. The inverse LCT transform of the blocks must be performed following an fixed order, because the complete decompression of one block depends on the decompression of its six neighbors and on ....

A. Moffat, R. Neal, and I.H. Witten. Arithmetic coding revisted. ACM Transactions on Information Systems, 16(3):256--294, 1998.

....new, more e#cient, algorithm is then introduced. We then present a new application for sparse data blocks in conjunction with linear error correcting codes for multiuser channels. 2 An arithmetic coding based sparsifier An algorithm for the generation of sparse blocks based on arithmetic coding [5] is presented in [4] The algorithm uses standard arithmetic coding but reverses the usual role of compression and decompression. The probabilistic model used is: Pr(s i =1 s 1 , s i 1 ) Pr(s i =1) p 1 (1) where s i is the ith bit of the sparse data. The algorithm is presented ....

Alistair Mo#at, Radford M. Neal, and Ian H. Witten. Arithmetic coding revisited. ACM Transactions on Information Systems, 16(3):256--294, July 1998.

....coding is natural since this scheme achieves near optimal compresssion with low variability in fitting with the theoretical analysis which assumes that optimal compression is feasible. We used a publicly available adaptive arithmetic compressor implemened by Carpinelli, Moffat, Neal, and Witten [13]. The compressor was run with default parameters and the bits option on. Similar highly compressed filtering mechanisms exist and would be interesting to try on the same problem. Lossy Dictionaries, for example, weigh each member of the set S, and uses a greedy algorithm to build a dictionary of ....

A. Moffat, R. Neal, and I.H. Witten. Arithmetic coding revisted. ACM Transactions on Information Systems, 16(3):256--294, 1998.

....the model to determine a code for each symbol. An e#cient coding scheme assigns short codes to common symbols and long codes to rare symbols, optimising code length overall. Adaptive schemes (where the model evolves as the data is processed) are currently favoured for generalpurpose compression [5, 6], and are the basis of utilities such as compress . However, because databases are divided into small records that must be independently decompressible [1] adaptive techniques are generally not e#ective. Moreover, the requirement of atomic decom pression precludes the application of vertical ....

A. Mo#at, R. Neal, and I. Witten. Arithmetic coding revisited. ACM Transactions on Information Systems, 1998. (To appear).

....gzipped) The third le in addition quantizes and delta codes the positions and the texture coordinates. However, the resulting loss in precision in not visible (22.8 KB gzipped) approximates the optimal compression possible in respect to the (context based) information entropyofasymbol sequence [16]. We can combine the advantages of arithmetic coding with that of a non binary ASCII coding by letting the arithmetic coder produce an ASCII string of zeros and ones instead of a binary bit stream. The Lempel Ziv coder [24] used by ###### ## ############ ###### ######### ######## 9 ######## ## ....

A. Moat, R. M. Neal, and I. H. Witten. Arithmetic coding revisited. ACM Transactions on Information Systems, 16(3):256-294, 1998.

....filter in order to achieve improved transmission size. We start by defining the problem as an optimization problem, which we solve using some simplifying assumptions. We then consider practical issues, including e#ective compression schemes and actual performance. We recommend arithmetic coding [9], a simple compression scheme well suited to this situation with fast implementations. Our work underscores an important general principle for distributed algorithms: when using a data structure as a message, one should consider the parameters of the data structure with both of these roles in ....

....are independent and each bit is 0 with probability p and 1 with probability 1 p, arithmetic coding compresses the string to near mH(p) bits with high probability, with the deviation from the average having a Cherno# like bound. For more information on arithmetic coding, we refer the reader to [9, 11]. For more precise statements and details regarding the low variability of arithmetic coding, we refer the reader to [7] More details will appear in the final version. Given this compression scheme, we suggest the following approach. Choose a maximum desired uncompressed size m. Then design a ....

[Article contains additional citation context not shown here]

A. Mo#at, R. Neal, and I. H. Witten. Arithmetic coding revisited. ACM Transactions on Information Systems, 16(3):256-294, July 1998.

....symbol stream into as few bits as possible. Arithmetic coding will always outperform the gzip coding that is applied to our ASCII symbol string. This is because arithmetic coding approximates the optimal compression possible in respect to the (contextbased) information entropy of a symbol sequence [15]. We can combine the advantages of arithmetic coding with that of a non binary ASCII coding by letting the arithmetic coder produce an ASCII string of zeros and ones instead of a binary bit stream. The Lempel Ziv coder [22] used by the standard gzip coders is able to compress the resulting ASCII ....

A. Moffat, R. M. Neal, and I. H. Witten. Arithmetic coding revisited. ACM Transactions on Information Systems, 16(3):256--294, 1998.

....filter in order to achieve improved transmission size. We start by defining the problem as an optimization problem, which we solve using some simplifying assumptions. We then consider practical issues, including e#ective compression schemes and actual performance. We recommend arithmetic coding [11], a simple compression scheme well suited to this situation with fast implementations. We follow by showing how to extend our work to other important cases, such as in the case where it is possible to update by sending changes (or deltas) in the Bloom filter rather than new Bloom filters. Our ....

....are independent and each bit is 0 with probability p and 1 with probability 1 p, arithmetic coding compresses the string to near mH(p) bits with high probability, with the deviation from the average having a Cherno# like bound. For more information on arithmetic coding, we refer the reader to [11, 16]. For more precise statements and details regarding the low variability of arithmetic coding, we refer the reader to the Appendix. We note that other compression schemes may also be suitable, including for example run length coding. Given this compression scheme, we suggest the following ....

[Article contains additional citation context not shown here]

A. Mo#at, R. Neal, and I. H. Witten. Arithmetic coding revisited. ACM Transactions on Information Systems, 16(3):256-294, July 1998.

....details corresponding to different frequency bands [6, 36] Details of a certain frequency can efficiently be added or removed to switch between resolutions. These details are represented by wavelet coefficients that typically have small absolute values and can be compressed by arithmetic coding [31]. Wavelets are thus a useful tool the concise, progressive transmission of scientific data [38] An important tool for wavelet construction is the lifting scheme, described by Sweldens [37] A similar technique was developed earlier by Dahmen [7] Wavelet lifting makes it possible to design ....

A. Moffat, R.M. Neal, and I.H. Witten, Arithmetic coding revisited, ACM Transactions on Information Systems, Vol. 16, No. 3, ACM, July 1998, pp. 256--294.

....options are to use Hu#man coding or arithmetic coding. Using Hu#man code as discussed in Stone [Sto86] is very fast, but is much less flexible than arithmetic coding. Cameron [Cam88] shows that arithmetic coding is more appropriate for good compression results and recent improved implementations [MNW98] make it also very fast. An arithmetic coder [WNC87] is a flexible means to encode a number of choices if each alternative i # 1, 2, n has a certain probability p i , where # n i=1 p i = 1 and n is given by the kind of choice node. The tuple M = 5 (p 1 , p 2 , p n ) is ....

Alistair Mo#at, Radford M. Neal, and Ian H. Witten. Arithmetic coding revisited. ACM Transactions on Information Systems, 16(3):256--294, 1998.

....10, 2000 Hu man coding because Hu man coding is restricted to translate each symbol into integer number of bits. The basic idea of arithmetic coding is the same as the principle of data compression: the more probable a symbol is, the fewer bits the symbol should use. Recently, an improved version [24] of the classic method [31] can take the contexts of input symbols into account. Therefore, we utilize this context based property to incorporate repeatedly appeared LVQ patterns. From our experiments, the revised method can produce the reduction of storage up to 15 , compared with the classic ....

A. Moat, R. M. Neal, and I. H. Witten, \Arithmetic coding revisited", ACM Transactions on Information Systems, vol. 16, no. 3, pp. 256-294, 1998.

No context found.

A. Mo#at, R. M. Neal, and I. H. Witten. Arithmetic coding revisited. ACM Transactions on Information Systems, 16:256--294, 1998.

No context found.

A. Mo#at, R.M. Neal, and I.H. Witten, Arithmetic coding revisited, ACM Transactions on Information Systems, Vol. 16, No. 3, 1998, pp. 256--294.

No context found.

A. Moffat, R.M. Neal, and I.H. Witten, Arithmetic coding revisited, ACM Transactions on Information Systems, Vol. 16, No. 3, ACM, July 1998, pp. 256--294.

No context found.

Mo#at, A., Neal, R.M., Witten, I.H.: Arithmetic coding revisited. ACM Transactions on Information Systems 16 (1998) 256--294

No context found.

Alistair Mo#at, Radford M. Neal, and Ian H. Witten. Arithmetic coding revisited. ACM Transactions on Information Systems, 16(3):256--294, July 1998.

No context found.

A. Moat, R. M. Neal, and I. H. Witten. Arithmetic coding revisited. ACM Transactions on Information Systems, 16:256-294, 1995.

No context found.

R. M. Moffat, Neal and I. H. Witten, Arithmetic Coding Revisited, ACM Transactions on Information Systems, 16.3: (1998).

Online articles have much greater impact   More about CiteSeer.IST   Add search form to your site   Submit documents   Feedback  

CiteSeer.IST - Copyright Penn State and NEC