James A. Storer. Data Compression: Methods and Theory. Computer Science Press, 1988.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....Chapter 4. In 1984, Welch increased the eciency of LZ78 with a new procedure, now known as LZW [37] In practice, LZW is much preferred over LZ78, but for our purposes the di erence is small. Also in the 1970 s, Storer and Szymanski were exploring a wide range of macrobased compression schemes [33, 35, 34]. They de ned a collection of attributes that such a compressor might have, such as recursive , restricted , overlapping , etc. Each combination of these adjectives described a di erent scheme, many of which they considered in detail. Apparently, none of these was precisely the context free ....

....in Section 3.4. 3.1 NP Hardness Theorem 5 There is no polynomial time algorithm for the smallest grammar problem with approximation ratio less than 8569=8568 unless P = NP. Proof. We use a reduction from a restricted form of vertex cover based closely on arguments by Storer and Szymanski [35, 34]. Let H = V; E) be a graph with maximum degree three and jEj jV j. We can map the graph H to a string over an alphabet that includes a distinct terminal (denoted v i ) corresponding to each vertex v i 2 V as follows: #v i j v i # j) #v i # j) v i ;v j )2E (#v i #v j # j) ....

James A. Storer. Data Compression: Methods and Theory. Computer Science Press, 1988.

....of u and v and for every w the following condition is true: u v w v v w = jwj jw j : The length of a shortest common supersequence is denoted by S(u; v) Creating shortest common supersequences is natural when merging sequences. This is useful for some types of compression [Sto88] or for efficient planning [FLY92] Longest common subsequences and shortest common supersequences are dual in the following sense. S(u; v) L(u; v) juj jvj : Proof. The lemma is a consequence of the simple property of finite sets: jAj jBj = jA Bj jA [ Bj : Corollary 5.2 Let ES n ....

....and the shortest common superstring problems. It can be shown that the expected length of a shortest common superstring is 2n Gamma O(1) The problem of finding a shortest common superstring for more sequences is more interesting. Common superstrings have applications in data compresson [Sto88] Gallant at al. GMS80] has shown that the shortest common superstring problem is NP complete. Middendorf [Mid94] have shown that the shortest common superstring problem over the binary alphabet remains NP complete even if each given sequence contains exactly three ones. Approximation algorithms ....

James A. Storer. Data Compression: Methods and Theory. Computer Science Press, Rockville, Maryland, 1988.

....types of redundancy found in a le. The shortcoming of such algorithms is that they assume, often inaccurately, that les are homogeneous throughout. Consequently, each exploits only a subset of the redundancy found in the le. Unfortunately, no algorithm is e ective in compressing all les [Sto88]. For example, Hu man encoding works best on data les with a high variance in the frequency of individual characters (including some graphics and audio data) achieves mediocre performance on natural language text les, and performs poorly in general on high redundancy binary data. On the other ....

....of the results and possibilities for future work are presented. This denotes, in our system, a le with a low rate of repeated strings. 2 Related Work It is a fundamental result of information theory that there is no single algorithm that performs optimally in compressing all les [Sto88]. However, much work has been done to develop algorithms and techniques that work nearly optimally on all classes of les. In this section we discuss adaptive algorithms, composite algorithms, and four popular commercial compression packages. 2.1 Adaptive Compression Algorithms and Composite ....

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James A. Storer. Data Compression: Methods and Theory. Computer Science Press, Rockville, MD, 1988.

....not larger than the corresponding sum in the right subtree. 1.2 Breaking Huffman Codes: An Overview of the Problem The field of data compression has grown vigorously since Huffman s paper. Hamming [1] discusses the information theory behind some of the basic compression techniques. Storer s book [7] gives a nice survey of the field. Adaptive compression schemes that adaptively adjust to changing source symbol statistics have been developed; Knuth s paper [6] gives one example. Jones [3] presents another interesting method, based on the use of splay trees, and provides an excellent discussion ....

James A. Storer. Data Compression: Methods and Theory. Computer Science Press, 1988. 35

....examine the first three in the next three subsections, and present SVD in detail in the next section. 2.1 String Compression Algorithms for lossless string compression are widely available (e.g. gzip, based on the well known Lempel Ziv algorithm [29] Huffman coding, arithmetic coding, etc. see [23]) While these techniques can achieve fairly good compression, the difficulty with them has to do with reconstruction of the compressed data. Given a query that asks about some customers or some days, we have to uncompress the entire database, for all customers and all days, to be able to answer ....

James A. Storer. Data Compression: Methods and Theory. Computer Science Press, Inc., 1988.

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James A. Storer, Data Compression: Methods and Theory, Computer Science Press, Rockville, MD, 1988.

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James Andrew Storer, Data compression: methods and theory, ISBN 0-88175-161-8, Computer Science Press, Rockville, Maryland, 1988.

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James A. Storer. Data Compression: Methods and Theory. Computer Science Press, 1988.

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James A. Storer. Data Compression: Methods and Theory. Computer Science Press, 1988.

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