M. Nelson. The Data Compression Book. Henry Holt and Co., Inc., New York, NY, USA, 1991.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....construction raises a critical issue on the data integrity due to error propagation. To speed up the massive comparisons on generalpurpose processors, traditional software algorithms, including hash table lookup and tree structured searching, have been developed and applied in storage systems [Nelson 96] For real time applications over several hundred Mbps communication channels, coprocessors with dedicated parallel processing architectures provide an alternative to achieve higher throughput. Two major hardware architectures for LZ compression are ContentAddressable Memory(CAM) based approach ....

....is shown in table 4. The algorithm of Using a separate decoding dictionary without parity check. Using a shared dictionary between encoding and decoding with 1 bit parity check per cell. Each of the five Xilinx XC4036XLA FPGAs on WILDFORCE board contains 1296 CLBs. 10 the C codes is from [Nelson 96] Here, the inverse comparison CED with the minimum execution time overhead is applied in WILDFORCE system, while the C programs on general purpose processors do not have any CED scheme. Note that since the decoding process is not in the critical path, the clock rate on WILDFORCE emulation is ....

Nelson, M., and J. L. Gailly, The Data Compression Book, 2nd edition, M&T Books, 1996.

....stage in JPEG [15] Huffman codes belongs into a family of codes with a variable length not a fixed length. That means that individual letter which makes a file is encoded with bit sequences that have distinct length. This characteristic of the code words helps to decrease the amount of redundancy [6, 14, 16, 21] in message data i.e. it makes data compression possible. Decreasing of redundancy in data by Huffman codes is based on the fact that distinct letters have distinct probabilities of incidence. This fact helps to create such code words, which really contribute to decreasing of redundancy i.e. to ....

.... constructing highly an efficient coding (or encoding) for finite source alphabets in 1952 [6] This method is known as the Huffman coding (or the Huffman s algorithm) and the corresponding code of a Huffman coding (or the code generated by the Huffman s algorithm) is said to be the Huffman code [4,14 15]. 2. Optimal Maximal Prefix Coding Schemes First, it is proven that existence of the optimal maximal prefix coding for finite source alphabets and that all Huffman coding schemes are optimal maximal prefix coding schemes. Theorem 1 Let I = S , P) be a finite information source. Then all ....

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M. Nelson, The Data Compression Book, M&T Books, New York, 1996.

....standards, JPEG is the most widely used one. JPEG is also employed in video compression standards such as MPEG and H.263. JPEG partitions an image into 8x8 blocks of data and encodes each block serially. The JPEG encoding process is comprised of lossy coding followed by a lossless entropy coding [1, 2]. The lossy encoding encodes a block of image data using the DCT (Discrete Cosine Transformation) and a quantization process. The entropy coding applies the run length coding (RLC) first and then a variable length coding (VLC) RLC encodes the run of an item into a two tuple, the item and its run ....

....run length coding (RLC) first and then a variable length coding (VLC) RLC encodes the run of an item into a two tuple, the item and its run length. To further compress the data, JPEG employs a VLC such as Huffman coding and arithmetic coding, which exploits repetition of twotuples encoded by RLC [2, 3, 4]. We observed that identical strings often appear in contiguous blocks of the data for simple images. However, the JPEG entropy encoding, which aims to exploit repetition of data within a block boundary, does not take the advantage of repetition of strings across block boundaries. In our previous ....

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. M. Nelson, The Data Compression Book. New York: M&T Books, 1992.

....dispersed in the crowd. 1 2 Protocol Header Protocol Header Transformation Joint 1 Transformation Joint 2 Figure 16: Body animation performed by a series of network messages, one for each joint transformation. The resulting message can even be compressed by using classical methods [17] [12] as long as the increased CPU load for compressing the data is of little significance compared to the reduction in band width use and transmission time. Protocol Header Transformation Joint 1 Transformation Joint 2 3 4 Joint 3 Figure 17: Using message aggregation, animating a body from one frame ....

Nelson, M. and Gaily, J.L. "The Data Compression Book", MIS Press, 1996.

....offsets and string lengths; they are also used to encode the tables of Huffman codes. 5. Redundant constant pool entries are eliminated. Figure 1 shows the rearrangement of information that takes place when a Jazz file is created from two class files. 3. 1 Huffman Codes Huffman coding [1] [5] is an optimal method of assigning variable length codes to symbols, where the symbols occur independently and randomly with known probabilities. Jazz uses Huffman codes for all indexes into the constant pool. The constant pool contains more than one type of information and one Huffman table is ....

....one table, because of duplication of indexes to particular strings in the Huffman tables, but the advantage of smaller Huffman codes to the frequently referenced class names could be great. Another potential optimization would be to eliminate the Huffman tables and use an adaptive Huffman code [1][5] instead. This would eliminate the overhead of the Huffman table while increasing the run time cost of the algorithm. Adaptive Huffman codes yield similar compression efficiency, but the need to continuously update the Huffman table as each symbol is decoded adds an additional run time burden. ....

M. Nelson and J.-L. Gailly. The Data Compression Book, 2nd Edition. M & T Books, New York, 1995.

....The transform coe#cients in each band exhibit unique statistical properties that can be used for encoding the image. For image compression, quantizers can be designed specifically for each band. The quantized coe#cients can then be entropy coded using either Hu#man coding or arithmetic coding [86, 87, 88]. In embedded coding, a key issue is to embed the more important information at the beginning of the bit stream. From a rate distortion point of view, one wants to quantize the coe#cients that cause larger distortion first. Let the wavelet transform be c = T (p) where p is the collection of image ....

....images are in YUV 4:1:1 format. For the SPIHT and JPEG algorithms, the images are converted to RGB 4:4:4 format from the YUV 4:1:1 format. In our experiments, the wavelet decomposition was based on Daubechies 9 7 tap filter bank [94] Adaptive arithmetic coding is used as the entropy encoder [87, 88]. The size of the image, the levels of wavelet transform, the initial threshold T , and the maximum data rate is embedded at the beginning of the bit stream as the header information. The image is decoded at di#erent data rates within the range of 0.5 bits per pixels (bpp) to 1.5 bpp. The ....

M. Nelson and J. Gailly, The data compression book. M&T Books, 1996.

....the phrase is encoded as a pair of values corresponding to the position of the phrase in the buffer and the length of the phrase. Besides the above general data compression algorithms, there are many compression methods designed for special applications such as speech, image, and video [8] [10] A compression algorithm for test data should meet two requirements: it should be lossless and have simple decompression. As the decompression is performed on the ATE side, the decompression time should be minimized to reduce the overall download time. However, compression time does not ....

.... was to compare the compression ratio of the proposed method with that of six well known compression methods, Huffman [1] arithmetic coding [2] compress, gzip, LZSS [7] and LZW [5] For the experiments, we implemented Huffman, arithmetic, LZW, and LZSS methods based on the programs available in [8], and used UNIX and GNU utilities for compress and gzip , respectively. Table 5 shows compression ratios achieved for the seven different methods. The size of D i is set to 15,000 for the proposed method. In other words, the test set is not partitioned into submatrices. The column headings for ....

M. Nelson and J-L Gailly, The Data Compression Book, M&T Books, 1996.

....uh # ## #ir#y hqrq#s. #qv x##pux# ## . htr# . thv h## AA s#qh## pir # #qv x#v #qv p rq#v#b d## Traditional compression techniques are not useful in this context. They achieve large compression rates that are very useful for archiving (e.g. Arithmetic Coding, Lempel Ziv, Huffman Coding, [2,5,7,8]. However, the compressed data sets are not directly queriable without prior decompression of whole blocks, due to the variable length of the compressed fields. The term variable length field refers to the usage of a variable number of bits to represent compressed values. ....

M. Nelson, J-L Gaily, "The Data Compression Book", 2nd edition, 1996 - M&T Books, ISBN 1-55851-434-1.

....stage in JPEG [15] Huffman codes belong into a family of codes with a variable length not a fixed length. That means that individual letter which makes a file is encoded with bit sequences that have distinct length. This characteristic of the code words helps to decrease the amount of redundancy [6, 14, 16, 21] in message data i.e. it makes data compression possible. Decreasing of redundancy in data by Huffman codes is based on the fact that distinct letters have distinct probabilities of incidence. This fact helps to create such code words, which really contribute to decreasing of redundancy i.e. to ....

....the total number of letters to be 1 k(r 1) where k is the number of levels in the tree. Therefore, we add enough dummy letters so that the total number of information source alphabet is of r k(r 1) The details of r ary (r 3) Huffman codes can be referred to References [4] p. 92 94) and [14] (p.67 69) Proof of Theorem 1: Since the r ary (r 2) Huffman code generated by the Huffman algorithm for any given finite information source alphabet is exactly corresponding to a complete r ary tree (by Lemma 4) Also, it is known that a complete r ary tree has to be corresponding to a maximal ....

M. Nelson, The Data Compression Book, M&T Books, New York,

....have used the Huffman code in the entropy encoder. Arithmetic coding appears to be much more effective to complete our encoder, since it bypasses the idea of replacing an input symbol with a specific code. Instead, it replaces a stream of input symbols with a single floating point output numbers [12]. In our encoder, the arithmetic coding has been implemented to work with only sixteen symbols in hexadecimal. 3. EXPERIMENTAL RESULTS The evaluation has been carried out on a Intel Pentium III 450 MHz bi processor with 256 Mbytes of RAM, but we have only used one processor. The operating system ....

M. Nelson and J.-L. Gailly. The Data Compression Book. M&T Books, 2 edition, 1996.

....= 1 , p : a= a 2 b 2 ) b= a 2 b 2 ) The Dual2 transform has the feature that the line passes through its dual point. Compression in Geometry Compression trades off space and time against error. Numerous techniques have been developed for (lossy and lossless) compression of images [24, 34, 37, 40, 43]. These techniques are aimed at reducing storage space and transmission time. More recently, there has been a flurry of activity aimed at the compression of geometric structures. This work applies to polyhedra and unstructured meshes. The goal is to develop schemes that generate lossy or lossless ....

M. Nelson. The Data Compression Book. M&T books, 2nd edition, Nov. 1995.

....8 employing the same BCH channel code, while a Huffman source coding scheme is used on the same source. The Huffman code is a very popular source coding scheme, which is often combined with other higher level compression or transformation techniques (such as JPEG, LZ, and other schemes) [8]. 5.2 Optimised Channel Coding The Tunstall coding technique exhibits certain properties which are very different from more common fixed to variable source coding methods. This suggests the use of a channel coding technique optimised for a source already encoded with an appropriate Tunstall ....

M. Nelson, The Data Compression Book. M&T Books, 1991.

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M. Nelson. The Data Compression Book. Henry Holt and Co., Inc., New York, NY, USA, 1991.

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Mark Nelson, Jean-Loup Gailly. The Data Compression Book. Ed. M&T Books. ISBN 1-55851-434-1. 1996.

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Nelson M., The Data Compression Book, M & T Publishing, Inc., (1991)

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M. Nelson and J.-L. Gailly, The Data Compression Book, 2 nd Edition. M & T Books, 1995.

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M. Nelson and J.-l. Gailly, The Data Compression Book, 2 ed., NY, USA: M&T Books, 1995.

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M. Nelson and J. L. Gailly. The Data Compression Book. M & T Books, New York, 1996.

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Mark Nelson. The Data Compression Book. M&T Publishing Company, Redwood City, California, 1991.

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M. Nelson, J. Gailly, The Data Compression Book, Second Edition, M&T Books, 1996. 8

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Mark Nelson. The Data Compression Book. BPB Publications, New Delhi, 1996. 64

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M. Nelson and J.I. Gailly, "The data compression book", M&T Books New York, NY 1995

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Nelson, M., and J. L. Gailly, The Data Compression Book, 2nd edition, M&T Books, 1996.

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Nelson, M. 1992. The Data Compression Book. M&T Books.

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M. Nelson, The Data Compression Book,M&TBooks, Redwood City, CA, USA, 1991.

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