Description: 本程序包括了数据压缩和解压的几种基本算法：算术编码，lz77，lzw；对于数据压缩的初学者了解和掌握这几种基本的数据压缩方法应该有帮助-the procedures include data compression and decompression of several basic algorithm : arithmetic coding, lz77, 4,558,302; Data compression for the beginners to understand and grasp these types of basic data compression method should help Platform: |
Size: 4810 |
Author:叶靥 |
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Description: 本程序实现了数据压缩中“算术编码”的全过程。执行环境为 VC++/Unix-realized by the data compression "arithmetic coding" of the entire process. The environment for the implementation of VC / Unix Platform: |
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Author:wubo |
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Description: 自适应算术编码 C语言This package was adapted from the program in \"Arithmetic Coding for
Data Compression\", by Ian H. Witten-adaptive arithmetic coding C language This package was adapted from t he program in "Arithmetic Coding for Data Compr ession ", by Ian H. Witten Platform: |
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Author:liuyong |
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Description: 本程序实现了数据压缩中“算术编码”的全过程。执行环境为 TC 3.0。-realized by the data compression "arithmetic coding" of the entire process. The environment for the implementation of TC 3.0. Platform: |
Size: 23759 |
Author:钟祖豪 |
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Description: 本程序实现了数据压缩中“算术编码”的全过程。执行环境为 VC++/Unix-realized by the data compression "arithmetic coding" of the entire process. The environment for the implementation of VC/Unix Platform: |
Size: 1370112 |
Author:wubo |
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Description: 用matlab实现算术编码，包含对数据的压缩和解压-Using matlab realize arithmetic coding, including the data compression and decompression Platform: |
Size: 16384 |
Author:lmg |
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Description: I ve written some many years ago dynamic Huffman algorithm to compress and decompress data. It is mainly targeted to data with some symbols occuring more often than the rest (e.g. having some data file consisted of 3 different symbols and their total number of occurence in that file s1(1000), s2(200), s3(30) so the total length of file is 1000+200+30=1230 bytes, it will be encoded assigning one bit to s1 and 2 bits to s2, s3 so the encoded length will be 1*1000+2*(200+30)=1460 bits=182 bytes). In the best case the file consisted of just one symbol will be encoded with compression ratio as 1:8. Huffman coding is used in image compression, however in JPEG2000 arithmetic codec is imployed. Platform: |
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Author:毛磊 |
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Description: 对信源数据，即exam，采用算术编码进行信源压缩编码-Source of data, that is, exam, using arithmetic coding for source compression coding Platform: |
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Author:luqi |
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Description: 图像压缩编码从本质上来说就是对要处理的图像源数据用一定的规则进行变换和组合,从而达到以尽可能少的代码来表示尽可能多的数据信息的目的.下面是图像压缩编码的源程序(shannon-fano编码)/(算术编码)/(位平面编码)/(预测编码)-Image compression coding is essentially to deal with the image of the source data used to change certain rules and combinations, so as to achieve as little as possible in the code as much as possible to express the purpose of the data. The following is the source of image compression coding procedures (shannon-fano code)/(arithmetic coding)/(bit-plane coding)/(predictive coding) Platform: |
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Author:刘亚宁 |
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Description: 算术编码是信源编码中的一种，是图像压缩的主要算法之一。 是一种无损数据压缩方法，也是一种熵编码的方法。-Arithmetic coding is a source coding, image compression is one of the main algorithms. Is a lossless data compression method, but also a method of entropy coding. Platform: |
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Author:zhangli |
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Description: Brief explanation with respect to Arithmetic coding an application in Data Compression, is given Platform: |
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Author:Nishant |
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Description: 熵编码(entropy encoding)是一类利用数据的统计信息进行压缩的无语义数据流之无损编码。本章先介绍熵的基本概念，然后介绍香农-范诺(Shannon-Fano)编码、哈夫曼(Huffman)编码、算术编码(arithmetic coding)、行程编码(RLE)和LZW编码等常用的熵编码方法。 哈夫曼编码建议了一种将位元进位成整数的算法，但这个算法在特定情况下无法达到最佳结果。为此有人加以改进，提供最佳整数位元数。这个算法使用二叉树来设立一个编码。这个二叉树的终端节点代表被编码的字母，根节点代表使用的位元。除这个对每个要编码的数据产生一个特别的表格的方法外还有使用固定的编码表的方法。比如加入要编码的数据中符号出现的机率符合一定的规则的话就可以使用特别的变长编码表。这样的编码表具有一定的系数来使得它适应实际的字母出现机率。-Entropy coding (entropy encoding) is a kind of use data of statistical information compression without semantic data flow of nondestructive coding. This chapter first introduces the basic concept of entropy, and then introduce Shannon- occupies (Shannon- Fano) coding, Huffman (Huffman) coding, arithmetic coding (arithmetic coding), stroke encoding (RLE) and LZW encoding used entropy coding method. Huffman encoding suggest a bit carry into integer algorithm, but this algorithm in specific cases not to achieve the best results. Therefore some improved, providing the best integer bit number. This algorithm using binary tree to set up a code. The binary tree terminal node representing the code letters, on behalf of the root node used bits. In addition to this for each to coded data to create a special form of the method and use fixed code table method. Such as join to coding data symbols appear probability accord with certain rules words can use special variable ChangBian chart. This code Platform: |
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Author:杨飞帆 |
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Description: The optimal predictors of a lifting scheme in the general n-dimensional case are obtained and applied
for the lossless compression of still images using rst quincunx sampling and then simple row-column
sampling. In each case, the e ciency of the linear predictors is enhanced nonlinearly. Directional post-
processing is used in the quincunx case, and adaptive-length postprocessing in the row-column case. Both
methods are seen to perform well. The resulting nonlinear interpolation schemes achieve extremely e cient
image decorrelation. We further investigate context modeling and adaptive arithmetic coding of wavelet
coe cients in a lossless compression framework. Special attention is given to the modeling contexts and
the adaptation of the arithmetic coder to the actual data. Experimental evaluation shows that the best of
the resulting coders produces better results than other known algorithms for multiresolution-based lossless
image coding.
- The optimal predictors of a lifting scheme in the general n-dimensional case are obtained and applied
for the lossless compression of still images using rst quincunx sampling and then simple row-column
sampling. In each case, the e ciency of the linear predictors is enhanced nonlinearly. Directional post-
processing is used in the quincunx case, and adaptive-length postprocessing in the row-column case. Both
methods are seen to perform well. The resulting nonlinear interpolation schemes achieve extremely e cient
image decorrelation. We further investigate context modeling and adaptive arithmetic coding of wavelet
coe cients in a lossless compression framework. Special attention is given to the modeling contexts and
the adaptation of the arithmetic coder to the actual data. Experimental evaluation shows that the best of
the resulting coders produces better results than other known algorithms for multiresolution-based lossless
image coding.
Platform: |
Size: 268288 |
Author:dee |
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Search - data compression arithmetic coding