基于静态字典的数据压缩无损位隔离算法

Md. Tariqul Islam, Romana Rahman Ema, Md. Riadul Islam, Tajul Islam
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引用次数: 3

摘要

数据压缩是通过消除相同数据位集的重复来减少数据的位或实际大小,以达到比主要数据位少的目的。字符加密是一种著名的统计数据压缩方法,它包括每天的记录样本,每个记录都有一些合适的编码公式。这种编码是一种对应编码方法的消遣,它包含了字符字符,用不同寻常的数据和符号装饰起来。数据压缩扩展到广泛的引用领域,包括数据存储、数据库技术和数据传输。本文提出了一种无损比特隔离(LBI)的统计压缩方法,它真实地体现在一种字符编码技术上。使用集合、子集和静态字典可以找到这些孤立的字符。同样,为了吸收LBI的结果,使用了两个最受欢迎的算法术语LZW和Huffman。咨询方法的研究结果实现了常见的压缩比0.52,并且再次优于LZW和Huffman。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Lossless Bit Isolation Algorithm for Data Compression by Using Static Dictionary
Data compression is the reduction of bit or actual size of data by eliminating the repetition of identical sets of data bits to achieve fewer bits than the primary one. Character encryption is a famous time period of statistics compression that includes an everyday sample of records individual with a few affable encoding formulations. This encoding is a pastime of corresponding encoding approach that encompasses character characters, dolled up with extraordinary sorts of data and emblems. Data compressions expand over a large field of citation including data storage, database technology, and data transmission. In this paper, a statistics compression method denoted as Lossless Bit Isolation (LBI) has been implemented which honestly manifested on a character encoding technique. These isolated characters have found using set, sub-set, and static dictionary. Similarly, for assimilating the outcome of LBI two most well-liked algorithm terms LZW and Huffman are used. The investigational consequences of the counseled approach carry off common compression ratio 0.52 and again talented than LZW and Huffman.
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