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Experimental Analysis on the Performance of a New Preprocessing Method Used in Data Compression

机译:数据压缩新预处理方法性能的实验分析

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This paper presents a new text transformation method, which has a few similarities with the StarNT text transformation method. StarNT is a dictionary-based lossless text transform algorithm. Many different compression methods have been devised by researchers to find a suitable solution of data transmission that utilises the entire network bandwidth optimally and which also achieves a higher compression ratio. Most of the approaches that are being used, like the Prediction by Partial Matching (PPM), Burrows-Wheeler Transform have been unable to achieve the best possible output as provided by theoretical calculations and hence have left researchers to find more efficient techniques of text compression. Further in this paper we also provide the experimental results of the timing performance and space utilisation by compression of our algorithm by comparing with StarNT method.
机译:本文介绍了一种新的文本转换方法,与Starnt文本转换方法有一些相似之处。 Starnt是一种基于字典的无损文本转换算法。研究人员已经设计了许多不同的压缩方法,以找到合适的数据传输解决方案,其利用整个网络带宽最佳地,并且还实现了更高的压缩比。正在使用的大多数方法,如通过部分匹配(ppm)的预测,挖掘机轮车变换一直无法实现理论计算所提供的最佳输出,因此留下了研究人员,以找到更有效的文本压缩技术。此外,在本文中,我们还通过与Starnt方法进行比较来提供通过压缩我们的算法来提供定时性能和空间利用的实验结果。

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