Due to the increase in the growth of data in this era of the digital world and limited resources, there is a need for more efficient data compression techniques for storing and transmitting data. Data compression can significantly reduce the amount of storage space and transmission time to store and transmit given data. More specifically, text compression has got more attention for effectively managing and processing data due to the increased use of the internet, digital devices, data transfer, etc. Over the years, various algorithms have been used for text compression such as Huffman coding, Lempel-Ziv-Welch (LZW) coding, arithmetic coding, etc. However, these methods have a limited compression ratio specifically for data storage applications where a considerable amount of data must be compressed to use storage resources efficiently. They consider individual characters to compress data. It can be more advantageous to consider words or sequences of words rather than individual characters to get a better compression ratio. Compressing individual characters results in a sizeable compressed representation due to their less repetition and structure in the data. In this paper, we proposed the ArthNgram model, in which the N-gram language model coupled with arithmetic coding is used to compress data more efficiently for data storage applications. The performance of the proposed model is evaluated based on compression ratio and compression speed. Results show that the proposed model performs better than traditional techniques.

由于这个数字世界时代数据增长的增加和资源有限，需要更有效的数据压缩技术来存储和传输数据。数据压缩可以显着减少存储和传输给定数据的存储空间量和传输时间。更具体地说，由于互联网、数字设备、数据传输等的使用增加，文本压缩在有效管理和处理数据方面受到了更多关注。多年来，各种算法已用于文本压缩，例如霍夫曼编码、Lempel -Ziv-Welch (LZW) 编码、算术编码等。然而，这些方法的压缩率有限，特别是对于必须压缩大量数据才能有效使用存储资源的数据存储应用。他们考虑单个字符来压缩数据。为了获得更好的压缩比，考虑单词或单词序列而不是单个字符可能更有利。压缩单个字符会产生相当大的压缩表示，因为它们在数据中的重复性和结构较少。在本文中，我们提出了ArthNgram模型，其中N-gram语言模型与算术编码相结合，用于更有效地压缩数据以用于数据存储应用。根据压缩比和压缩速度评估所提出模型的性能。结果表明，所提出的模型比传统技术表现更好。