Reversible arithmetic coding for quantum data compression

I.L. Chuang; D.S. Modha

doi:10.1109/18.841192

Reversible arithmetic coding for quantum data compression

IEEE Transactions on Information Theory ◽

10.1109/18.841192 ◽

2000 ◽

Vol 46 (3) ◽

pp. 1104-1116 ◽

Cited By ~ 14

Author(s):

I.L. Chuang ◽

D.S. Modha

Keyword(s):

Data Compression ◽

Arithmetic Coding

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Information technology. Data compression for information interchange. Binary arithmetic coding algorithm

10.3403/00328593 ◽

1994 ◽

Keyword(s):

Information Technology ◽

Data Compression ◽

Arithmetic Coding ◽

Binary Arithmetic ◽

Information Interchange

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Arithmetic coding for data compression

Proceedings of the IEEE ◽

10.1109/5.286189 ◽

1994 ◽

Vol 82 (6) ◽

pp. 857-865 ◽

Cited By ~ 142

Author(s):

P.G. Howard ◽

J.S. Vitter

Keyword(s):

Data Compression ◽

Arithmetic Coding

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Design of Effective Lossless Data Compression Technique for Multiple Genomic DNA Sequences

10.54216/fpa.060103 ◽

2021 ◽

pp. 17-25

Author(s):

Mahmud Alosta ◽

◽

Alireza Souri

Keyword(s):

Data Compression ◽

Dna Sequences ◽

Genomic Dna ◽

Arithmetic Coding ◽

Huffman Coding ◽

Compression Technique ◽

Compression Performance ◽

Proposed Model ◽

Evaluation Parameters ◽

Genome Dataset

In recent years, a massive amount of genomic DNA sequences are being created which leads to the development of new storing and archiving methods. There is a major challenge to process, store or transmit the huge volume of DNA sequences data. To lessen the number of bits needed to store and transmit data, data compression (DC) techniques are proposed. Recently, DC becomes more popular, and large number of techniques is proposed with applications in several domains. In this paper, a lossless compression technique named Arithmetic coding is employed to compress DNA sequences. In order to validate the performance of the proposed model, the artificial genome dataset is used and the results are investigated interms of different evaluation parameters. Experiments were performed on artificial datasets and the compression performance of Arithmetic coding is compared to Huffman coding, LZW coding, and LZMA techniques. From simulation results, it is clear that the Arithmetic coding achieves significantly better compression with a compression ratio of 0.261 at the bit rate of 2.16 bpc.

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