A Classification of Lossless and Lossy Data Compression Schemes
Lee Chin Kho1, S. S. Ngu2, A. Joseph3, D. A. A. Mat4, K. Kuryati5

1Lee Chin Kho*, Department of Electrical Engineering, University Of Adelaide, Adelaide, Australia
2Sze Song Ngu, Department of Electrical Engineering, University Of Adelaide, Adelaide, Australia
3Annie Joseph, Senior Lecturer, Department of Electronic, University Malaysia
4Dayang Azra Binti Awang Mat, Senior Lecturer in Faculty of Engineering, University Malaysia
5Kuryati Kipli, Department of Electrical and Electronics, Faculty of Engineering, UNIMAS
Manuscript received on December 15, 2019. | Revised Manuscript received on December 20, 2019. | Manuscript published on January 10, 2020. | PP: 3393-3398 | Volume-9 Issue-3, January 2020. | Retrieval Number: C8575019320/2020©BEIESP | DOI: 10.35940/ijitee.C8575.019320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Data compression is a promising scheme to increase memory system capacity, performance and energy advantages. The compression performance could affect the overall network performance when compression scheme is implemented in a communication field. Many data compression schemes have been introduced. Most of other researchers choose very limited parameters to analyze the performance of the selected data compression scheme. This paper classifies the major data compression schemes according to nine different perspectives, such as homogeneity, purpose, accuracy, structuring of the data, repetition distance, structure sharing, number of passes, sampling frequency, and sample size ratio. Various data compression schemes are examined and classified according to the parameters mentioned above. The classification will provide researchers with the in-depth insight on the potential role of compression schemes in memory components and network performance of future extreme-scale systems. 
Keywords: Data Compression, Lossless, Homogeneity, Accuracy.
Scope of the Article: Classification