Open AccessA Classification of Lossless and Lossy Data Compression Schemes
Author(s) -
Lee Chin Kho,
Sze Song Ngu,
Annie Joseph,
Dayang Azra Binti Awang Mat,
Kuryati Kipli
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.c8575.019320
Subject(s) - lossy compression , lossless compression , computer science , data compression , data compression ratio , compression (physics) , data mining , image compression , algorithm , artificial intelligence , materials science , composite material , image (mathematics) , image processing
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.