5527568
doi
10.35940/ijeat.B2097.1210220
oai:zenodo.org:5527568
Blue Eyes Intelligence Engineering and Sciences Publication(BEIESP)
Publisher
D. Thiripurasundari
School of Electronics Engineering, VIT Chennai, India,
An Approach to Efficient Dictionary Utilization and Improved Data Compression Technique for LZW Algorithm
S. Revathi
School of Electronics Engineering, VIT Chennai, India,
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
data compression, LZW, dictionary encoding, lossless encoding.
<p>This paper proposes an improved data compression technique compared to existing Lempel-Ziv-Welch (LZW) algorithm. LZW is a dictionary-updation based compression technique which stores elements from the data in the form of codes and uses them when those strings recur again. When the dictionary gets full, every element in the dictionary are removed in order to update dictionary with new entry. Therefore, the conventional method doesn’t consider frequently used strings and removes all the entry. This method is not an effective compression when the data to be compressed are large and when there are more frequently occurring string. This paper presents two new methods which are an improvement for the existing LZW compression algorithm. In this method, when the dictionary gets full, the elements that haven’t been used earlier are removed rather than removing every element of the dictionary which happens in the existing LZW algorithm. This is achieved by adding a flag to every element of the dictionary. Whenever an element is used the flag is set high. Thus, when the dictionary gets full, the dictionary entries where the flag was set high are kept and others are discarded. In the first method, the entries are discarded abruptly, whereas in the second method the unused elements are removed once at a time. Therefore, the second method gives enough time for the nascent elements of the dictionary. These techniques all fetch similar results when data set is small. This happens due to the fact that difference in the way they handle the dictionary when it’s full. Thus these improvements fetch better results only when a relatively large data is used. When all the three techniques' models were used to compare a data set with yields best case scenario, the compression ratios of conventional LZW is small compared to improved LZW method-1 and which in turn is small compared to improved LZW method-2.</p>
Zenodo
2020-12-30
info:eu-repo/semantics/article
5527567
1632577704.160101
507073
md5:87e4a758df2f1ba5948ce824088cb82f
https://zenodo.org/records/5527568/files/B20971210220.pdf
public
International Journal of Engineering and Advanced Technology (IJEAT)
10
2
224-229
2020-12-30