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Distributed Cluster Processing to Evaluate Interlaced Run-Length Compression Schemes

by Ankit Arora, Sachin Bagga, Rajbir Singh Cheema
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 46 - Number 6
Year of Publication: 2012
Authors: Ankit Arora, Sachin Bagga, Rajbir Singh Cheema
10.5120/6913-9311

Ankit Arora, Sachin Bagga, Rajbir Singh Cheema . Distributed Cluster Processing to Evaluate Interlaced Run-Length Compression Schemes. International Journal of Computer Applications. 46, 6 ( May 2012), 26-33. DOI=10.5120/6913-9311

@article{ 10.5120/6913-9311,
author = { Ankit Arora, Sachin Bagga, Rajbir Singh Cheema },
title = { Distributed Cluster Processing to Evaluate Interlaced Run-Length Compression Schemes },
journal = { International Journal of Computer Applications },
issue_date = { May 2012 },
volume = { 46 },
number = { 6 },
month = { May },
year = { 2012 },
issn = { 0975-8887 },
pages = { 26-33 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume46/number6/6913-9311/ },
doi = { 10.5120/6913-9311 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:39:04.461220+05:30
%A Ankit Arora
%A Sachin Bagga
%A Rajbir Singh Cheema
%T Distributed Cluster Processing to Evaluate Interlaced Run-Length Compression Schemes
%J International Journal of Computer Applications
%@ 0975-8887
%V 46
%N 6
%P 26-33
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Parallel computation, a greater advancement in computational hardware as well as new achievement in current scientific computing such as image processing involves huge exhaustive computation and data processing leading towards parallel architectures. Parallel hardware organization basically a suitable interconnection among computational hardware, where current trends now involves clustered organization of distributed hardware to achieve parallel effects. Cluster environment consisting multi-computer network nodes provides flexible architecture towards high complex data parallelism as well as control parallelism operations. Further detail consists interlaced graphics mechanism with run-length encoding to achieve high compression benefits. Run-length compression speedup benefits have already described in the research IJCA-2011 cluster based performance evaluation of run-length image compression, which is now updated to cover interlaced lossy compression schemes. In general interlacing provides a lossy compression formulation but acceptable in real-life scenarios. Finally, the interlaced methodology and cluster based analysis results will be discussed.

References
  1. Ankit Arora, Amit chhabra Nov 2011, Cluster Based Performance evaluation of Run length Image Compression, Vol. 33, International Journal of Computer Application, Foundation of Computer Science, New York.
  2. Yan Fang Oct 2011, parallel implementation of fractal image compression in web service environment (IEEE-2011).
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  5. TD Nguyen, 1996 Parallel Application Characterization for Multiprocessor Scheduling, Department of Computer Science and Engineering, Box 352350 University of Washington, Seattle, WA 98195-2350 USA.
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  8. John Amanatides, Antialiasing of Interlaced Video Animation 1990, ACM-0-89791-344-2/90/008/0077.
  9. Carl Franklin, Visual Basic 6. 0 Internet Programming 1999, ISBN-10: 0471314986, Wiley Publishing Ltd.
Index Terms

Computer Science
Information Sciences

Keywords

Parallelism Distributed Clustering Multi-computers Run-length Image Compression Interlacing Twips

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