Special Issue "Data Compression"
A special issue of Algorithms (ISSN 19994893).
Deadline for manuscript submissions: closed (30 September 2009)
Special Issue Editor
Guest Editor
Dr. David Salomon
Computer Science Department (Retired), California State University, Northridge, CA 913308281, USA
Website: http://www.davidsalomon.name/
EMail: dsalomon@csun.edu
Phone: +1 619 443 6528
Fax: +1 619 749 5229
Interests: computer graphics; data compression; cryptography
Special Issue Information
Dear Colleagues,
Data compression is the operation of converting an input data file to a smaller file. This operation is important for the following reasons: 1. People like to accumulate data. Thus, no matter how big a storage device one has, sooner or later it is going to fill up. 2. People hate to wait for data transfers. We often upload and download files from our computers and we hate to wait for long, slow data transfers. How can data be compressed? We can represent the same amount of information in fewer bits because the original data representation is not the shortest possible. It is intentionally long in order to simplify processing the data. We say that our data representations have redundancies. Compressing data is done by locating its redundancies and reducing or eliminating them. Thus, the field of data compression tries to understand the sources of redundancies in different types of data and find clever methods to eliminate them. Today, after decades of research, there are hundreds of algorithms and dozens of implementations that can reduce the size of all types of digital data. It is my hope that this issue of Algorithms will make a significant contribution toward this goal.
Dr. David Salomon
Guest Editor
Keywords
 data compression
 data coding
 source coding
 information theory
 entropy
 data redundancy
 variablelength codes
Published Papers (6 papers)
Algorithms 2010, 3(2), 145167; doi:10.3390/a3020145
Received: 23 December 2009; in revised form: 18 March 2010 / Accepted: 18 March 2010 / Published: 1 April 2010
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Algorithms 2010, 3(1), 6375; doi:10.3390/a3010063
Received: 4 November 2009; in revised form: 8 January 2010 / Accepted: 25 January 2010 / Published: 29 January 2010
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Algorithms 2009, 2(4), 14291448; doi:10.3390/a2041429
Received: 30 September 2009; Accepted: 20 November 2009 / Published: 25 November 2009
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Algorithms 2009, 2(3), 12211231; doi:10.3390/a2031221
Received: 25 June 2009; in revised form: 31 August 2009 / Accepted: 15 September 2009 / Published: 22 September 2009
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Algorithms 2009, 2(3), 11051136; doi:10.3390/a2031105
Received: 9 July 2009; in revised form: 8 September 2009 / Accepted: 9 September 2009 / Published: 10 September 2009
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Article:
Graph Compression by BFS
Algorithms 2009, 2(3), 10311044; doi:10.3390/a2031031
Received: 30 June 2009; in revised form: 20 August 2009 / Accepted: 21 August 2009 / Published: 25 August 2009
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Last update: 20 February 2014