Abstract/Details

DATA COMPRESSION WITH SOURCE CODING FOR UNKNOWN SOURCE STATISTICS

LEE, DONG HEE. 
 University of Maryland, College Park ProQuest Dissertations Publishing,  1981. 8205240.

Abstract (summary)

Previous source coding methods, i.e., Shannon coding, Fano coding and Huffman coding, require precise knowledge of the statistical description of the process to be compressed. This demand is rarely met in practice. Source coding with unknown statistical description or with incomplete or inaccurate statistical description is called universal coding. Minimax redundancy is an performance measure for universal coding. Two universal noiseless source coding methods, source matching and efficient universal source coding, were developed as an approximation to minimax universal coding by Davisson, Leon-Garcia, Pursley, McEliece and Wallace. The redundancies of the source matching game and efficient universal coding are lower and upper bounds of minimax redundancy respectively for finite block length. Both of them are arbitrarily close to the redundancy of minimax coding as block length increases. In this dissertation, these two methods are considered and compared to each other for the class of first-order Markov sources with finite alphabet.

Indexing (details)


Subject
Electrical engineering
Classification
0544: Electrical engineering
Identifier / keyword
Applied sciences
Title
DATA COMPRESSION WITH SOURCE CODING FOR UNKNOWN SOURCE STATISTICS
Author
LEE, DONG HEE
Number of pages
86
Degree date
1981
School code
0117
Source
DAI-B 42/09, Dissertation Abstracts International
Place of publication
Ann Arbor
Country of publication
United States
ISBN
9798413189733
University/institution
University of Maryland, College Park
University location
United States -- Maryland
Degree
Ph.D.
Source type
Dissertation or Thesis
Language
English
Document type
Dissertation/Thesis
Dissertation/thesis number
8205240
ProQuest document ID
303164012
Copyright
Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.
Document URL
https://www.proquest.com/docview/303164012