Paper Info
Title
Efficient Run-Length Encoding of Binary Sources with Unknown Statistics
Abstract
Abstract We present a new binary entropy coder of the Golomb family, with an adaptation strategy that is nearly ,optimum ,in a ,maximum-likelihood sense. This new encoder can be implemented efficiently in practice, since uses only integer arithmetic and no divisions. That way, the proposed encoder has a complexity nearly identical to that of popular adaptive Rice coders. However, whereas Golomb-Rice coders have an excess rate with respect to the source entropy of up to 4.2% for binary sources with unknown statistics, the proposed encoder has an excess rate of less than 2%.
Year
DOI
Venue
2004
10.1109/DCC.2004.1281510
Data Compression Conference
Keywords
Field
DocType
binary sources,efficient run-length encoding,unknown statistics,source coding,information theory,maximum likelihood,statistics,switches,maximum likelihood estimation,data compression,encoding,codecs,binary codes,polynomials,entropy,run length encoding
Computer science,Binary entropy function,Theoretical computer science,Run-length encoding,Artificial intelligence,Truncated binary encoding,Codec,Binary number,Entropy encoding,Pattern recognition,Binary code,Statistics,Encoding (memory)
Conference
ISSN
ISBN
Citations 
1068-0314
0-7695-2082-0
3
PageRank 
References 
Authors
0.60
7
2
Name
Order
Citations
PageRank
Max H. M. Costa130.60
Henrique Malvar2690105.66