Arithmetic coding in lossless waveform compression

A method for applying arithmetic coding to lossless waveform compression is discussed. Arithmetic coding has been used widely in lossless text compression and is known to produce compression ratios that are nearly optimal when the symbol table consists of an ordinary alphabet. In lossless compressio...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:IEEE transactions on signal processing Ročník 43; číslo 8; s. 1874 - 1879
Hlavní autor: Stearns, S.D.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York, NY IEEE 01.08.1995
Institute of Electrical and Electronics Engineers
Témata:
Applied sciences
Arithmetic
Binary sequences
Coding, codes
Data compression
Decorrelation
Encoding
Exact sciences and technology
Gaussian distribution
Information, signal and communications theory
Signal and communications theory
Speech
Telecommunications and information theory
Telemetry
Signal compression
Coding
Prediction
Seismic signal
Arithmetic code
Signal processing
Optimization
ISSN:1053-587X
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:A method for applying arithmetic coding to lossless waveform compression is discussed. Arithmetic coding has been used widely in lossless text compression and is known to produce compression ratios that are nearly optimal when the symbol table consists of an ordinary alphabet. In lossless compression of digitized waveform data, however, if each possible sample value is viewed as a "symbol" the symbol table would be typically very large and impractical. the authors therefore define a symbol to be a certain range of possible waveform values, rather than a single value, and develop a coding scheme on this basis. The coding scheme consists of two compression stages. The first stage is lossless linear prediction, which removes coherent components from a digitized waveform and produces a residue sequence that is assumed to have a white spectrum and a Gaussian amplitude distribution. The prediction is lossless in the sense that the original digitized waveform can be recovered by processing the residue sequence. The second stage, which is the subject of the present paper, is arithmetic coding used as just described. A formula for selecting ranges of waveform values is provided. Experiments with seismic and speech waveforms that produce near-optimal results are included.< >
Bibliografie:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:1053-587X
DOI:10.1109/78.403346