DocumentCode
1037294
Title
A progressive universal noiseless coder
Author
Effros, Michelle ; Chou, Philip A. ; Riskin, Eve A. ; Gray, Robert M.
Author_Institution
Inf. Syst. Lab., Stanford Univ., CA, USA
Volume
40
Issue
1
fYear
1994
fDate
1/1/1994 12:00:00 AM
Firstpage
108
Lastpage
117
Abstract
The authors combine pruned tree-structured vector quantization (pruned TSVQ) with Itoh´s (1987) universal noiseless coder. By combining pruned TSVQ with universal noiseless coding, they benefit from the “successive approximation” capabilities of TSVQ, thereby allowing progressive transmission of images, while retaining the ability to noiselessly encode images of unknown statistics in a provably asymptotically optimal fashion. Noiseless compression results are comparable to Ziv-Lempel and arithmetic coding for both images and finely quantized Gaussian sources
Keywords
encoding; image coding; tree data structures; vector quantisation; TSVQ; image coding; noiseless compression results; progressive image transmission; pruned tree-structured vector quantization; successive approximation capabilities; universal noiseless coding; unknown statistics; Biomedical imaging; Bit rate; Decoding; Gaussian noise; Image coding; Image databases; Image reconstruction; Image storage; Noise robustness; Statistics;
fLanguage
English
Journal_Title
Information Theory, IEEE Transactions on
Publisher
ieee
ISSN
0018-9448
Type
jour
DOI
10.1109/18.272460
Filename
272460
Link To Document