TY - JOUR
T1 - A Progressive Universal Noiseless Coder
AU - Effros, Michelle
AU - Gray, Robert M.
AU - Riskin, Eve A.
PY - 1994/1
Y1 - 1994/1
N2 - We combine pruned tree-structured vector quantization (pruned TSVQ) with Itoh’s universal noiseless coder. By combining pruned TSVQ with universal noiseless coding, we 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.
AB - We combine pruned tree-structured vector quantization (pruned TSVQ) with Itoh’s universal noiseless coder. By combining pruned TSVQ with universal noiseless coding, we 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.
KW - Progressive transmission
KW - medical image coding
KW - universal noiseless coding
UR - http://www.scopus.com/inward/record.url?scp=0028201657&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0028201657&partnerID=8YFLogxK
U2 - 10.1109/18.272460
DO - 10.1109/18.272460
M3 - Article
AN - SCOPUS:0028201657
SN - 0018-9448
VL - 40
SP - 108
EP - 117
JO - IEEE Transactions on Information Theory
JF - IEEE Transactions on Information Theory
IS - 1
ER -