TY - JOUR
T1 - Optimal bit allocation and best-basis selection for wavelet packets and TSVQ
AU - Goldschneider, Jill R.
AU - Riskin, Eve A.
PY - 1999/9
Y1 - 1999/9
N2 - To use wavelet packets for lossy data compression, the following issues must be addressed: quantization of the wavelet subbands, allocation of bits to each subband, and best-basis selection. We present an algorithm for wavelet packets that systematically identifies all bit allocations/best-basis selections on the lower convex hull of the rate-distortion curve. We demonstrate the algorithm on tree-structured vector quantizers used to code image subbands from the wavelet packet decomposition.
AB - To use wavelet packets for lossy data compression, the following issues must be addressed: quantization of the wavelet subbands, allocation of bits to each subband, and best-basis selection. We present an algorithm for wavelet packets that systematically identifies all bit allocations/best-basis selections on the lower convex hull of the rate-distortion curve. We demonstrate the algorithm on tree-structured vector quantizers used to code image subbands from the wavelet packet decomposition.
KW - Best basis selection
KW - Bit allocation
KW - Compression
KW - Tree-structured vector quantization
KW - Wavelet packets
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U2 - 10.1109/83.784444
DO - 10.1109/83.784444
M3 - Article
AN - SCOPUS:0012064546
SN - 1057-7149
VL - 8
SP - 1305
EP - 1309
JO - IEEE Transactions on Image Processing
JF - IEEE Transactions on Image Processing
IS - 9
ER -