Generalized variable dimensional set partitioning for embedded wavelet image compression

A vector enhancement of Said and Pearlman's (1996) set partitioning in hierarchical trees (SPIHT) methodology, named VSPIHT, has recently been proposed for embedded wavelet image compression. While the VSPIHT algorithm works better than scalar SPIHT for most images, a common vector dimension to...

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Bibliographic Details
Published in1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258) Vol. 6; pp. 3145 - 3148 vol.6
Main Authors Mukherjee, D., Mitra, S.K.
Format Conference Proceeding
LanguageEnglish
Published IEEE 1999
Subjects
Image coding
Lagrangian functions
Lattices
Partitioning algorithms
Quantization
Rate-distortion
Statistics
Transform coding
Wavelet coefficients
Wavelet transforms
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Summary:A vector enhancement of Said and Pearlman's (1996) set partitioning in hierarchical trees (SPIHT) methodology, named VSPIHT, has recently been proposed for embedded wavelet image compression. While the VSPIHT algorithm works better than scalar SPIHT for most images, a common vector dimension to use for coding an entire image may not be optimal. Since statistics vary widely within an image, a greater efficiency can be achieved if different vector dimensions are used for coding the wavelet coefficients from different portions of the image. We present a generalized methodology for developing a variable dimensional set partitioning coder, where different parts of an image may be coded in different vectoring modes, with different scale factors, and up to different number of passes. A Lagrangian rate-distortion criterion is used to make the optimum coding choices. Coding passes are made jointly for the vectoring modes to produce an embedded bitstream.
ISBN:0780350413
9780780350410
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.1999.757508