000033844 001__ 33844
000033844 005__ 20190316233418.0
000033844 0247_ $$2doi$$a10.1109/79.952805
000033844 02470 $$2DAR$$a1957
000033844 02470 $$2ISI$$a000170742300007
000033844 037__ $$aARTICLE
000033844 245__ $$aWavelets, approximation, and compression
000033844 269__ $$a2001
000033844 260__ $$c2001
000033844 336__ $$aJournal Articles
000033844 520__ $$aOver the last decade or so, wavelets have had a growing impact on signal processing theory and practice, both because of the unifying role and their successes in applications. Filter banks, which lie at the heart of wavelet-based algorithms, have become standard signal processing operators, used routinely in applications ranging from compression to modems. The contributions of wavelets have often been in the subtle interplay between discrete-time and continuous-time signal processing. The purpose of this article is to look at wavelet advances from a signal processing perspective. In particular, approximation results are reviewed, and the implication on compression algorithms is discussed. New constructions and open problems are also addressed
000033844 700__ $$g107537$$aVetterli, Martin$$0240184
000033844 773__ $$j18$$tIEEE Signal Processing Magazine$$k5$$q59-73
000033844 8564_ $$uhttps://infoscience.epfl.ch/record/33844/files/Vetterli01a.pdf$$zn/a$$s2275715
000033844 909C0 $$xU10434$$0252056$$pLCAV
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000033844 917Z8 $$x218003
000033844 937__ $$aLCAV-ARTICLE-2001-013
000033844 970__ $$aVetterli01a/LCAV
000033844 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000033844 980__ $$aARTICLE