Multiresolution Signal Decomposition: Transforms, Subbands, and Wavelets
This book provides an in-depth, integrated, and up-to-date exposition of the topic of signal decomposition techniques. Application areas of these techniques include speech and image processing, machine vision, information engineering, High-Definition Television, and telecommunications. The book will serve as the major reference for those entering the field, instructors teaching some or all of the topics in an advanced graduate course and researchers needing to consult an authoritative source.
n The first book to give a unified and coherent exposition of multiresolutional signal decomposition techniques
n Classroom tested textbook clearly describes the commonalities among three key methods-transform coding, and wavelet transforms
n Gives comparative performance evaluations of many proposed techniques
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Akansu algorithm aliasing energy all-pass analysis ﬁlters ASSP band basis functions Bernstein polynomial Binomial QMF bit allocation block transforms Chapter coefﬁcients components correlation model Daubechies decimation deﬁned Digital discrete discrete cosine transform domain down-sampler dyadic tree energy compaction Figure Filter ﬁlter bank ﬁnite ﬁrst ﬁxed Fourier transform frequency Haar Haar functions Hence high-pass ﬁlter Hp(z ideal ﬁlter IEEE IEEE Trans Image Coding impulse response inﬁnite interpolation Laplacian pyramid lattice linear phase lossless low-pass ﬁlter M-band modulation multirate obtain orthogonal transform orthonormal wavelet output parameters perfect reconstruction polynomial polyphase matrix power complementary Prob Proc Quadrature Mirror Filter representation samples satisﬁes scaling function sequence shown in Fig Signal Processing spectral coefﬁcients STFT subband ﬁlter banks subsampling synthesis ﬁlters time-domain time-frequency transform coding two-band up-sampler variance vector Vetterli wavelet ﬁlters wavelet function wavelet transform zero