The 13th International Conference on Digital Signal Processing, Santorini, Greece, 2-4 July 1997In this paper, we propose a new M-channel wavelet bases called the cosine-modulated wavelets. We first generalize the theory of two-channel biorthogonal compactly supported wavelet bases to the M-channel case. A sufficient condition for the M-channel perfect reconstruction filter banks to construct M-channel compactly supported wavelet bases is given. By using this condition, a family of orthogonal and biorthogonal M-channel cosine-modulated wavelet bases is constructed by iterations of cosine-modulated filter banks (CMFB). The advantages of the approach are their simple design procedure, efficient implementation and good filter quality. A method for imposing the regularity on the cosine-modulated filter banks is also introduced and design example is given.published_or_final_versio

Chan, SC

Ho, KL

Luo, Y

HKU Scholars Hub

Title M-channel cosine-modulated wavelet basesAuthor(s) Chan, SC; Luo, Y; Ho, KLCitation International Conference On Digital Signal Processing, Dsp,1997, v. 1, p. 325-328Issued Date 1997URL http://hdl.handle.net/10722/45992Rights Creative Commons: Attribution 3.0 Hong Kong LicenseM-channel Cosine-Modulated Wavelet Bases S. C. Chan*, Y. Luo**, and K. L. Ho Department of Electrical and Electronic Engineering University of Hong Kong Pokfulam Road, Hong Kong “scchan @eee. hku. hk, **yluo@ eee.hku.hk Abstract In this paper, we propose a new M-channel wavelet bases called the cosine-modulated wavelets. We first generalize the theory of two-channel biorthogonal compactly supported wavelet bases to the M-channel case. A sufficient condition for the M-channel perfect reconstruction filter banks to construct M-channel compactly supported wavelet bases is given. By using this condition, a family of orthogonal and biorthogonal M-channel cosine-modulated wavelet bases is constructed by iterations of cosine-modulated filter banks (CMFB). The advantages of the approach are their simple design procedure, efficient implementation and good filter quality. A method for imposing the regularity on the cosine-modulated filter banks is also introduced and design example is given. 1. Introduction Wavelets are functions generated from the dilations and translations of one basic function called the wavelet function [l] .  More recently, wavelet functions have been constructed and studied extensively both in the mathematical and signal processing communities [I]-[4]. In signal analysis, wavelet transform (WT), which is a representation of a signal in terms of a set of wavelet basis functions, allows the signal to be analyzed in different resolutions or scales. WT makes a different resolution trade-off in the time-frequency plane as compared with the short-time Fourier transform. It has better time resolution in high frequency and better frequency resolution in low frequency. This property is useful to detect discontinuity in non-stationary signals which usually have slowly varying components accompanied with transient high frequency spikes. The theory of wavelets is closely related to perfect reconstruction (PR) multirate filter banks. Daubechies [ 11 constructed compactly supported orthonormal wavelets from iterations of two-channel discrete filter banks with certain regularity condition. Since two- channel paraunitary PR filter banks cannot have non- trivial linear phase solution, more general biorthogonal filter banks were studied. In [2], more general biorthogonal compactly supported wavelet bases were introduced with similar regularity condition. The idea of constructing orthonormal wavelets by multirate filter banks has also been extended to the more general case of M-channel orthonormal wavelets [3], [4]. Like the dyadic case, it is also possible to obtain M-channel wavelets bases from M-channel PR orthogonal filter banks with added regularity condition. In this paper, we first derive a sufficient condition for a biorthogonal M-channel PR filter banks to construct a biorthogonal M-channel compactly supported wavelet 0-7803-4137-6/97/$10.0001977 IEEE bases. It is shown that the lowpass filter in the PR filter bank has to satisfy similar regularity condition and the bandpass and highpass filters have to satisfy the admissible condition. Then, we propose to use the cosine-modulated filter banks (CMFB) [6]-191, [ 1 I] to construct such wavelet bases. The design of M-channel wavelet bases is more difficult than the two-channel case due to the large number of design parameters and difficulties in meeting the regularity condition. The advantages of the CMFB are their low design and implementation complexities, good filter quality, and ease in imposing the regularity conditions. Both orthogonal and biorthogonal M-channel wavelets can be constructed by CMFB. We shall use the following notations. h, [n] and g, [n], i = O,l, ..., M - 1, respectively, represent analysis and synthesis filters of M-channel filter banks [5]. g”,[n] represents the mirror image of g , [ n ] ,  namely, g”,[nl=g,[-n]. $(XI  and v,(x), i=1,2 ,..., M - I ,  represent, respectively, the scaling and wavelet functions. While $(x) and W , ( X )  are the corresponding dual functions. Caption letter represents discrete-time Fourier transform or Fourier transform. - In Section 2, we shall briefly review the theory of the M-channel wavelets. Section 3 is devoted to an overview of M-channel CMFB. Design procedure and design example of the M-channel cosine-modulated wavelet bases are given in Section 4. Finally, we summarize our results in the conclusion. 2. Theory of M-channel Wavelets In [3] and [4], the M-channel orthonormal wavelets are constructed by iteration of M-channel orthogonal filter banks. The analysis and synthesis filters are time- inverse of each other. In the biorthogonal cases, there is not such restriction. DSP 97 - 325 Here, we have two dual bases, each generated from a set of wavelet functions. First of all, we start with the discrete-time Fourier transfoi-ms (scaled by M-”’ ) of h,, [.I and io [nl ,  H,(o) = M-”?Ch,,[rz]e-’“~, (2-1) G,,(o) = M - ” 2 x g , j [ n ] e - J n w .  (2-2) I By iterating these discrete-time filters, it is possible to define the Fourier transform a([) and 6(<) of the scaling function @(x) and its dual $(x) using the following infinite products: Q(5) = ( 2 7 ~ - ” ~ f i H ~ ) ( M - ~ k ) ,  (2-3) 6(g) = ( ~ T I ) - ~ ” ~ G ~ ( M - ~ ~ ) .  (2-4) ’=I J=I These infinite products can onlyconverge if, (2-3) and (2-4) will then converge uniformly and absolutely on compact sets to @([) and &(5) which are well-defined C” functions. From (2-3) and (2-4), we also have: H(,(O) = G”(0) = 1 (2-5) Taking the inverse Fourier Transform leads to the well known two-scale difference equations of $(x) and its dual 6(x) as follows: $ ( ~ ) = f i x h , , L n N ( M x - n ) ,  (2-8) $(x) = f i x  g”,[rz]$( Mx - 1 2 ) .  (2-9) 1 (2-8) and (2-9) tell us that $(x) and 6(x) can be written as a linear combination of their contracted (by M) and shifted versions. Therefore the space spanned by: $0 P J ( x )  = ~ - 1 ’ 2  $(M-’x  - k ) ,  @ O . k J ( x )  = ~ - 1 ’ 2  $ ( M - ’ x - k ) ,  k E Z  - - (2-10) at a given resolution j can be viewed as a multiscale approximation of a signal J ( x )  . To show that @(x) and $(x) can be used to generate a basis, we need M-1 wavelet functions and their duals to describe the remaining “details” in the approximation. w, (x) = f i x h ; [ l Z l $ (  Mx - / I )  , @ , ( x ) = ~ C g ” , [ n ] ~ ( M x - r 2 ) ,  (2-12) (2-1 1) where i = 1,2 ,..., M - 1 . We can also define iy”,”(x) and @“”(x) as: i y i ’ , k ’ ( x )  = M - j ’ * ~ ~ ( M - ’ x - k ) ,  (2-13) @;J . ” ( ( . )  = M-1’2- ~ , ( M - ’ x - k ) ,  (2-14) k E Z , i = 1 , 2  ,..., M-1.  We give an important theorem without proof. For more details on the proof, interested readers can refer to [lo]. Theorem 2.1 The functions yf:J.k ’ (x)  and @ i j . k ) ( x ) ,  constructed as (2-13) and (2-14), generate a biorthogonal M-channel wavelet bases in  L2(%) if they satisfy the following three conditions: (Cl) 4 [ n ]  and g,[n],  i = O,1, ..., M-I. constitute a PR M-channel filter bank; I 1 (C2) ---Chi[n]=Oand--g”,[nJ=O, hi7 (1 hi7 J7 i = 1,2, ..., M-1 ; (C3) Both H,(a) and Go (a) have K order zeros at 2z.e M 0, = -, t = 1,2,. ., M - 1, K 2 1 . (C2) ensures that the wavelets will satisfy the admissible conditions[ 11: (C3) is the familiar regularity condition. (C3) together with (Cl) will ensure that $ ’ ~ k ’ ( ~ )  and @i’,”(x) constitute a Riesz bases in L’(%). Therefore, we have, for any f ( x )  E L?(%) , f ( x )  = ~ ~ \ ~ ~ ‘ ) i y ~ j . ’ )  , j ,  k E Z with c,!~,,) = (f(x),$’.”) . These two equations are called the wavelet series and wavelet coefficients, respectively. 1,k.i 3. Theory of CMFB In this section, we shall introduce the theory of CMFB and its design procedure. More details can be found in [6]-191, [11]. In CMFB, the analysis filter bank f , ( n )  and synthesis filter bank g , ( n )  are obtained respectively by modulating the prototype filters h(n) and s(n), s, ( n )  = h(n)c,,, > 8,  (n )  = S(~~)C-k,, > k=0,1, ... M-1,  12=0,1, ..., N - I ,  (3-1) where M is the number of channels and N is the length of the filters. Two  oss sib le modulations can be used: , (3-2a) DSP 97 - 326 number of zeros at o = 0 and satisfy (C2) automatically. This design procedure for the orthogonal and biorthogonal cases is similar except for the different filter banks (Orthogonal or hiorthogonal). Here we only give an example of biorthogonal cosine-modulated wavelets. In this example, the parameters of the CMFB are M=4, K=l and N=40. Fig.1 are the scaling function, the three wavelet functions and their duals. Fig.2 are the impulse and frequency response of prototype filter. In our example, these basis functions are obtained from iterating the corresponding two-level tree-structured CMFB. 5 .  Conclusion In this paper we have presented a sufficient condition for constructing M-channel wavelets and proposed a family of M-channel cosine-modulated wavelet bases. The advantages of the approach are their simple design procedure, efficient implementation and good filter quality. Design example is given to demonstrate the usefulness of the method. Reference 1. Daubechies, "Orthonormal bases of compactly supported wavelets," Comm. Pure Applied Math., A. Cohen, I .  Daubechies and J.  Feauveau, "Biorthogonal bases of compactly supported wavelets," Conzm. P ~ i r e  Applied. Math. Vol. XLV, pp. 485-560, 1992 H. Zou and A. H. Tewfik, "Discrete orthogonal M-band wavelet decompositions," in Proc. IEEE P. Steffen, P. N. Heller, R. A. Gopinath and C. S .  Burrus, "Theory of regular M-band wavelet bases," IEEE Trans. on SP., VoI. 41, No. 12, pp.3497-3511, Dec. 1993. P. P. Vaidyanathan, MLilrirate systems and filter banks. Englewood Cliffs, NJ: Prentice Hall, 1992. R. D. Koilpillai and P. P. Vaidyanathan, "Cosine- modulated FIR filter banks satisfying perfect reconstruction," IEEE Trans. on SP., pp. 770-783, Apr. 1992. H. S .  Malvar, "Extended lapped transforms: properties, applications, and fast algorithms." IEEE Trans. SP., Vol. 40, pp. 2703-2714, Nov. 1992. T. Q. Nguyen and R. D. Koilpillai, "The theory and design of arbitrary-length cosine-modulated filter banks and wavelets, satisfying perfect reconstruction," IEEE Trans. on SP., Vol. 44, No. 3,  pp.473-483, Mar. 1996 Y. Luo, S .  C. Chan and K. L. Ho, "Theory and design of orthogonal and biorthogonal cosine modulation filter banks and wavelet bases," Submitted to IEEE Trans. on CAS I/, Special Vol. XLI, pp.909-996, 1988. ICASSP-92, pp.605-608, Vol. IV. Issue on Multirate Systems, Filter Banks, Wavelets, and Applications. S .  C. Chan, "Theory of M-channel hiorthogonal compactly supported wavelet bases," Internal Report, The University of Hong Kong, Dec. 1996. Y. Luo, S .  C. Chan and K. L. Ho, "Theory and design of arbitrary-length biorthogonal cosine- modulated filter banks," to appear in Proc. IEEE ISCAS'97, Hong Kong. I Scaling and wavelet functions and their dual functions Fig.2 Prototype filter of 4-channel CMFB (a) Frequency response (b) Impulse response DSP 97 - 328 

M-channel cosine-modulated wavelet bases

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M-channel cosine-modulated wavelet bases

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