Fast cosine transform for FCC lattices

Bastian Seifert Knut Huper¨ Christian Uhl Faculty of Engineering Sciences Institute of Faculty of Engineering Sciences Ansbach University of Applied Sciences University of Wurzburg¨ Ansbach University of Applied Sciences Ansbach, Germany Wurzburg,¨ Germany Ansbach, Germany [email protected] [email protected] [email protected]

Abstract—Voxel representation and processing is an important sampling frequency [2], [3]. For example, one could achieve issue in a broad spectrum of applications. E.g., 3D imaging the same sampling density on a FCC lattice compared to a in biomedical engineering applications, video game development CC lattice with 29 % less sampling points. Recently, tools and volumetric displays are often based on data representation by voxels. By replacing the standard sampling lattice with a for interpolation on these lattices were published, e.g. the face-centered lattice one can obtain the same sampling density Voronoi splines [4]. Furthermore, it was shown that human with less sampling points and reduce aliasing error, as well. We beings recognize the sampled object at least as good as if introduce an analog of the discrete cosine transform for the face- the data was sampled on a CC lattice [5]. The superiority of centered lattice relying on multivariate Chebyshev . A sampling on non-standard lattices has been shown in medical fast for this transform is deduced based on algebraic theory and the rich geometry of the special applications [6], as well. unitary Lie of degree four. Even though there are classical abstract sampling and recon- Index Terms—discrete cosine transform (DCT), fast Fourier struction theorems on arbitrary lattices [7] and methodologies transform (FFT), FCC lattices, Chebyshev polynomials, volumet- for decomposing general lattices into cartesian sublattices ric image representation for computing Fourier transforms [8], FFTs on FCC and BCC lattices have been elaborated only recently [9]. One I.INTRODUCTION big disadvantage of these transforms is that they implicitly The approximation of real world objects by voxel data assume directed lattices, while 3D images are space-dependent and fast processing of these 3D-samples is of great interest objects, and hence it is more natural to model them on in a broad field of applications. For example, in biomedical undirected lattices. Using the theory of algebraic signal pro- engineering one is interested in fast processing of volumetric cessing [10] it is easy to realize that 1D undirected lattices are data in a variety of 3D-imaging procedures, like computed connected to discrete cosine transforms, which are based on tomography, magnetic resonance imaging and positron emis- Chebyshev polynomials [11], and deduce the corresponding sion tomography to name just a few. In computer games FFT-like [12]. Relying on this concept, an analog engineering there is an ongoing trend to rely graphics on of the discrete cosine transform on the hexagonal lattice, based voxels instead of polygons. Another field of voxel processing on two variable Chebyshev polynomials, was derived together is volumetric displays requiring a large amount of bandwith with its fast algorithm [13]. Recently, discrete cosine trans- for refreshing. Hence, achieving the same sampling density by forms on hexagonal lattices have gained attention as feature less data would be highly desirable. Besides faster processing, generators for artificial neural networks in face detection [14]. reducing aliasing errors and jitter noise by alternative data The multivariate Chebyshev polynomials [15] are now clas- sampling techniques is a motivation for the presented study. A sical, but have found applications only in the last years. Apart method to achieve this, is to change the sampling lattice from from the applications in algebraic signal processing they are arXiv:1807.10058v1 [eess.SP] 26 Jul 2018 the standard rectangular lattice, also known as cartesian cube applied in the discretization of partial differential equations (CC) lattice, to either the face-centered cubic (FCC) lattice in [16], [17], for the derivation of cubature formulas in [18], or the body-centered cubic (BCC) lattice. The FCC lattice [19], [20] and developing discrete transforms in [21]. is associated to a densest sphere packing, while the BCC In this paper we are studying the derivation of an analog lattice is interrelated to a sphere covering [1]. These lattices of the discrete cosine transform on the FCC lattice and the are in duality to each other, i.e. the frequency data of data corresponding fast algorithm. Our derivation is based on alge- sampled on one lattice will sit on the other lattice and vice braic signal processing theory, which is recalled in section II, versa. Both lattices have advantageous sampling properties and Chebyshev polynomials of the first kind in three variables, when compared to the CC lattice, each in its own region of whose construction is shown in section III. The nice properties of the Chebyshev polynomials in three variables are due to the This work is partially supported by the European Regional Development connection to the rich geometry of the Lie group SU(4), which Fund (ERDF) is briefly sketched. We derive the fast algorithm in section IV 978-1-5386-5346-3/18/$31.00 © 2018 IEEE and apply the transform to an artifical data set in section V. II.ALGEBRAICSIGNALPROCESSING in one variable to polynomials in multiple variables, we re- Algebraic signal processing theory [10] gives a unified place the permutations of two elements by another permutation notion for the central concepts of linear signal processing. group and represent it as matrices. Then we multiply these The basic objects in this theory are signal models, which matrices with vectors, exponentiate and average over them. More formally, let (k, θ) := exp(2πik>θ) for k ∈ Zd, θ ∈ Rd, consist of triples (A ,M, Φ), where A is the filter space, 1 N (k, θ) := P (k, wθ) W M the signal space, and Φ: C → M is a bijective map, and denote by s |W | w∈W , termed - called z-transform. Typically, the filter and the signal space symmetrization or generalized cosine [15], for a permutation are choosen to be equal as polynomials in multiple variables. group W . The definition of multivariate Chebyshev polyno- The multiplication of the polynomials is defined modulo some mials is then straightforward: subset, termed ideal, I of polynomials, i.e. A = M = Definition 1 Let W be a permutation group. For each k ∈ Zd C  [x1, . . . , xn] I. If we choose a Grobner¨ basis [22] for the define the corresponding multivariate Chebyshev polynomials ideal this results in multiplication modulo the polynomials in of the first kind as that Grobner¨ basis. Choosing a basis in M gives rise to the z-transform Φ by mapping each vector entry to a coefficient Tk(x1, . . . , xd) := (k, θ)s (4) of the basis elements of M. For example choosing A = M = with x (θ) := (e , θ) being a change of variables for j = C[z−1]hz−n −1i with basis {1, z−1, z−2, . . . , z−n+1} yields j j s 1, . . . , d, here e ∈ Zd denote the standard basis vectors. the well-known z-transform from discrete, finite-time signal j processing We list some of the properties multivariate Chebyshev poly- −1 −n+1 nomials obey. Φ:(s0, s1, . . . , sn−1) 7→ s0 + s1z + ··· + sn−1z . (1) Proposition 2 −n k For the multivariate Chebyshev polynomials The zeros of z −1 are precisely the discrete frequencies ωn, associated to the permutation group W one has with ω being an nth . If the zeros α of the ideal n i i.) T (x , . . . , x ) = 1 I are distinct, we have, by the Chinese remainder theorem, 0 1 d , ii.) T (x , . . . , x ) = x , a decomposition of the signal space into one-dimensional ej 1 d j subspaces iii.) Tk = TwT k for all w ∈ W , iv.) the  C[x1, . . . , xn] I 1 X 1 X ∼ M  (2) TkT` = |W | Tk+w>` = |W | T`+w>k, (5) = C[x1, . . . , xn] hx1 − αi,1, . . . , xn − αi,ni. w∈W w∈W i v.) the multivariate Chebyshev polynomials span the space A realizing this decomposition is called Fourier trans- of multivariate polynomials, form of the signal model. In the case of the discrete, finite time vi.) signal model this is precisely the discrete . the decomposition property There are distinguished elements of the filter space which Tk`ej = Tkej ◦ (T`e1 ,...,T`e ), (6) generate the whole space. In the context of the algebraic d signal processing theory, they are called shifts. In the example for k, ` ∈ Z, i.e. the generalized Chebyshev polynomials C[z−1]hz−n −1i the generator would be z−1. The generators form a semigroup, allow for visualization of the signal model by multiplying each vii.) the Chebyshev polynomials Tnej , for j = 1, . . . , d, form basis element with the generators and drawing arrows to the a Grobner¨ basis for the ideal they generate. basis elements appearing in the result. Remark 3 i.) There is an intimate connection to differential III.PERMUTATIONS AND GENERALIZED CHEBYSHEV geometry, as the permutation groups are a special case POLYNOMIALS of so called Weyl groups associated to simple, simply- connected, compact Lie groups. The construction can We briefly discuss the construction of multivariate Cheby- indeed be carried out for every Weyl group. shev polynomials via permutation groups. The construction yields a basis of the space of multivariate polynomials. This ii.) Properties i.) to iv.) of Prop. 2 also hold for any group basis has useful properties, which can be exploited to develop of integer matrices. FFT-like algorithms. We start by recalling the definition of the iii.) By looking at the definition it is not easy to derive classical Chebyshev polynomials of the first kind as that the multivariate Chebyshev polynomials are actually polynomials. This can be clarified by the recurrence T (x) = T (cos θ) = cos nθ = 1 e2πinθ + e−2πinθ (3) k k 2 relation using a suitable and sufficient set of starting for θ ∈ (0, 1) and x = cos(θ). The appearing numbers 1 conditions. and −1 can be interpreted as 1 × 1 matrices representing iv.) The decomposition property Prop. 2, vi.) is stated in the permutations of two elements. The number 2 is then the a more intricate form in [15, Sect. 6] for Chebyshev number of all such permutations, i.e. we average over all polynomials associated to affine Weyl groups and in [23, permutations. For a generalization of Chebyshev polynomials Sect. 3] using the algebraic generalization. We are interested in the permutation group S4. This group yields multivariate Chebyshev polynomials giving rise to a signal model on the FCC lattice. There are 24 possiblities to permute 4 elements. These permutations can be represented as 3 × 3 matrices, with generator matrices h −1 0 0 i h 1 1 0 i h 1 0 0 i s1 = 1 1 0 , s2 = 0 −1 0 , s3 = 0 1 1 . (7) 0 0 1 0 1 1 0 0 −1 2 1 2 1 3 These matrices satisfy si = 3, (s1s2) = 3, (s1s3) = 3 13, (s2s3) = 13, where 13 denotes the 3 × 3 identity matrix. All other matrices, representing a permutation of 4 letters, can be generated by various multiplications of these three matrices.  3 3 Let θ1 θ2 θ3 ∈ Z . Using coordinates u := e2πiθ1 , v := e2πiθ2 , and w := e2πiθ3 , we obtain the general Fig. 1: The common zeros of T8,0,0,T0,8,0,T0,0,8. power form 1 n l+m −l T n  = Tn,m,`(u, v, w) = 24 (u v w 2πi/n m with ωn = e being a root of unity and i, j, k = 0, . . . , n− ` 1. + u−nvl+m+nw−l + unvmwl + u−nvm+nwl + um+nvlw−l−m The 512 common zeros for n = 8 are displayed in Fig. 1, where we used the real coordinates from above. + u−m−nvl+m+nw−l−m + ul+m+nv−lw−m −l−m−n m+n −m l+m+n −l−m m In case of the symmetric group S4 the general recurrence + u v w + u v w relation Prop. 2, iv.) reads + u−l−m−nvnwm + um+nv−mwl+m + u−m−nvnwl+m + ul+mv−lw−m−n  −l−m m −m−n m l −l−m−n 1 + u v w + u v w Tj,k,p · Tn,m,` = 24 T −j + k + n  + T −j + k + n  −j + m + p −j + k + m − p + u−mvl+mw−l−m−n + ulv−l−mw−n l − j l − j −l −m −n −l −m−n n + T −j + k + n  + T −j + k + n  + T−j + k + n + u v w + u v w −j + m + p −j + k + m − p k + m l + p k + l − p l + p l −l−m−n n l+m −l−m−n m+n + u v w + u v w + T−j + k + n + T−k + n + p + T n − p  −l−m −n m+n m −m−n l+m+n k + m −j + m + p −j + k + m − p + u v w + u v w k + l − p l − j l − j

−m −n l+m+n + T−k + n + p + T n − p  + T−k + n + p + u v w ) −j + m + p −j + k + m − p m − k l + p k + l − p l − j The form can be obtained via the parametrization + T n − p  + T−k + n + p + T n − p  m − k m − k m − k 1 −1 −1 −1 l − j j − k + l j − k + l x := 4 u + u v + v w + w , (8) 1 −1 −1 + T −k + n + p  + T n − p  + T −k + n + p  y := 6 v + v + uw + (9) j − k + m + p j + m − p j − k + m + p l + p k + l − p j − k + l +u−1vw−1 + u−1w + uv−1w, + T n − p  + T j + n  + T j + n  + T j + n  1 −1 −1 −1  j + m − p k + m k + m j − k + m + p z := 4 u + uv + vw + w . (10) j − k + l l + p k + l − p l + p  Note that x and z are complex conjugates while y is real −1 −1 −1 + T j + n  + T j + n  +T j + n . since u = u , v = v , and w = w . Hence we have a j + m − p j − k + m + p j + m − p 1 k + l − p j − k + l j − k + l real representation if we use the coordinates x := 2 (x + z) 1 e and ze := 2i (x − z). Using the power form we can easily deduce the common This recurrence relation allows to derive the visualization of zeros of a suitable subset of the generalized Chebyshev the signal model. polynomials, which we need for the development of a Cooley- Tukey-type-algorithm on the FCC lattice. IV. FFT ALGORITHMFOR FCC COSINETRANSFORM Lemma 4 The system of equations

Tn,0,0(x, y, z) = 0, (11) A. The FCC cosine transform

T0,n,0(x, y, z) = 0, (12) We consider the signal model M = A = T0,0,n(x, y, z) = 0 (13)  n×n×n C[x, y, z] hTn,0,0,T0,n,0,T0,0,ni with Φ: C → M has n3 solutions, given in (u, v, w)-coordinates as given as 1+8i j 3+8k (ui, vj, wk) = (ω8n , ωn, ω8n ) (14) sn,m,` 7→ sn,m,`Tn,m,`. (15) 1  −2πir 2πi(r−s) 2πi(s−t) 2πit ρ(r, s, t) := 4 e + e + e + e ,

which yield the common zeros of T1,0,0,T0,1,0, and T0,0,1 in 1 3  (x, y, z)-coordinates for (r, s, t) = 8 , 0, 8 , i.e. they are all equal to zero.  Consider the filter space A = C[x, y, z] hTn,0,0 − σ(r, s, t),T0,n,0 − τ(r, s, t),T0,0,n − ρ(r, s, t)i and the signal space M = A . If we find a decomposition of the ideal hTn,0,0 − σ(r, s, t),T0,n,0 − τ(r, s, t),T0,0,n − ρ(r, s, t)i into coprime ideals, M decomposes into one-dimensional spaces

(a) The shifts T1,0,0 (red), (b) Convex hull of the shifts, given by the factors of the ideal by the Chinese remainder the- T0,1,0 (blue), and T0,0,1 (or- a rhombic dodecahedron. orem. The decomposition is achieved by finding the common ange). zeros of the system of equations

Tn,0,0 − σ(r, s, t) = 0,

T0,n,0 − τ(r, s, t) = 0, (17)

T0,0,n − ρ(r, s, t) = 0. Inspecting the power form, we find the n3 solutions for (17) in (u, v, w)-parameterization as 2πi r+i 2πi s+j 2πi t+k {(e n , e n , e n ) | i, j, k = 0, . . . , n − 1}. Hence we get the decomposition: (c) Space-filling via rhombic dodecahedra on FCC lattice points. Lemma 5 For any n, we have Fig. 2: The three variable Chebyshev polynomials give rise to D Tn,0,0 − σ(r, s, t),T0,n,0 − τ(r, s, t), shift operators. The rescaled convex hull of these shifts fills E the whole space. T0,0,n − ρ(r, s, t)

\ D r+i s+j t+k  (18) = x − σ n , n , n , Note that bn := (Tj,k,p | 0 ≤ j, k, p < n) is a basis of M. By 0≤i,j,k

The elements T ,T , and T are the shifts of our M .D s+j (19) 1,0,0 0,1,0 0,0,1 =∼ C[x, y, z] x − σ r+i , , t+k , signal model. If we multiply any element of M by one n n n 0≤i,j,k

B. The Cooley-Tukey-type-algorithm for FCC DCT

In this subsection we derive the fast radix-2×2×2 algorithm for the FCC cosine transform. Recall from [13, Sect. 3], that a recursive FFT algorithm can be deduced via stepwise decomposition of the signal space M. This procedure then gives a fast algorithm if the sample size n is decomposable, e.g. n = 2k for some k. Applying the stepwise composition for the case n = 2m yields

C[x, y, z] (21) Fig. 3: Runtime of a naive O(n6) implementation (red di- hTn,0,0 − σ(r, s, t),T0,n,0 − τ(r, s, t),T0,0,n − ρ(r, s, t)i amonds) and the fast O(n3 log(n)) algorithm (blue penta- C  −→ [x, y, z] (22) grams).

T2,0,0(Tm,0,0,T0,m,0,T0,0,m) − σ(r, s, t),

T0,2,0(Tm,0,0,T0,m,0,T0,0,m) − τ(r, s, t), get a permutation matrix Pn in (25). The fast algorithm is T0,0,2(Tm,0,0,T0,m,0,T0,0,m) − ρ(r, s, t) given as the matrix M −→ C[x, y, z] (23) DCTn×n×n(r, s, t) ir ,is,it=0,1 M r+ir s+is t+it  D = Pn · DCTm×m×m , , r+ir s+is t+it  2 2 2 (26) Tm,0,0 − σ , , , 2 2 2 ir ,is,it=0,1 r+ir s+is t+it  1 T0,m,0 − τ 2 , 2 , 2 , · (DCT2×2×2(r, s, t) ⊗ m3 ) · Bn(r, s, t). E r+ir s+is t+it  Looking at the matrix representation makes it easy to see T0,0,m − ρ 2 , 2 , 2 that the whole algorithm has cost O(n3 log(n)). Hence it can M M  −→ C[x, y, z] (24) compete with the tensor product of standard Cooley-Tukey ir ,is,it=0,1 kr ,ks,kt=0,...,m−1 FFTs or fast Cosine transforms. First numerical experiments D r+ir +2kr s+is+2ks t+it+2kt  verify the effectivity of the proposed methodology compared x − σ n , n , n , to the naive O(n6) implementation, as depicted in Fig. 3. r+ir +2kr s+is+2ks t+it+2kt  y − τ n , n , n , E V. APPLICATION TO VOXEL DATA AND FUTURE WORK z − ρ r+ir +2kr , s+is+2ks , t+it+2kt  n n n One of the main advantages of using the algebraic signal M −→ C[x, y, z] (25) model is that we do not actually have to sample data on

jr ,js,jt=0,...,n−1 the FCC lattice, but can instead use the z-transform to map D any data on it. Hence we can compare the effect of the x − σ r+jr , s+js , t+jt , n n n FCC transform directly with the standard approach to discrete E y − τ r+jr , s+js , t+jt , z − ρ r+jr , s+js , t+jt  . cosine transforms on cc lattices. n n n n n n For graphic representation of voxel data on a rectangular grid, each data point gets assigned to a cuboid and can define Each step is encoded by a matrix. We have for (22) a certain properties, like color or opacity. Now for the FCC complicated basis change matrix Bn(r, s, t). We go from the lattice we have to assign the points to rhombic dodecahedra old basis bn ordered lexicographically to the new basis instead, as they fill the space on this lattice. The difference in the graphical representation is shown in Fig 4, where we " T0,0,0T0,0,0(Tm,0,0,T0,m,0,T0,0,m) # created an artifical data set portraying a sword. The data values b := . . en . were mapped to opacity values, i.e. data points with value 0 Tm−1,m−1,m−1T1,1,1(Tm,0,0,T0,m,0,T0,0,m) are not visible, while the ones with 1 are fully visible. In Fig. 5a we see the effect of the threefold tensor product of This basis change, which has O(n3) entries, will the discrete cosine transform and in Fig. 5b the effect of the be described in future works. For (23) we apply FCC Chebyshev transform. Aside from an explicit description the matrix DCT2×2×2(r, s, t) ⊗ 1m3 , for (24) we of the matrices used in the decomposition steps, we plan obtain the steps via the application of to derive Cooley-Tukey-type-algorithms based on generalized L DCT r+ir , s+is , t+it . Finally we Chebyshev polynomials for all simple, compact Lie groups. ir ,is,it=0,1 m×m×m 2 2 2 (a) Sword represented via cuboids. (a) The threefold discrete cosine spectrum of the sword on a CC lattice.

(b) Sword represented via rhombic dodecahedra. (b) The DCT spectrum of the sword on a FCC lattice. Fig. 4: Data representation on a rectangular grid and on a Fig. 5: Spectra of the Sword. SU(4) grid.

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