Downsampling as a Matrix Operation

Module by: Ivan Selesnick

The downsampler is a linear system, but it is not time-invariant. (Figure 1)

Figure 1

yn=x 2 n y n x 2 n The downsampler can also be represented in matrix form. ⋮y0y1y2y3⋮=⋮⋮…1000000……0010000……0000100……0000001…⋮⋮⋮x0x1x2x3x4x5x6⋮ ⋮ y0 y1 y2 y3 ⋮ ⋮ ⋮ … 1 0000 00… … 0 0100 00… … 0 0001 00… … 0 0000 01… ⋮ ⋮ ⋮ x0 x1 x2 x3 x4 x5 x6 ⋮ where D=⋮⋮…1000000……0010000……0000100……0000001…⋮⋮ D ⋮ ⋮ … 1 0000 00… … 0 0100 00… … 0 0001 00… … 0 0000 01… ⋮ ⋮ .

We can write y=Dx y D x where DD is the infinite matrix having the form shown. Note that the matrix DD that represents downsampling is not a Toeplitz matrix -- it is not constant along the diagonals. As we know, the downsampling operation can not be represented as a convolution.

Let's see what the transpose of the downsampler is. We can do this by observing the form of the transpose of the matrix DD, which has the following form: DT=⋮⋮…1000……0000……0100……0000……0010……0000……0001…⋮⋮ D ⋮ ⋮ … 1 000 … … 0 0 00 … … 0 1 00 … … 0 0 00 … … 0 0 10 … … 0 0 00 … … 0 0 01 … ⋮ ⋮ The matrix DTD represents upsampling: z=DTy⇔zn=[↑2]yn ⇔ z D y z n [↑2] y n Note that DTD≠I D D I so therefore upsampling is not an orthogonal transformation. Indeed, when the matrix product DTD D D is computed, we find that DTD=⋮⋮…100000……000000……001000……000000……000010……000000……000001…⋮⋮ D D ⋮ ⋮ … 1 0000 0… … 0 0000 0… … 0 0100 0… … 0 0000 0… … 0 0001 0… … 0 0000 0… … 0 0000 1… ⋮ ⋮ So z=DTDx⇔zn=[↑2][↓2]xn=xnifn is even 0 ifn is odd ⇔ z D D x z n [↑2] [↓2] x n x n n is even 0 n is odd Equivalently, Zz=0.5Xz+X-z Z z 0.5 X z X z

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This work is licensed by Ivan Selesnick under a Creative Commons Attribution License (CC-BY 1.0).

Last edited by Charlet Reedstrom on Aug 13, 2005 11:11 am GMT-5.