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The Optimal Orthogonal Transform

Module by: Phil Schniter. E-mail the author

Summary: In this module, we establish that for transform coding, the optimum orthogonal tranform is the Karhunen-Loeve Transform (KLT). Related properties are also discussed.

  • Ignoring the effect of transform choice on uniform-quantizer efficiency γy, Gain Over PCM suggests that TC reconstruction error can be minimized by choosing the orthogonal transform T that minimizes the product of coefficient variances. (Recall that orthogonal transforms preserve the arithmetic average of coefficient variances.)

    Aside: Eigen-Analysis of Autocorrelation Matrices:

    Say that R is the N×NN×N autocorrelation matrix of a real-valued, wide-sense stationary, discrete time stochastic process. The following properties are often useful:
    1. R is symmetric and Toeplitz. (A symmetric matrix obeys R=RtR=Rt, while a Toeplitz matrix has equal elements on all diagonals.)
    2. R is positive semidefinite or PSD. (PSD means that xtRx0xtRx0 for any real-valued x.)
    3. R has an eigen-decomposition
      R=VΛVt,R=VΛVt,
      (1)
      where V is an orthogonal matrix (VtV=IVtV=I) whose columns are eigenvectors {vi}{vi} of R:
      V=(v0v1vN-1),V=(v0v1vN-1),
      (2)
      and Λ is a diagonal matrix whose elements are the eigenvalues {λi}{λi} of R:
      Λ=diag(λ0λ1λN-1).Λ=diag(λ0λ1λN-1).
      (3)
    4. The eigenvectors {λi}{λi} of R are real-valued and non-negative.
    5. The product of the eigenvectors equals the determinant (k=0N-1λk=|R|k=0N-1λk=|R|) and the sum of the eigenvalues equals the trace (k=0N-1λk=k[R]k,kk=0N-1λk=k[R]k,k).
  • The KLT: Using the outer product,
    y(m)yt(m)=y02(m)y0(m)y1(m)y0(m)yN-1(m)y1(m)y0(m)y12(m)y1(m)yN-1(m)yN-1(m)y0(m)yN-1(m)y1(m)yN-12(m)y(m)yt(m)=y02(m)y0(m)y1(m)y0(m)yN-1(m)y1(m)y0(m)y12(m)y1(m)yN-1(m)yN-1(m)y0(m)yN-1(m)y1(m)yN-12(m)
    (4)
    Using Ak,kAk,k to denote the kthkth diagonal element of a matrix A, matrix theory implies
    k=0N-1σyk2=k=0N-1E{y(m)yt(m)}k,kE{y(m)yt(m)}=TE{x(m)xt(m)}Tt=Tt·TIE{x(m)xt(m)}RxTt·TIsince|TtA|=|A|=|AT|fororthogonalT=k=0N-1λkRx,k=0N-1σyk2=k=0N-1E{y(m)yt(m)}k,kE{y(m)yt(m)}=TE{x(m)xt(m)}Tt=Tt·TIE{x(m)xt(m)}RxTt·TIsince|TtA|=|A|=|AT|fororthogonalT=k=0N-1λkRx,
    (5)
    thus minimization of kσyk2kσyk2 would occur if equality could be established above. Say that the eigen-decomposition of the autocorrelation matrix of x(n)x(n), which we now denote by Rx, is
    Rx=VxΛxVxtRx=VxΛxVxt
    (6)
    for orthogonal eigenvector matrix Vx and diagonal eigenvalue matrix Λx. Then choosing T=VxtT=Vxt, otherwise known as the Karhunen-Loeve transform (KLT), results in the desired property:
    E{y(m)yt(m)}=E{Vxtx(m)xt(m)Vx}=VxtRxVx=VxtVxΛxVxtVx=Λx.E{y(m)yt(m)}=E{Vxtx(m)xt(m)Vx}=VxtRxVx=VxtVxΛxVxtVx=Λx.
    (7)
    To summarize:
    1. the orthogonal transformation matrix T minimizing reconstruction error variance has rows equal to the eigenvectors of the input's N×NN×N autocorrelation matrix,
    2. the variances of the optimal-transform outputs {σyk2}{σyk2} are equal to the eigenvalues of the input autocorrelation matrix, and
    3. the optimal-transform outputs {y0(m),,yN-1(m)}{y0(m),,yN-1(m)} are uncorrelated. (Why? Note the zero-valued off-diagonal elements of Ry=E{y(m)yt(m)}Ry=E{y(m)yt(m)}.)
  • Note that the presence of mutually uncorrelated transform coefficients supports our approach of quantizing each transform output independently of the others.

Example 1: 2×22×2 KLT Coder

Recall Example 1 from "Background and Motivation" with Gaussian input having Rx=1ρρ1.Rx=1ρρ1. The eigenvalues of Rx can be determined from the characteristic equation Rx-λI=0Rx-λI=0:

1-λρρ1-λ=(1-λ)2-ρ2=01-λ=±ρλ=1±ρ.1-λρρ1-λ=(1-λ)2-ρ2=01-λ=±ρλ=1±ρ.
(8)

The eigenvector v0 corresponding to eigenvalue λ0=1+ρλ0=1+ρ solves Rxv0=λ0v0Rxv0=λ0v0. Using the notation v0=v00v01v0=v00v01 and v1=v10v11v1=v10v11,

v00+ρv01=(1+ρ)v00ρv00+v01=(1+ρ)v01v01=v00.v00+ρv01=(1+ρ)v00ρv00+v01=(1+ρ)v01v01=v00.
(9)

Similarly, Rxv1=λ1v1Rxv1=λ1v1 yields

v10+ρv11=(1-ρ)v10ρv10+v11=(1-ρ)v11v11=-v10.v10+ρv11=(1-ρ)v10ρv10+v11=(1-ρ)v11v11=-v10.
(10)

For orthonormality,

v002+v012=1v0=1211v002+v012=1v0=1211
(11)
v102+v112=1v1=121-1.v102+v112=1v1=121-1.
(12)

Thus the KLT is given by T=Vxt=(v0v1)t=12111-1T=Vxt=(v0v1)t=12111-1.
Using the KLT and optimal bit allocation, the error reduction relative to PCM is

σr2|TCσr2|PCM=γyγx·(1+ρ)(1-ρ)12(1+ρ)+(1-ρ)=1-ρ2σr2|TCσr2|PCM=γyγx·(1+ρ)(1-ρ)12(1+ρ)+(1-ρ)=1-ρ2
(13)

since γy=γxγy=γx for Gaussian x(n)x(n). This value equals 0.6 when ρ=0.8ρ=0.8, and 0.98 when ρ=0.2ρ=0.2 (compare to Figure 2 from "Background and Motivation").

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