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Lossless Wyner-Ziv endpoint is the Slepian-Wolf rate

Example

Let (X,Y) be any finite IID source pair, let X^=X, and use Hamming distortion d(x,x^)=1{x≠x^}. At D=0, the Wyner-Ziv rate equals H(X∣Y), the same conditional rate as one-encoder lossless compression with decoder side information Y.

Facts & Assumptions

Given: A finite IID pair (X,Y), Hamming distortion, and target D=0.

[F1]

Finite-alphabet memoryless Wyner-Ziv theorem: the operational rate equals the minimum finite I(X;U∣Y) for X^=g(Y,U) with expected distortion at most zero.

[F2]

Finite conditional mutual information identities and equality: I(X;U∣Y)=H(X∣Y)−H(X∣U,Y).

[F3]

Finite-IID k-source Slepian–Wolf rate region: for two finite IID sources, achievable lossless rates satisfy RX≥H(X∣Y), RY≥H(Y∣X), and RX+RY≥H(X,Y), and these inequalities are sufficient.

[F4]

Finite conditional entropy: conditional entropy is the finite weighted entropy of the conditional laws on positive-mass conditioning values.

[F5]

Finite KL divergence is nonnegative with equality exactly at equal laws: I(X;Y)=D(PXY∥PXPY)≥0.

[F6]

Finite entropy and mutual-information chain rules: H(Y)−H(Y∣X)=I(X;Y) and H(X∣Y)+H(Y)=H(X,Y) for finite variables.

[F7]

Finite distributions, entropy, divergence, and stochastic matrices: a point-mass probability vector has entropy zero under 0ln⁡0=0.

[F8]

Finite fixed-length multiterminal lossless code: separate finite-length encoders send indices to a common decoder, and achievable rates allow vanishing block-error probability.

Verification

1.1F1F2F4F7givenalgebra

Any feasible test channel has nonnegative Hamming distortion with expectation zero, so X=X^=g(Y,U) almost surely. Every positive-mass conditional law of X given (U,Y) is therefore a point mass; [F4] and [F7] give H(X∣U,Y)=0, and [F2] gives I(X;U∣Y)=H(X∣Y). The admissible choice U=X, g(y,u)=u has zero distortion and the same objective, so the Wyner-Ziv minimum is exactly H(X∣Y).

2.1F3F5F6F8step 1.1algebra∎

In the two-source Slepian-Wolf region of [F3], the subset {X} requires RX≥H(X∣Y). The rate pair (RX,RY)=(H(X∣Y),H(Y)) satisfies the other two inequalities because H(Y)≥H(Y∣X) follows from [F5] and the first identity in [F6], while H(X∣Y)+H(Y)=H(X,Y) is the second identity in [F6]. These are the operational rates of the fixed-length code convention in [F8], so the minimum X-encoder rate with the Y-encoder sending its lossless sequence is H(X∣Y), matching step 1.1.

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