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motion estimation and compensation for very low bitrate video coding

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174 Chapter B. Rate Distortion theory<br />

measures the average a priori uncertainty onX.<br />

Considering two alphabets AM <strong>and</strong> AN , one can de ne a product space<br />

AMN:<br />

AMN : f(j; k)jj 2 AM;k2 AN g : (B.6)<br />

The joint distribution p(j; k) <strong>and</strong> the joint ensemble (AMN;p(j; k))<br />

help de ning the marginal distributions:<br />

p(j)=<br />

N,1 X<br />

k=0<br />

p(j; k); q(k) =<br />

<strong>and</strong> the conditional distributions:<br />

p(jjk)=<br />

p(j; k)<br />

q(k)<br />

M,1 X<br />

j=0<br />

p(j; k); (B.7)<br />

p(j; k)<br />

; q(kjj)= : (B.8)<br />

p(j)<br />

A r.v. Z that assumes the event (j; k) with a probability p(j; k) maybe<br />

de ned as: Z =(X; Y ) with X : p(j) <strong>and</strong> Y : q(k). The conditional<br />

self-in<strong>for</strong>mation is the in<strong>for</strong>mation one receives when told that the<br />

event X = j has occurred if one already knows the occurrence of the<br />

event Y = k:<br />

<strong>and</strong> the conditional entropy is:<br />

I(jjk)=, log p(jjk); (B.9)<br />

H(XjY )=, X<br />

p(j; k) log p(jjk): (B.10)<br />

The mutual in<strong>for</strong>mation of both r.v.'s X <strong>and</strong> Y is thus:<br />

j;k<br />

I(j; k)=I(j) , I(jjk)=I(k; j); (B.11)<br />

<strong>and</strong> the average mutual in<strong>for</strong>mation or average amount of transmitted<br />

in<strong>for</strong>mation:<br />

H(X; Y )=H(X) , H(XjY )= X<br />

p(j; k)<br />

p(j; k) log : (B.12)<br />

p(j)q(k)<br />

j;k

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