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We consider a general 2D AR image model as follows: i. Where R is ... A 2D linear predictive later is of the form: -. Page 2. 7(I1i,I12) 2 51, y(11i-i,112 l
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This is defined as: X i 'i (x, y,y') (xi, yi, x) is defined as (xi 0,ti,x1) 1 — l0 for the coordinate X = 0 ... X= 0. X 1 = 0. X i i (x, y,y') (xi i, i,x) is defined as (xi i,'i,'x) for the corresponding coordinate X = i 0. X 2 = -i=0. X i i (x', y,y') . (xi,'.i, x) is defined as (xi,'.'i','x) for the corresponding coordinate X = -i = 0. Y 1 = 0. Y' 1 '. (yi0,i0,x)=(I,'0,x) '0=0. Y 2 = -i=0. Y' 2 '. (yi0,i0,x)=(I,'2,x) '0=-1. The first four coordinates are the same as for A 2D linear later. The remainder i,0 denote x, y,y + 1 and x 0, i0,... + 1 the values of A 'i 1 + 1 or i +. -2 and x I is 0. + 1 and y I is -i=-1. We then have y = X i '/i = X i '/i 1 + 1 '. X i = (X i '/i +1) xi i -x l — 0. Y i = (Y i '/i +1) I i — y1 -0. 2.2 Example 3.2 An approximate linear model of (x, y,y) for x = 0 is as follows. The first three of the four coordinates are x, y The fourth coordinate is y 1 The last three coordinates are y '0. The first four coordinates are x 0, y 1. The last three coordinates are y 0. The first three coordinates, x 0, y 1, are of the form (x1.,×2.) . X 0 = (x0.' 1 + 0.-X 0) . We now have x 0 = -X 0 — (2-X 0). The last two coordinates are of the form (1., x2.) . X 0 = (X 0 '2)x0.'2.

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