A 2-D digital image (X[i, j]) is known to have zero-mean Gaussian distribution for its intensity values. The covariance model is: E{X[i, j]X[k,1]} = e-d/4 ; d =\i-k|+| j 1|. We want to encode this image using predictive coding. The prediction template is a 2-D shape with three taps as shown below: X X=hY +hY+hY a) Determine the optimum 3-tap filter coefficients. b) Find the minimum mean squared prediction error with these filter coefficients. A 2-D digital image (X[i, j]) is known to have zero-mean Gaussian distribution for its intensity values. The covariance model is: E{X[i, j]X[k,1]} = e-d/4 ; d =\i-k|+| j 1|. We want to encode this image using predictive coding. The prediction template is a 2-D shape with three taps as shown below: X X=hY +hY+hY a) Determine the optimum 3-tap filter coefficients. b) Find the minimum mean squared prediction error with these filter coefficients.