Average every n*n elements in a two dimensional matrix

I have a two dimensional matrix, and create a second matrix with the averages of every n*n elements. So, say I have a 100*100 matrix; I would want a 10*10 matrix where each single element is the average of the 10*10 elements in the original matrix at that location.

So far I have tried splitting the matrix into n separate row vectors, averaging each of those every n elements, concatenating and re averaging ever n of the averaged vectors. Then I concatenate each of those vectors. Is there a more efficient way to do this this, without using so many loops? It is very inefficient and inelegant for such a large matrix.

Thanks

Answer 1

[Sean de Wolski]

If you have the Image Processing Toolbox, you can use blockproc

blockproc(magic(100),[10 10],@(bs)sum(bs.data(:)))

And for more info:

doc blockproc

Otherwise just use a double for-loop.

OR if you're curious to learn about vectorizing and speed is a top priority:

G = reshape(sum(reshape(sum(reshape(A,sz(1),blk(2),[]),2),blk(1),[]),1),sz./blk);

And broken into pieces:

A   = magic(100);

blk = [10 10];
sz  = size(A);

B   = reshape(A,sz(1),blk(2),[]); %reshape so every blk column is a page
C   = sum(B,2); %sum blocks across rows
D   = reshape(C,blk(1),[]); %reshape so each column is a block
E   = sum(D,1); %sum columns
F   = reshape(E,sz./blk); %reshape to final size.

[Peter]

Yes, that's perfect. That give the sum of those elements, and to get the mean I just divided by the number of elements.