it shows that naturally occurring images are organized by having similar rows and columns close to each other, which is - by default - good for most compression algorithms that prefer regularities/similarities to be as local as possible.
edit: but it can be good for other cases, where there are no good-enough default orders. For example "customer - product bought" matrix (binary matrix, customer rows, product columns: X customer bought Y product = 1, otherwise =0 ).
Here the order of rows/columns are not predefined, and ordering by similar customers and products the compression of the matrix could be improved. Finding these similarities also can be a good starting point for some recommendation/collaborative filtering engine.
it shows that naturally occurring images are organized by having similar rows and columns close to each other, which is - by default - good for most compression algorithms that prefer regularities/similarities to be as local as possible.
edit: but it can be good for other cases, where there are no good-enough default orders. For example "customer - product bought" matrix (binary matrix, customer rows, product columns: X customer bought Y product = 1, otherwise =0 ).
Here the order of rows/columns are not predefined, and ordering by similar customers and products the compression of the matrix could be improved. Finding these similarities also can be a good starting point for some recommendation/collaborative filtering engine.