1. An apparatus for content-processing filtering in an operational phase that is capable of learning from a training set of known discrete content signals and desired transform values by finding the best coefficients for a transform in a learning phase, comprising: at least one memory having stored therein at least one transform; at least one processor; and computer-readable instructions, which when executed by said processor, apply said at least one transform to received content data; wherein the at least one transform comprises linear combinations of pre-selected non-linear functions, and wherein the at least one transform is in the format of: 
 y=a 0 s 0 +a 1 s 1 +. .. +a n s n +ƒ 1 ( s )( a 1,0 s 0 +. .. +a 1,n s n )+ƒ 2 ( s )( a 2,0 s 0 +a 2,1 s 1 +. .. +a 2,n s n )+. .. where: s i (1≦i≦N) are the discrete values of a given neighborhood of s 0 and p=p 1 , p 2 ,. .. , p m are optional additional parameters and f i (s,p) (1≦i≦N) are the selected primary filter functions and wherein each specific set of a i coefficients defines a specific filter function; and wherein the functions have the property of ƒ(as+c)=f(s) for each scalar a and c values where s+c denotes the vector (s 0 +c, s 1 +c,. .. , s n +c) and ∑ 0 ≤ i ≤ n ⁢ a i = 1 as well as ∑ 0 ≤ i ≤ n ⁢ a k , i = 1 , and the desired filter function is in the format of: 
 F ( as+c )= aF ( s )+ c.