1. A method comprising: providing a variety of multi-channel, multiple-regression (MMR) prediction models, each MMR prediction model adapted to approximate an image having a first dynamic range in terms of an image having a second dynamic range, and prediction parameters of the respective MMR prediction model, by applying inter-color image prediction; receiving a first image and a second image, wherein the second image has a different dynamic range than the first image; selecting a multi-channel, multiple-regression (MMR) prediction model from the variety of MMR models; determining values of the prediction parameters of the selected MMR model; computing an output image approximating the first image based on the second image and the determined values of the prediction parameters applied to the selected MMR prediction model; outputting the determined values of the prediction parameters and the computed output image, wherein the variety of MMR models includes a first order multi-channel, multiple regression prediction model incorporating cross-multiplications between the color components of each pixel according to the formula 
 {circumflex over (v)} i =sc i {tilde over (C)} (1) +s i {tilde over (M)} (1) +n wherein {circumflex over (v)} i =[{circumflex over (v)} i1 {circumflex over (v)} i2 {circumflex over (v)} i3 ] denotes the predicted three color components of the i-th pixel of the first image, s i =[s i1 s i2 s i3 ] denotes the three color components of the i-th pixel of the second image, {tilde over (M)} (1) is a 3×3 matrix and n is a 1×3 vector according to M ~ ( 1 ) = [ m 11 ( 1 ) m 12 ( 1 ) m 13 ( 1 ) m 21 ( 1 ) m 22 ( 1 ) m 23 ( 1 ) m 31 ( 1 ) m 32 ( 1 ) m 33 ( 1 ) ] , ⁢ and n = [ n 11 n 12 n 13 ] , ⁢ sc i = [ s i ⁢ ⁢ 1 · s i ⁢ ⁢ 2 s i ⁢ ⁢ 1 · s i ⁢ ⁢ 3 s i ⁢ ⁢ 2 · s i ⁢ ⁢ 3 s i ⁢ ⁢ 1 · s i ⁢ ⁢ 2 · s i ⁢ ⁢ 3 ] , ⁢ and C ~ ( 1 ) = [ mc 11 ( 1 ) mc 12 ( 1 ) mc 13 ( 1 ) mc 21 ( 1 ) mc 22 ( 1 ) mc 23 ( 1 ) mc 31 ( 1 ) mc 32 ( 1 ) mc 33 ( 1 ) mc 41 ( 1 ) mc 42 ( 1 ) mc 43 ( 1 ) ] , wherein the prediction parameters of said first order multi-channel, multiple regression prediction model are numerically obtained by minimizing the mean square error between the first image and the output image.