A method comprising: providing a variety of multi-channel, multiple-regression 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 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 v ^ i = sc i ⁢ C ̃ 1 + s i ⁢ M ̃ 1 + n wherein v ^ i = v ^ i ⁢ 1 v ^ i ⁢ 2 v ^ i ⁢ 3 denotes the predicted three color components of the i -th pixel of the first image, s i = s i ⁢ 1 s i ⁢ 2 s i ⁢ 3 denotes the three color components of the i -th pixel of the second image, M̃ is a 3x3 matrix and n is a 1x3 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. providing a variety of multi-channel, multiple-regression 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 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 v ^ i = sc i ⁢ C ̃ 1 + s i ⁢ M ̃ 1 + n wherein v ^ i = v ^ i ⁢ 1 v ^ i ⁢ 2 v ^ i ⁢ 3 denotes the predicted three color components of the i -th pixel of the first image, s i = s i ⁢ 1 s i ⁢ 2 s i ⁢ 3 denotes the three color components of the i -th pixel of the second image, M̃ is a 3x3 matrix and n is a 1x3 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.