Multiple color channel multiple regression predictor

摘要

Inter-color image prediction is based on multi-channel multiple regression (MMR) models. Image prediction is applied to the efficient coding of images and video signals of high dynamic range. MMR models may include first order parameters, second order parameters, and cross-pixel parameters. MMR models using extension parameters incorporating neighbor pixel relations are also presented. Using minimum means-square error criteria, closed form solutions for the prediction parameters are presented for a variety of MMR models.

主权项

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, andprediction 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=sci{tilde over (C)}(1)+si{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,si=[si1 si2 si3] 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 toM~(1)=[m11(1)m12(1)m13(1)m21(1)m22(1)m23(1)m31(1)m32(1)m33(1)],⁢andn=[n11n12n13],⁢sci=[si⁢⁢1·si⁢⁢2si⁢⁢1·si⁢⁢3si⁢⁢2·si⁢⁢3si⁢⁢1·si⁢⁢2·si⁢⁢3],⁢andC~(1)=[mc11(1)mc12(1)mc13(1)mc21(1)mc22(1)mc23(1)mc31(1)mc32(1)mc33(1)mc41(1)mc42(1)mc43(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.