Evaluating the Performance of Polynomial Regression Method with Different Parameters during Color Characterization

Bangyong Sun,Shisheng Zhou,Han Liu,Wenli Li

doi:10.1155/2014/418651

Bangyong Sun, Shisheng Zhou + Show 2 more

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https://doi.org/10.1155/2014/418651

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Abstract

The polynomial regression method is employed to calculate the relationship of device color space and CIE color space for color characterization, and the performance of different expressions with specific parameters is evaluated. Firstly, the polynomial equation for color conversion is established and the computation of polynomial coefficients is analysed. And then different forms of polynomial equations are used to calculate the RGB and CMYK’s CIE color values, while the corresponding color errors are compared. At last, an optimal polynomial expression is obtained by analysing several related parameters during color conversion, including polynomial numbers, the degree of polynomial terms, the selection of CIE visual spaces, and the linearization.

Highlights

As color electronics often have different imaging characteristics, such as the imaging mechanism, color space, apparatus capability, and material peculiarity [1], the color images always look different in some detail when they are output by different devices
The polynomial regression method can be used to describe nonlinear problems, in which the dependent variable y is modeled as an nth degree polynomial of independent variables, so this model can be rightly used in color signal processing systems
ΔE 3.0490/9.2965 2.6520/7.0334 2.6400/6.9698 1.8530/3.4335 1.3645/1.8619 than 10 terms are used in RGB signal processing, the CIELAB color space is recommended as the device-connection space, while for the case when the polynomial terms are between 10 and 20, the CIEXYZ space is suggested

Summary

Introduction

As color electronics often have different imaging characteristics, such as the imaging mechanism, color space, apparatus capability, and material peculiarity [1], the color images always look different in some detail when they are output by different devices. (2) the selection of device-connection space, such as CIEXYZ and CIELAB spaces, may have some influence on the signal processing precision [5, 6], so it still needs to be analyzed and tested for polynomial regression models;. For both the RGB and the CMYK signals, the effect of linearization processing should be analyzed and tested [7, 8], which may reveal whether or not it should be added for specific color devices and polynomials In this paper, these issues above are analyzed and tested in corresponding experiments. The different polynomials expressions, the different device-connection color spaces, and influence of linearization for signal processing are all tested on RGB and CMYK devices. For the specific RGB and CMYK color signal processing systems, the optimal parameters are obtained with detailed analysis

Polynomial Regression Model for Color Signal Processing

Study on the Key Parameters during Color Signal Processing

Conclusions