Improved multi-input parameter optimization method for camera colorimetric characterization

Abstract

In order to improve the accuracy of camera colorimetric characterization, a multi-input parameter optimization method was proposed in this paper. The input parameters of the traditional camera characterization method were generally RGB values; in the proposed method, the luminance parameter L was introduced in addition to RGB values, and the four-input parameters of RGBL were used as input parameters for the conversion model. In the experiment, 549 colors were uniformly selected from the Munsell Book of Color (Matte Edition), and the RGBL values and corresponding CIEXYZ values of the selected colors were measured by a spectroradiometer and three cameras, including an imaging luminance meter, respectively. Then, a polynomial model and a backpropagation (BP) neural network model were employed to establish the improved color conversion model with RGBL four-input parameters, which was compared with three-input parameter models to verify the effectiveness of the proposed method. Experimental results show that the proposed method can significantly improve the conversion accuracy and reduce the color difference with a maximum reduction of 57.7% in CIELAB.