Radial Basis Function Neural Network for the Evaluation of Image Color Quality Shown on Liquid Crystal Displays

Abstract

The color quality of an image shown on a liquid crystal display (LCD) can be measured with a spectroradiometer; however, this instrument is expensive, work under controlled illumination conditions with an artificial source of light, and measurements take a long time. A spectroradiometer returns measurements of wavelength or CIE color space. A low-cost and fast alternative consists of using a digital camera that outputs RGB measurements. Unfortunately, comparisons between measurements obtained with both instruments cannot be performed; hence, conversion equations must be used. The main problem is that equations do not consider the effects caused by the camera lens, sensor variations, and configurable parameters such as gain and the exposure time. This paper proposes the architecture of a radial basis function neural network (RBFNN) to measure the image color quality displayed by an LCD using a digital camera. The RGB values acquired with a camera are used as inputs to the RBFNN. The output predicted the luminance and chromaticity components in the CIExyY color space and included the corrections to the lens and camera parameters. First, the RBFNN topology is explained, including the calculation of the number of neurons in the hidden layer, and the definition of the dispersion centers and their associated spread. Next, the experiments related to RGB color space reconstruction and conversion from RGB to CIE are presented. The proposed approach was tested on a real automotive scenario. The results obtained were similar to those measured with the spectroradiometer with an accuracy of 93.3%. Moreover, the results remained within limits established by the six-sigma methodology.