Long short-term memory model for predicting the angle-dependent reflectance distributions for glossy and matte surfaces

This study introduces an innovative recurrent neural network called long short-term memory (LSTM) as a prediction model, which is used to predict angle-dependent reflectance distributions of colour samples with glossy and matte surfaces. A two-dimensional (2D) reflectance measurement system was developed to measure the angle-dependent reflectance in this study. Its structure mainly included a semicircular rotating mechanism, a high-resolution digital camera and a high-quality white light-emitting diode. A semicircular rotating mechanism was designed to rotate from 10° to 170° in the vertical direction. Two ColorGauge miniaturised colour charts with glossy and matte surfaces were selected as test chips. The test chips on ColorGauge miniaturised colour charts included fives colours of glossy white, glossy black, matte red, matte green and matte blue. The reflectance distributions of the test chips were measured by the 2D reflectance measurement system, and the measured reflectance data were used as training data in the LSTM model. In comparison with second- and third-order regressions, the mean CIE lightness difference (0.09) using the LSTM model was lower. Therefore, it was verified that the LSTM model performed well in predicting reflectance distributions. In addition, the LSTM model was also validated on the additional test samples (10 matte chromatic samples and five glossy achromatic samples). The maximum and minimum mean CIE lightness differences were 3.77 and 0.64 for matte chromatic samples, and 2.34 and 0.42 for glossy achromatic samples, respectively. The results of small prediction errors indicated that the LSTM model presents excellent prediction performance.