基于视觉特性的色纺纱全光谱配色算法优化

为提高色纺纱计算机配色算法的精准性及适用性,针对目前配色算法难以同时保证理论计算的色差值及配比相对偏差同时最小的问题,以全光谱配色算法为基础,结合经典Stearns-Noechel光学理论模型,探索人眼视觉特性以确定视觉特性对不同波长下反射光的敏感系数,并将其引入配色算法中进行加权计算,预测单色纤维混合配比,同时,综合预测色差值、配比相对偏差值及欧式距离等对配色效果进行评价。结果表明,引入泊松分布赋值人眼敏感系数的配色算法结果最优,平均预测色差值为0.29,且均在1以内,平均配比相对偏差值为0.612,最小,欧式距离均值为0.087,相对较小;改进之后的配色算法通过一次计算即可实现预测色差值最小,且预测配比精准,针对色纺纱可初步实现计算机精准配色。

Optimization of full spectrum color matching algorithm for color spun yarn based on visual characteristics

In order to improve the accuracy and applicability of computer color matching algorithm for color spunyarns, a full-spectrum color matching algorithm was proposed based on the classical Stearns Noechel optical theoretical model, aiming at the problems that it is difficult to minimize error in calculated color difference value and that in matching relative deviation. The sensitivity coefficient of human visual characteristics to reflected light at different wavelengths was determined by exploring human visual characteristics, and it was introduced into the color matching algorithm for weighted calculation to predict the monochrome fiber mixing ratio. The color matching effect was evaluated by predicting the color difference value, the relative deviation value of the ratio and the Euclidean distance. Results show that the color matching algorith with Poisson distribution introduced to the human eye sensitivity coefficient is optimal, with the average prediction color difference value being 0.29 and all within 1, the ratio of the average relative deviation value being minimal 0.612, Euclidean distance average being 0.087 which is relatively small. When using the improved color matching algorithm, the prediction of the color difference value can be achieved through one calculation, leading to a small color difference with higher accuracy. With the improved algorithm, computer assisted color matching for color spunyarns can be primarily achieved.