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A new set of colour appearance data was obtained from a series of experiments in which 240 wool samples having a large size of 3 x 3 in. square that subtends 10° at the observer's eye, and covering a large colour gamut were used. Each colour was assessed by a panel of five observers using a magnitude estimation method under three light sources, D65, TL84, and A. In total, 10,770 estimations were made, and these form a set of new colour appearance data in textiles. Data analysis was carried out and shows that the accuracy of the visual results for different observers is quite good. the colour appearance data were used to test the performance of seven chromatic‐adaptation transformations, i.e., BFD, Bartleson, von Kries, CIE, Hunt, CIEL*a*b*, and Fairchild. the results show that the BFD transformation gives the most accurate prediction to the visual results between all transformations studied. All transformations can have a better fit to the experimental grid under TL84 than that under A. However, the results given by the best chromatic‐adaptation transformation are still unsatisfied in colour industries, especially in the textile industry. the two sets of experimental grids respectively derived from this work and by Luo et al. (1991) were also studied. an expected difference between the two sets of experimental grids was found. the difference may mainly result from two factors: the experimental method in assessment and the size of the samples. © 1995 John Wiley & Sons, Inc.

Color image is one of the most important factors in art and design. In general, artists and designers apply their own personal image meanings into their work. However, the image meaning for a specific work is frequently in conflict with those of the general observer. Thus it is necessary and important to derive one set of merit color image scales which can be utilized to predict the color image meanings of works in parallel with the average person's perception and which can also serve as a guide for artists and designers. In this study, the psychophysical method (magnitude estimation method), usually used in visual measurement of color appearance, was employed to attempt to establish new color image scales to evaluate the color image meanings of works matching those of the average person. The results show that new color image scales WIP are developed, and the relativity between the latest color image scales WIP and the color attributes (say Lightness L*, Hue h, and Chroma C*) of the CIELAB color space is also discussed. The total mean value of coefficient of variation for the results of visual assessment in the experiment of evaluating the color image meanings of the 207 color specimens used, in general, is about 36, similar to that for those experiments conducted using the psychological method. Also, a good relationship between the new color image scales and the color attributes of the CIELAB color space can be found.

In this study, a GM(1,1) model is applied to forecast the trend of textile fashion colors. Through the historical time series data of the color suggestion ratios by international forecasting facilities in the past, the GM(1,1) model is applied to forecast the fashion color trend. Besides the GM(1,1) model, a gray neural network model (i.e. GNNM(1,1)) is developed to compare the precision with it as well. The simulated results show that the GM(1,1) model has higher forecast precision than the GNNM(1,1) one. The predicted results of the color trend using the GNNM(1,1) model are less close to the real circumstances than as expected. With the assistance of a gray model-based prediction model, the fashion color trend can be traced more precisely and easily.

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