Learning color-appearance models by means of feed-forward neural networks

Device-independent color imaging demands a reliable color-appearance model. We present a method for faithfully approximating color-appearance models b y means of feed-forward neural networks trained with the error back-propaga tion algorithm. In particular we present experimental evidence that in seve ral "standard" viewing conditions recommended for testing color-appearance models, the same network architecture is capable of learning quite satisfac torily the transformations performed by different color-appearance models. (C) 1999 John Wiley & Sons, Inc.