@inproceedings{d47bf596e90a4de5a37e49297c82f474,
title = "Color characterization for displays based on color appearance matching",
abstract = "The complexity of cross-media color reproduction is that even the problem of device dependence of color space is solved, color distortion still exists in the different background and viewing condition. In this study, the color characterization for the computer monitor is established with visual matching experiments that based on the color appearance model CIECAM02 and back propagation neural network (BPNN). After analyzing prediction results and the influence of training methods, transfer function, the number of hidden layers and nodes of BPNN, 'log-sigmoid' is selected as transfer function, the structure of BPNN is 3-6-6-6-3 in this paper. The average prediction color difference of training samples and test samples are 1.016 and 1.726 respectively within acceptable range of color difference of human vision.",
keywords = "Artificial neural network, Color characterization model, Color correction, Cross-media",
author = "Xueqiong Bai and Ningfang Liao and Haobo Cheng and Wenming Yang and Chenyang Deng and Yasheng Li",
note = "Publisher Copyright: {\textcopyright} 2018 SPIE.; Optoelectronic Imaging and Multimedia Technology V 2018 ; Conference date: 11-10-2018 Through 12-10-2018",
year = "2018",
doi = "10.1117/12.2500797",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Qionghai Dai and Tsutomu Shimura",
booktitle = "Optoelectronic Imaging and Multimedia Technology V",
address = "United States",
}