A comparative study of a CRT colorimetric prediction model by neural networks and the models by conventional method

✍ Scribed by Liao Ningfang; Gao Zhiyun

• Publisher: John Wiley and Sons
• Year: 1999
• Tongue: English
• Weight: 95 KB
• Volume: 24
• Category: Article
• ISSN: 0361-2317
• DOI: 10.1002/(sici)1520-6378(199902)24:1<45::aid-col9>3.0.co;2-n

✦ Synopsis

In order to produce desired colors on CRT screens, much work has been done on the problem of the CRT colorimetric prediction. However, it would take great pains to overcome the troubles such as the constant channel chromaticity, the gun or channel independence, and the screen background effect, etc., with the conventional prediction methods such as PLCC and PLVC models, etc. To solve such problems, we propose a completely different CRT colorimetric prediction model by using a set of Artificial Neural Networks (ANN), where a set of back-propagation (BP) neural networks is used to perform a nonlinear conversion between RGB values and XYZ values. By comparing some typical conventional CRT colorimetric prediction models with our neural-networks-based model theoretically, the article indicates that our new model can overcome the troubles faced by the conventional models, and by experiment the article shows that our new model can yield a satisfactory prediction result.