Image quality assessment is a very important field in image processing. Human observation is slow and subjective, it also requires strict environment setup for the psychological test ¹. Thus developing algorithms to match desired human experiments is always in need. Many studies have focused on detecting the fading phenomenon after the materials are printed, that is to monitor the persistence of the color ink ^2-4. However, fading is also a common artifact produced by printing systems when the cartridges run low. We want to develop an automatic system to monitor cartridge life and report fading defects when they appear. In this paper, we first describe a psychological experiment that studies the human perspective on printed fading pages. Then we propose an algorithm based on Color Space Projection and K-means clustering to predict the visibility of fading defects. At last, we integrate the psychological experiment result with our algorithm to give a machine learning tool that monitors cartridge life.

Date Published: 8 February 2015
PDF: 8 pages
Proc. SPIE 9396, Image Quality and System Performance XII, 93960F (8 February 2015); doi: 10.1117/12.2078324

Published in SPIE Proceedings Vol. 9396:
Image Quality and System Performance XII
Mohamed-Chaker Larabi; Sophie Triantaphillidou, Editor(s)