It is of great value to be able to track image quality of a printing system and detect changes before/when it occurs. To do that effectively, image quality data need to be constantly gathered and processed. A common approach is to print and measure test-patterns over-time at a pre-determined schedule and then analyze the measured image quality data to discover/detect changes. But due to the presence of other printer noise, such as page-to-page instability, mottle etc., it is likely that the measured image quality data for a given image quality attribute of interest (e.g. streaks) at a given time is governed by a statistical model rather than a deterministic one. This imposes difficulty for methods intended to detect image quality changes reliably unless sufficient data of test samples are collected. However, these test samples are non-value- add to the customers and should be minimized. An alternative is to directly measure and assess the image quality attributes of interest from customer pages and post-processing them for detecting changes. In addition to the difficulty caused by sources of other printer noise, variable image contents from customer pages further impose challenges in the change detection. This paper addresses these issues and presents a feasible solution in which change points are detected by statistical model-ranking.

Date Published: 24 January 2012
PDF: 10 pages
Proc. SPIE 8293, Image Quality and System Performance IX, 82930P (24 January 2012); doi: 10.1117/12.905666

Published in SPIE Proceedings Vol. 8293:
Image Quality and System Performance IX
Frans Gaykema; Peter D. Burns, Editor(s)