Autonomous detection of text fade point with color laser printers

Abstract

Fading is one of the issues of most critical concern for print quality degradation with color laser electrophotographic printers. Fading occurs when the cartridge is depleted. ISO/IEC 19798:2007(E) specifies a process for determining the cartridge page yield for a given color electro-photographic printer model. It is based on repeatedly printing a suite of test pages, followed by visual examination of the sequence of printed diagnostic pages. But this method is a very costly process since it involves visual examination of a large number of pages. And also the final decision is based on the visual examination of a specially designed diagnostic page, which is different than typical office document pages, since it consists of color bars, and contains no text. In this paper, we propose a new method to autonomously detect the text fading in prints from home or office color printers using a typical office document page instead of a specially designed diagnostic page. In our method, we scan and analyze the printed pages to predict where expert observers would judge fading to have occurred in the print sequence. Our approach is based on a machine-learning framework in which features derived from image analysis are mapped to a fade point prediction.

Blockwise Based Detection of Local Defects

“…Jing et al [2] borrowed a metric from the image quality area to print defect assessment. Ju et al [3], Yan et al [13] and Xiao et al [12] proposed new algorithms to predict the visibility of fading defects. Zhang et al [14][15] modeled periodic and aperiodic bands, and applied a histogram-based method to detect them.…”