Characterization of Electrophotographic Print Artifacts: Banding, Jitter, and Ghosting

Abstract

Electrophotographic (EP) print banding, jitter, and ghosting artifacts are common sources of print quality degradation. Traditionally, the characterization of banding and jitter artifacts relies mainly on the assumption that the defect has either a horizontal or vertical orientation which permits the simple 1-D analysis of the defect profile. However, this assumption can easily be violated if a small amount of printer or scanner skew is introduced to the analyzed images. In some cases, the defect can inherently be neither vertical nor horizontal. In this case, unless the defect orientation has been accurately detected before analysis, the 1-D-based approaches could bias the estimation of the defect severity. In this paper, we present an approach to characterize the jitter and banding artifacts of unrestricted orientation using wavelet filtering and 2-D spectral analysis. We also present a new system for detecting and quantifying ghosting defects. It includes a design for a printed test pattern to emphasize the ghosting defect and facilitate further processing and analysis. Wavelet filtering and a template matching technique are used to detect the ghost location along and across the scanned test pattern. A new metric is developed to quantify ghosting based upon its contrast, shape, and location consistency. Our experimental results show that the proposed approaches provide objective measures that quantify EP defects with a rank ordering correlation coefficient of 0.8 to 0.98, as compared to the subjective assessment of print quality experts.

Competition on counter measures to 2-D facial spoofing attacks

“…Our spoofing detection approach was inspired by image quality assessment and characterization of printing artifacts [9]. It is assumed that the face prints contain printing quality defects which can be recognized using texture features.…”

Face liveness detection by exploring multiple scenic clues

“…For photo based attacks, for instance, [15] all background 99.375% MTA [6] all background 100% print banding, jitter, and ghosting artifacts are all common reasons for quality degradation as described in [5], [14]. Here we focus on the banding effect.…”

“…These two clues are all explored in temporal domain. Since the 2D fake faces are usually printed by a printer or showed through LCD screens, banding effect [5], which is absent in a genuine face, probably will exist on the fake images, due to the quality degradation in reproduction. This is the third liveness clue named banding effect, and is explored in spatial domain.…”

Analysis and Evaluation Model for Print Mottle using Wavelet Image Denoising Method

“…Wavelet analysis is a powerful tool to analyze images, as it can relate time domain with frequency domain to compensate for the shortcomings of the above techniques. Therefore, wavelet analysis has become more widely used in analyzing print mottle (Bernié et al 2004;Dube et al 2005;Eid et al 2011;Liu et al 2012).…”