Print Quality Inspection

Technology developed at the lab for automated print quality diagnostics was sold as part of a product with the introduction of the inline scanner in May 2010. The digital quality assurance technology was introduced as a major new feaure of the HP Indigo 7600 press in May 2012.

The inspection technology behind the Automated Alert Agent is all about perceptual image difference. We have to compare the image of the digital original and the scanned image of the actual print. But, images should be compared as a human would, namely, we have to understand what differences are expected due to the print and scan process, what differences are ‘forgivable’, and modify this decision accordingly. Notice that some of the decisions depend on the job print content and some on customer requirements. To that end visual feedback on customer jobs needs to filter ‘technical’ differences, and be able to scale all other differences perceptually.

The image of the AAA interface depicts a defect detected and reported by the system Notice that the digital original on the left has different colors than the scan of the print on the right. Also, the smooth blue background has texture that is typical of the way print micro-pattern interplays with the scanner array. Additionally, the micro-structure of printed edges are obviously not as smooth as the original. None of these major differences triggered the system. It was the faint artifact that triggered the alert. Indeed such defects, while faint are often objectionable. This faint artifact due probably to a dent in the press’ blanked caused possibly by a ware due to past paper jam is detected reliably in real -time, with an insignificant rate of false alarms.

The system behind AAA is not trivial nor is it simple. It includes known and novel algorithms spanning color processing, image registration, mathematical morphology, and parallel computing. However, the technology which made the difference is our image difference technology. It is based on the state of the art Structural SIMilarity (SSIM) metric, but makes sure it is invariant to sub-pixel misregistration inherent in the print and scan scenario.

Understanding that diagnostics is changing with the machine it diagnoses, we developed a technology that can learn to identify new defects based on tagged examples so that new diagnostics routines can be automated based on a simple procedure rather than involving an imaging expert every time there is a need to update print quality troubleshooting. The print quality diagnostics technology is based on perceptual quality evaluation and machine learning.