The engine determines the printing velocity of a printer. Nowadays, some printers incorporate an in-line scanner to scan each page as it is printed and to diagnose print quality defects in real time. However, the velocity of the paper may vary as the page is being printed. When the printer’s printing velocity is variable, and the scanner is still scanning at a constant rate, the scanned printed image will be a stretched and compressed image. This stretched and compressed scanned image is different from the real printed image. It is difficult to detect and analyze the print quality directly from this stretched and compressed scanned image. In this article, we present the printing velocity simulation and estimation. The article is organized into three parts: the first part introduces the print speed simulation tool; the second part introduces the specifically designed test page for printing velocity estimation; the third part introduces the core algorithm: the print velocity estimation algorithm. The printing velocity estimation algorithm contains image processing methods, and it uses the Dynamic Time Warping (DTW) algorithm [1] to accurately and consistently estimate the exact printing speed. In the end, we will show the results of this algorithm. This printing velocity estimation algorithm with the specifically designed test page can accurately estimate the printing velocity. Besides, the printing velocity estimation algorithm also works for complex customer content.
Local defects are very common on printed pages. Automatic detection of such defects will help the product support personnel to diagnose the problem and fix it more efficiently. Among previous works on local defect detection on printed pages, most of them divide the printed page into small blocks and calculate the variation within each block. This method is time consuming and not robust in dealing with defects at different scales. In this paper, we propose a robust framework for detecting the local defects on scanned printed pages. To achieve the efficiency and robustness, our framework applies the Gaussian pyramids method and the selective search method. We also create manual features for classification to increase the detection accuracy. Finally, applying our method on printed pages demonstrates its efficacy.
Print quality is an important criterion for a printer’s performance. The detection, classification, and assessment of printing defects can reflect the printer’s working status and help to locate mechanical problems inside. To handle all these questions, an efficient algorithm is needed to replace the traditionally visual checking method. In this paper, we focus on pages with local defects including gray spots and solid spots. We propose a coarse-to-fine method to detect local defects in a block-wise manner, and aggregate the blockwise attributes to generate the feature vector of the whole test page for a further ranking task. In the detection part, we first select candidate regions by thresholding a single feature. Then more detailed features of candidate blocks are calculated and sent to a decision tree that is previously trained on our training dataset. The final result is given by the decision tree model to control the false alarm rate while maintaining the required miss rate. Our algorithm is proved to be effective in detecting and classifying local defects compared with previous methods.