Xerox Corporation

D Rene Rasmussen

Edul N Dalal

Peter A Crean

Meera Sampath

Wencheng Wu

Michael E Farrell

David C Robinson

Gaurav Sharma

Thyagarajan Balasubramanian

Mark Q Shaw

Norman W Zeck

Susan June Zoltner

Eric Jackson

Ronald M Rockwell

Ashok V Godambe

Raj Minhas

Bimal Mishra

A print defect management device that supports job-specific print defect management automatically assesses print job pages to determine the severity of image quality defects likely to occur on one or more selected printers. Views of identified troubled pages may be rendered to include approximations of color and image quality defects based on the original page image data, and each printer's color rendition data and defect data, thereby allowing troubled pages for one or more selected printers to be viewed prior to printing. Suggested changes may be automatically or manually applied. Once satisfied with the image quality of print job pages rendered for a specific printer, a user may submit the print job to the same printer, thereby assuring that the user's image quality expectations are met in the printed product. The device may support job-specific print defect management with both local and/or remote printers via LAN, WAN and/or Internet based connectivity.

Job-specific print defect management

The present invention is directed to a system and method for test target selection in conjunction with dynamic test pattern generation. In the invention, a test pattern page(s) is composed using an optimal set of test targets, which can be accommodated or adjusted to fit within size constraints of the test pattern. The method of the present invention makes use of layout optimization to ensure that related and optimized test targets are accommodated on a single test pattern. For example, it may be preferable to 'squeeze in' a smaller-than-normal uniform area target, rather than not to print it at all during a test.

Dynamic test pattern composition for image-analysis based automatic machine diagnostics

This invention relates to a method and apparatus for implementing a trapping operation on a digital image during image processing. Some embodiments of the present invention provide an intelligent trapping process to compensate for registration errors or halo effect errors by an imaging device. At various times, a set of customized trapping parameters may be determined for the device based on its actual or estimated performance. The imaging device may be checked automatically on a periodic basis, in response to an event, or upon request. The trapping parameters may be unidirectional and may vary temporally or spatially. The imaging device can then print images based on its customized set of trapping parameters.

Intelligent trapping

An ink key simulator for image forming devices may simulate the effect of ink key adjustments in offset presses. The image adjustment may be made in response to activation of a simulated ink key, for example, by adjusting the pixel values of the stored data and/or by adjusting imaging light source intensity within a given region of the image. Using the ink key simulator, adjustments to image quality from printers may be made on-the-fly using an interface which is familiar to the operators of offset presses.

Non-offset image forming device with stimulated ink key interface and method

A method for improving the image quality, which includes substantially predicting an effect of reload error for at least one toner color of a developed image, modulating the color density of at least one pixel of a digital image to compensate for the predicted effect of the reload error, generating the developed image based upon the modulated digital image.

Reload error compensation method

A virtual tech rep system that can assist in reducing service call visits and customer complaints by readily analyzing image quality of an output image device, such as a copier or printer. The system generates a hardcopy test pattern output from the copier or printer and then scans the test pattern using either the platen of the digital copier or an external scanner to form a digital raster image. Test targets in the digital raster image are then determined using pattern recognition software and image quality analysis is then performed on the test targets based on the sensitivity of the human visual system. It is preferable that the image quality analysis is based on human vision systems rather than a direct comparison between original and printed images as many differences may go unnoticed by a human viewer whereas other types of differences may be readily noticed, even if only slight. The system optionally sends the test results and possibly part or all of the scanned test pattern to a service or diagnostic facility (remote or local) for further analysis when potential defects are identified. This process may be performed at start-up or automatically initiated after a predetermined number of operating cycles or hours of use have been attained.

Virtual tech rep by remote image quality analysis

Using a system of computer modules operatively associated with a document processing machine, banding defect analysis is accomplished by analyzing specific test patterns via image processing. The banding defects are characterized in terms of quantitative parameters based on an analysis of the banding defect. Key features are extracted from the banding defect parameters. The key features are analyzed in a diagnostic engine, to determine the possible source of the defect. The identified source is correlated to a recommended repair service procedure. The diagnostic process may be augmented by also including machine data in the analysis.

Automated banding defect analysis and repair for document processing systems

A toner process control process, which includes substantially predicting an effect of reload error on a developed test patch, generating a developed test patch, sensing color density data from the test patch with a sensor, altering the sensed color density data to compensate for the predicted effect of reload error, and adjusting toner output according to the altered sensed color density data.

Reload error compensation in color process control methods

A toner control process, which includes substantially determining how reload error will affect a printed image, modulating the color density of a test patch to compensate for reload error prior to printing the test patch, printing the modulated test patch, sensing the digital image, adjusting toner output according to the sensed digital image.

A method of adjusting an image production system to account for a variable property of a desired output medium includes storing a reference characterization of at least one measured value associated with a reference output medium. An output value, adjusted for the variable property of the desired output medium, is determined based on data including the reference characterization of the reference output medium, for example stored in color lookup tables, color rendering dictionaries, or the like. Based on the adjusted output value determined, the desired output medium is then printed or otherwise marked using the output value.

Printer characterization adjustment for different papers

This invention specifically covers one of the many image quality (IQ) metrics that can be part of an overall image quality (IQ) analysis engine. The specific problem with image quality addressed with this metric is that of printed text. Analytical outline font characters are used to represent certain traits of existing text characters, allowing proper image quality analysis while simplifying image processing. Preferably, a set of analytical outline font characters are used, with each representing a different trait.

Outline font for analytical assessment of printed text quality

An image quality analysis system is provided that can determine various aspects of image quality easily and with minimal user involvement and minimal user expertise. The system uses a scanner, either a stand-alone or part of a multi-function printer/scanner/copier, to scan a printed test pattern, and then perform a series of analyses on the scanned image using an image quality analysis module that may be built into the image output device being tested, or provided as a stand-alone component that can receive the output from the scanner. There are often a number of different test patterns that would be used depending on which print quality issues are being tested. By encoding each test pattern with a coded identification label, not only can the particular test pattern be identified, but the analysis to be performed can also be determined from the corresponding script when the printed test pattern is scanned and subsequently decoded. Particularly suitable decoders are OCR and barcode readers. Such identification labels and/or scripts can also be used to identify a particular machine that is being tested and other relevant information.

Method to allow automated image quality analysis of arbitrary test patterns

An image quality analysis system is provided for image output devices, such as printers and copiers, that overcomes problems with differences in analysis results caused by use of different image input devices. This is achieved by computing a differential transfer function that makes subsequent analysis device independent. Moreover, the analysis is performed on an image that has been blurred to also reflect absolute image quality metrics as seen by a human observer. By determining the resolving characteristics of the input scanner, the scanned image can be processed, with little or no artifacts, to resemble the image as perceived by a human observer, while at the same time eliminating differences that would arise when using a scanner having a different spatial resolving power.

Method to obtain consistent image quality measurements from different image input devices

This invention specifically covers one of the many image quality (IQ) metrics that can be part of an overall image quality analysis engine. The specific problem with image quality addressed with this metric is that of a region of a printed (or copied) image, which was intended to have a uniform color, but which shows visible color variations. The color variation can have many different forms, both with respect to the type of color difference and with respect to the spatial nature of the non-uniformities. The image quality analysis system readily distinguishes between non-uniformities in the following different categories, including: (a) amplitude modulated cluster dot halftone patterns; (b) frequency modulated halftone patterns (e.g., stochastic screens); (c) irregular two-dimensional variations from noise; (d) isolated (non-periodic) one-dimensional streaks; (e) periodic, one-dimensional bands; and (f) two-dimensional periodic variations (Moire). The results from the image quality analysis can then be used as a basis for diagnosing machine problems.

Image processing algorithm for characterization of uniformity of printed images

A special test pattern and image quality analysis system and process for evaluating the test pattern are provided to accurately measure color-color registration in an image output device that prints in CMYK color. The process is robust even when using relatively low-resolution CCD-based scanners and also is unique in that the process automatically factors out errors originating from skew between the detector and the subject of measurement. Further, registration of each of C, M, and Y relative to K are independently determined. The test pattern includes an upper part and a lower part, with both the upper and lower parts having a black K component and the upper part also including color components for C, M and Y. The system and method have potential for being built in to color printers or copiers to allow measurement of color misregistration within the paper path and automatically correct for color registration errors.

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Pittsford, NY, United States of America

D Rene Rasmussen