Abstract: A method adapts image data using more than eight bits per pixel to be compatible with devices using only 8-bit per pixel data. The method separates the higher bit depth data into an 8-bit image data stream, the balance of the bits are carried in a separate tag data stream. The 8-bit image data stream can be used in legacy devices that can handle only 8-bit data, and the tag data stream can be used in legacy devices that incorporate a tag data stream for their internal image processing.

Abstract: A first sigma filtering circuit sigma filters an image to produce a filtered image. An analysis circuit processes the sigma filtered image to produce an approximation part and a detail part. A second sigma filter circuit filters the approximation part to produce a sigma filtered approximation part. Another analysis circuit process the sigma filtered approximation part to produce a second approximation part and a second detail part. A third sigma filter circuit sigma filters the second approximation part to produce a sigma filtered second filtered approximation part. A first synthesizer synthesizes the sigma filtered second filtered approximation part and the second detailed part to produce a first reconstructed image, and a second synthesizer synthesizes the first reconstructed image and the first detail part to produce a final filtered image.

Abstract: A method is described to combine two integer lookup tables to realize a single integer lookup table. The method converts each lookup table to a set of floating point values. The conversion process generates a set of floating point values that are as close as possible to the underlying analytic or smooth function that generated the tables in the first place. A system to implement the method is also described.

Abstract: A system determines the noise level of image data by high pass filtering image data. Absolutes values of the high pass filtered image data are determined. Thereafter, multiple mean values for absolute values less than a predetermined number of threshold values are determined. Based upon the determined mean values, a plurality of estimated mean values is calculated, each estimated mean value being calculated from a combination of two determined mean values. The noise of the image is determined from a combination of the minimum estimated mean value and the maximum estimated mean value. This noise can be optionally used by a sigma filter, at Step S740, to sigma filter the image data.

Abstract: 4+ color management sequentially processes four colors at a time from among the 4+ colors to leverage solution capability using a 4-color management tools. In methods and apparatus for processing 4+ colors, received information of 4+ colors may be processed in a first stage using four colors of the 4+ colors, such as CMYO. The processed four colors are then mapped into three virtual colors. The mapped three virtual colors and one additional unprocessed color of the 4+ colors are then processed in a second stage using a 4-color tool. From this, a second color model of at least five colors (4+) is generated. The resulting four colors determine five printer colors, and are then mapped into three virtual colors. The mapped three virtual colors and one additional unprocessed color of the 4+ colors are then processed in a third stage using a 4-color tool. From this, a third color model of at least five colors (4+) is generated.

Abstract: A system determines the noise level of image data by high pass filtering image data. Absolutes values of the high pass filtered image data are determined. Thereafter, multiple mean values for absolute values less than a predetermined number of threshold values are determined. Based upon the determined mean values, a plurality of estimated mean values is calculated, each estimated mean value being calculated from a combination of two determined mean values. The noise of the image is determined from a combination of the minimum estimated mean value and the maximum estimated mean value. This noise can be optionally used by a sigma filter, at Step S740, to sigma filter the image data.

Abstract: A color management method, system and storage medium output 4+ color separation signals to a 4+ (5 or more) color print engine. An input source color is converted into virtual intermediate CMYK separation signals using a 4-color management tool, which may be a conventional tool. These virtual intermediate CMYK separation signals are received as input within a digital front end (DFE), which transforms the intermediate signals into 4+ separation signals, such as for a 6-color print engine. A transformation unit of the DFE passes some of the input separation signals through directly as output (without transformation) while transforming others by splitting them into light and dark separation signals, such as light and dark cyan or magenta using a blend curve that will output the same tonal quality. The transformation unit may use a blending curve that takes into account ink-limit constraints and may maximize light colorant usage to improve image smoothness in light image regions.

Abstract: 4+ color management sequentially processes four colors at a time from among the 4+ colors to leverage solution capability using a 4-color management tools. In methods and apparatus for processing 4+ colors, received information of 4+ colors may be processed in a first stage using four colors of the 4+ colors, such as CMYO. The processed four colors are then mapped into three virtual colors. The mapped three virtual colors and one additional unprocessed color of the 4+ colors are then processed in a second stage using a 4-color tool. From this, a second color model of at least five colors (4+) is generated, The resulting four colors determine five printer colors, and are then mapped into three virtual colors. The mapped three virtual colors and one additional unprocessed color of the 4+ colors are then processed in a third stage using a 4-color tool, From this, a third color model of at least five colors (4+) is generated.

Abstract: Defects in an image forming system may give rise to scratched fiducials, missing fiducial regions, or other defects in an image that can run parallel to the process direction. The present disclosure provides for a fiducial compensation method and system for detecting defects thereby allowing spatial tone reproduction curves to be calculated and applied to a digital image in order to eliminate printed streaks due to a photoreceptor's non-uniformities.

Abstract: Disclosed is a method and system for processing image data, which may be generated by a scanning subsystem, and the segmentation and treatment of leaky windows or segments within the image. In addition to the identification of window regions or segments having leaky boundaries, the method and system include the subsequent control of enhancement and other image processing techniques applied to such images so as to reduce or eliminate artifacts that result from the processing of leaky window regions.

Abstract: Streak compensation in a digital printer is provided utilizing a spatially varying Printer Model and Run Time updates to generate Spatially Varying Tone Reproduction Curves (STRCs) which are used as actuators to compensate for streaks during run time. A full width array sensor is used to measure streak profiles and the STRCs are used as actuators to compensate for streaks. Streak profile measurements taken at a limited number of area coverage levels combined with a Printer Streaks Basis Function Model are used to estimate and project the streak behavior at all area coverage levels and at all inboard-to-outboard spatial locations. The projection is then used in a pixel-wise error feedback control scheme to drive each profile to a desired shape, thereby compensating for streaks.

Abstract: The present application is a method of producing digital image objects with enhanced halftone edges. The method operates by selecting a target pixel location within the digital image; observing a set of pixels within a pixel observation window superimposed on the digital image relative to the target pixel location; generating edge-state codes for a plurality of pairs of neighboring vectors of pixels within the pixel observation window; generating edge-identification codes from the plurality of edge-state codes using at least one look-up table; and, utilizing the edge-identification code to select and apply to the digital image at the target pixel either a first halftone screen having a first fundamental frequency and a first angle or a second halftone screen having a second fundamental frequency and a second angle, wherein the second frequency and second angle are harmonically matched to the first frequency and first angle.

Abstract: A system and method for processing a digital image for rendering are provided. The method includes performing one or more Line Width Control (LWC) operations on digital image data including pixels having pixel values representing gray levels and tag states providing information for specialized rendering techniques thereby changing one or more pixel values resulting in one or more inaccurate pixel tag states, identifying and reassigning one or more inaccurate pixel tag states for improving the rendering of the resultant digital image. An apparatus, such as an image processing system, capable of performing line width control and tag reassignment is also provided.

Abstract: The teachings provided herein disclose a method for corner sharpening in the display of a bitmapped digital image. The method includes the steps of selecting a target pixel location within the digital image; observing a set of pixels within a pixel observation window superimposed on the digital image relative to the target pixel location; generating edge-state codes for a plurality of pairs of neighboring vectors of pixels within the pixel observation window; generating corner-identification codes from the plurality of edge-state codes using at least one look-up table so as to thereby identify corner pixels; and, assigning a pixel value in an output image plane in a location corresponding to the target pixel in the input image, such that assigned value extends a corner where indicated by a corner identification code, thereby producing a sharpening effect. The method may be used for improving the print quality of line-art corners and other fine details as found in both font and image data.

Abstract: A color management method, system and storage medium output 4+ color separation signals to a 4+ (5 or more) color print engine. An input source color is converted into virtual intermediate CMYK separation signals using a 4-color management tool, which may be a conventional tool. These virtual intermediate CMYK separation signals are received as input within a digital front end (DFE), which transforms the intermediate signals into 4+ separation signals, such as for a 6-color print engine. A transformation unit of the DFE passes some of the input separation signals through directly as output (without transformation) while transforming others by splitting them into light and dark separation signals, such as light and dark cyan or magenta using a blend curve that will output the same tonal quality. The transformation unit may use a blending curve that takes into account ink-limit constraints and may maximize light colorant usage to improve image smoothness in light image regions.

Abstract: A system determines the noise level of image data by high pass filtering image data. Absolutes values of the high pass filtered image data are determined. Thereafter, multiple mean values for absolute values less than a predetermined number of threshold values are determined. Based upon the determined mean values, a plurality of estimated mean values is calculated, each estimated mean value being calculated from a combination of two determined mean values. The noise of the image is determined from a combination of the minimum estimated mean value and the maximum estimated mean value. This noise can be optionally used by a sigma filter, at Step S740, to sigma filter the image data.

Abstract: A system determines the noise level of image data by high pass filtering image data. Absolutes values of the high pass filtered image data are determined. Thereafter, multiple mean values for absolute values less than a predetermined number of threshold values are determined. Based upon the determined mean values, a plurality of estimated mean values is calculated, each estimated mean value being calculated from a combination of two determined mean values. The noise of the image is determined from a combination of the minimum estimated mean value and the maximum estimated mean value. This noise can be optionally used by a sigma filter, at Step S740, to sigma filter the image data.

Abstract: A system determines the noise level of image data by high pass filtering image data. Absolutes values of the high pass filtered image data are determined. Thereafter, multiple mean values for absolute values less than a predetermined number of threshold values are determined. Based upon the determined mean values, a plurality of estimated mean values is calculated, each estimated mean value being calculated from a combination of two determined mean values. The noise of the image is determined from a combination of the minimum estimated mean value and the maximum estimated mean value. This noise can be optionally used by a sigma filter, at Step S740, to sigma filter the image data.

Abstract: A first sigma filtering circuit sigma filters an image to produce a filtered image. An analysis circuit processes the sigma filtered image to produce an approximation part and a detail part. A second sigma filter circuit filters the approximation part to produce a sigma filtered approximation part. Another analysis circuit process the sigma filtered approximation part to produce a second approximation part and a second detail part. A third sigma filter circuit sigma filters the second approximation part to produce a sigma filtered second filtered approximation part. A first synthesizer synthesizes the sigma filtered second filtered approximation part and the second detailed part to produce a first reconstructed image, and a second synthesizer synthesizes the first reconstructed image and the first detail part to produce a final filtered image.

Abstract: The teachings provided herein disclose a method for the identification of edge pixels within a digital image. The method operates by generating edge-state codes for a plurality of pairs of neighboring vectors of pixels within a given observation window, and generating an edge-identification code from the plurality of edge-state codes using a look-up table. The edge identification provides information that can be used for subsequent treatments such as rendering anti-aliased pixels, selecting preferred halftoning and tone reproduction for edge pixels, corner sharpening, and object recognition and segmentation.