Abstract: A method and system for image processing, in conjunction with classification of images between natural pictures and synthetic graphics, using SGLD texture (e.g., variance, bias, skewness, and fitness), color discreteness (e.g., R_L, R_U, and R_V normalized histograms), or edge features (e.g., pixels per detected edge, horizontal edges, and vertical edges) is provided. In another embodiment, a picture/graphics classifier using combinations of SGLD texture, color discreteness, and edge features is provided. In still another embodiment, a “soft” image classifier using combinations of two (2) or more SGLD texture, color discreteness, and edge features is provided. The “soft” classifier uses image features to classify areas of an input image in picture, graphics, or fuzzy classes.

Abstract: A method and apparatus are provided for use in single channel segmentation of color images for reclassifying pixels which were inappropriately classified as belonging to a “black” or “white” class. The apparatus includes a re-classification circuit receiving first classification data generated by a single channel segmentation circuit operative to classify pixels of a multi-color channel input image. The re-classification circuit selectively re-classifies pixels that were previously classified by the single channel segmentation circuit into an “other” segmentation class based on a comparison of classification data associated with the pixels relative to a set of predefined classification data types.

Abstract: A color conversion table designed to produce 0% under-color removal and a 100% under-color removal TRC are configured as a matched pair and are used to render an image from fuzzy detected color signals, including providing variable under-color removal to obtain fuzzy black conversion. The output of the color conversion table and the TRC are multiplied by a weighted value which depends on a value of a received neutral tag. The weighted outputs of the color conversion table and TRC are added such that a varying weighted output is generated. The generated output is used in image rendering to produce a smooth transition from a full color to monochrome, where variable amounts of under-color removal are obtained using the color correction table and TRC.

Abstract: Image information is re-calibrated based on the type of marking process and materials by which the image corresponding to the image information was marked on a substrate. The marking process is automatically detected by using, for example, at least one spatial characteristic obtained from the image information.

Abstract: A method and apparatus are provided for the processing of an image, such as a document. The invention determines the location of differing content types within the document allowing specialized processing of various content types. The invention performs the identification of pixels having similar content characteristics into windows during the first scanning pass of the document by the use of an identifier equivalence table to update selected memory locations to a base identifier during processing. A second pass processing is available to enhance or alter the image by the use of the information gathered during first pass processing. The present invention benefits from a very low memory requirement while being able to determine windows extending the length or width of the image.

Abstract: A method and system implements a dynamic error diffusion process. A grey level value representing a pixel is received. An image segmentation circuit determines an image characteristic of the pixel being processed. A threshold circuit thresholds the grey level value (pixel) and generates an error value as a result of the threshold process. A portion of the error value is diffused to adjacent pixels on a next scanline. The distribution of this error value is dynamic in that different sets of weighting coefficients are used based the image characteristic of the processed pixel. One set of coefficients are utilized in processing a pixel having a first image characteristic, and a second set of coefficients are utilized in processing a pixel having a second image characteristic.

Abstract: A method and system implements screening and a high addressability characteristic into an error diffusion process. A grey level value representing a pixel is received. The grey level value has a first resolution which corresponds to an original input resolution. The grey level value is then screened. A threshold circuit thresholds the grey level value and generates an error value as a result of the threshold. A portion of the error value is diffused to adjacent pixels on a next scanline. The screened grey level value can also be interpolated to generate subpixel grey level values which correspond to a second resolution. The second resolution is higher than the first resolution and corresponds to the high addressability characteristic. The threshold circuit would then threshold the grey level value and generate an error value having a resolution corresponding to the first resolution.

Abstract: A system and method classify a pixel of image data as one of a plurality of image types. A first image characteristic value for the pixel, a second image characteristic value for the pixel, a third image characteristic value for the pixel, and a fourth image characteristic for the pixel is determined. Some of these determinations may be resolution dependent. The values from these determination are utilized in assigning an image type classification to the pixel. An auto-segmentation circuit enables classifying a pixel as a continuous tone and detecting a gradient in the window of pixels of which the pixel is a member. A filter enhances the image data of the pixel tone when a gradient indicating that image data within the window of pixels is changing at a rate greater than a predetermined threshold is detected or lowpasses the image data of the pixel tone when a gradient indicating that image data within the window of pixels is changing at a rate less than a predetermined threshold is detected.

Abstract: A high addressable hybrid error diffusion process adds an error value to an input grey image value to produce a modified input grey image value before comparing the modified input grey image value with a predetermined threshold value. A rendering value and error is generated based on the comparison. The error is equal to n(GL/N)*Aeff, wherein n is equal to the number of subpixels turned ON, GL is the maximum grey value for a pixel, N is the addressability of the rendering system, and Aeff is an effective spot area value for the rendering system. The effective spot area value is retrieved from a look-up table having a plurality of effective spot area values based on the input grey image value.

Abstract: A method and apparatus for segmenting image data into windows and for classifying the windows as typical image types includes making two passes through the image data. The method includes a step of making a first pass through the image data to identify windows and to record the beginning points and image types of each of the windows, and a step of making a second pass through the image data to label each of the pixels as a particular image type. The invention also includes a macro-detection method and apparatus for separating a scanline of image data into edges and image runs and for classifying each of the edges and image runs as standard image types. In the macro-detection method, image runs and edges are classified based on micro-detection results and based on image types of adjacent image runs.

Abstract: A system and method classify a pixel of image data as one of a plurality of image types. A first image characteristic value for the pixel, a second image characteristic value for the pixel, a third image characteristic value for the pixel, and a fourth image characteristic for the pixel is determined. Some of these determinations may be resolution dependent. The values from these determination are utilized in assigning an image type classification to the pixel. Moreover, if at least one of the image characteristic values is greater than a predetermined threshold value the pixel is classified as a halftone peak value.

Abstract: An image desired to be reproduced is scanned to determine its video pixel gray values. A histogram generator generates a histogram distribution representing a frequency of the gray values. The histogram distribution is analyzed to determine a background peak gray value of the image and a standard deviation of the histogram distribution based on a Gaussian approximation. A thresholding circuit dynamically adjusts the background peak value based on the standard deviation and a selected scaling factor to generate a background threshold value. The background threshold value expands a range of background gray values in the image which are eliminated during image reproduction. Eliminating substantially all background gray values improves the quality of the reproduced image.

Abstract: Automatic image enhancement of halftone images is performed by subjecting the halftone image to a low-pass filter so as to smooth out halftone variations between adjacent pixels prior to performing conventional image analysis and image processing.

Abstract: A system and method classify a pixel of image data as one of a plurality of image types. A first image characteristic value for the pixel, a second image characteristic value for the pixel, a third image characteristic value for the pixel, and a fourth image characteristic for the pixel is determined. Some of these determinations may be resolution dependent. The values from these determination are utilized in assigning an image type classification to the pixel. Moreover, if at least one of the image characteristic values is greater than a predetermined threshold value the pixel is classified as a halftone peak value.

Abstract: A system and method classify a pixel of image data as one of a plurality of image types. A first image characteristic value for the pixel, a second image characteristic value for the pixel ,a third image characteristic value for the pixel, and a fourth image characteristic for the pixel is determined. Some of these determinations may be resolution dependent. The values from these determination are utilized in assigning an image type classification to the pixel. Moreover, if at least one of the image characteristic values is greater than a predetermined threshold value the pixel is classified as a halftone peak value.

Abstract: A system and method provides the necessary image information to enable tonal correction for independent regions of a compound document. A scanner scans an image and generates image data. A histogram circuit, connected to the scanner, generates histogram data from the image data for a predetermined number of regions corresponding to the image. A windowing circuit, operatively connected to the scanner and in parallel with the histogram circuit, generates a window corresponding to an independent area of the image that requires image enhancement and an association circuit associates corresponding histogram data with the generated window. The associated histogram data and window are used by an image enhancement process to modify the image data to provide a more desirable reproduction of the scanned image.

Abstract: A method and system implements a dynamic error diffusion process. A grey level value representing a pixel is received. An image segmentation circuit determines an image characteristic of the pixel being processed. A threshold circuit thresholds the grey level value (pixel) and generates an error value as a result of the threshold process. A portion of the error value is diffused to adjacent pixels on a next scanline. The distribution of this error value is dynamic in that different sets of weighting coefficients are used based the image characteristic of the processed pixel. One set of coefficients are utilized in processing a pixel having a first image characteristic, and a second set of coefficients are utilized in processing a pixel having a second image characteristic.

Abstract: A method and apparatus to process an electronic image of a document that is previously divided into a plurality of classified tiled rectangular blocks. Each pixel within halftone blocks is analyzed to calculate a busyness measure value, preferably a sum of Laplacians although other range-type functions could be used. When the busyness value is less than a first predetermined threshold value, the video intensity level of the pixel is compared against a second predetermined threshold value and when greater than the second predetermined threshold value, the pixel is re-classified as being a Text document type to produce a crisp, clean background in the rendered image.

Abstract: The present invention provides a marker detection and thinning process for a photocopier which is executed during a single scan of a document and does not require a prescan of the document. The system realtime analyzes pixel image data, which is stored in a memory during the scanning process, to detect a marker line of a predetermined color and to thin the marker line. This analysis begins with image data stored in the memory that corresponds to a first pixel. The first pixel is associated with the scanline being presently scanned. It is determined if the image data of the first pixel represents a color that is equivalent to the predetermined color. Marker data is then stored in a buffer at a location corresponding to the first pixel if it is determined that the image data of the first pixel represents a color that is equivalent to a predetermined marker color. The data, stored in the buffer at a second pixel location, is analyzed.

Abstract: A printing system for rendering marks on a recording medium receives a multi-level grey scale pixel value representing a pixel having a first resolution. A screening circuit generates a screened multi-level grey scale pixel value equal to (G.sub.L -V.sub.i)+(S.sub.i -Th)*Dmp.sub.Vi *Mod.sub.Eff wherein G.sub.L is the maximum grey level value of the pixel, V.sub.i is equal to the multi-level grey scale pixel value of the first resolution, S.sub.i is equal to a screen value corresponding to a position of the pixel, Th is the threshold value, Dmp.sub.Vi is a video dependent dampening factor, and Mod.sub.Eff is a modulation multiplication factor. An interpolator converts the screened multi-level grey scale pixel value to a second resolution, the second resolution being higher than the first resolution, and a binarization circuit binarizes the converted multi-level grey scale pixel value so as to output a binary signal and an error value, the error value having a resolution equal to the first resolution.