Xerox Corporation

Ryan David Metcalfe

Xing Li

David Jon Metcalfe

Peter D McCandlish

Barbara Lynn Farrell

Clara Cuciurean-Zapan

Zhenhuan Wen

Theresa Michelle Marconi

Louis Ba La

What is disclosed is a system and method for increasing throughput in the image processing path of a digital document reproduction device. A digital image to be processed is received. Memory is allocated into two buffers, Band B. Each buffer has size where M is a size of the allocation. The digital image is divided into N number of tiles. Then, for tiles T, where i=1 to N: (A) the image data of tile Tis decompressed into buffer B, and (B) the image data of buffer Bis decompressed into the image path pipeline. Send a signal indicating that processing of the digital image has completed.

Increasing data throughput in the image processing path of a document reproduction device

A method, non-transitory computer readable medium and apparatus for starting a multiple scanline error diffusion method are disclosed. For example, the method includes identifying a pixel for each scanline of a plurality of scanlines, wherein the pixel that is identified in the each scanline of the plurality of scanlines is offset, setting all pixels behind the pixel for the each scanline of the plurality of scanlines that is identified with a white pixel value and starting the multiple scanline error diffusion method.

Method and apparatus for starting an error diffusion method

Image scaling disclosed herein comprises receiving an image from an image capturing device and partitioning an image into at least one image tile using a partitioning module. A determination is made if the image tile requires slow scan scaling and/or fast scan scaling. The image tile is subject to slow scan scaling. The image tile is then transposed, scaled in the fast scan direction, and then again transposed to an original orientation. The tile is reassembled into a scaled image and may be rendered by a rendering device.

Vectorized two stage tile-based scaling

An image processor determines the number of pixels to be added/removed within each row/column of pixels of an image to accomplish a given image enlargement/reduction. The image processor evenly distributes pixels that are to be added/removed along the full length of each of the rows or columns of pixels, and adds/removes pixels in a process that identifies a set of original pixels that are immediately adjacent to the pixel to be added/removed. The image processor then replaces each of the sets of the original pixels with a replacement set (that has one more/less pixel relative to the set of original pixels). Also, for each pixel that will be added/removed, the image processor determines data values for replacement pixels in each of the replacement sets by weighting data values from original pixels in the sets of original pixels. The image processor outputs an enlarged/reduced size version of the input image.

Simultaneous duplex magnification compensation for high-speed software image path (SWIP) applications

Methods and systems render higher bit per pixel contone images to lower bit formats using multiple registers of a SIMD processor. The rendering process uses a first register to maintain contone image values of all the pixels being simultaneously processed. A second register maintains a threshold value used during the conversion process. A third register maintains one value for the print ready format pixels (e.g., those having less bits per pixel), and a fourth register maintains the other value (e.g., 0) for the print ready format pixels. Also, a fifth register maintains the conversion error amount for all the pixels being simultaneously processed. Sixth through ninth registers maintain distributed conversion error amounts produced by the diffusing process (for different pixels being simultaneously processed); and a tenth register maintains the pixels in the print-ready format produced by the conversion for all the pixels being simultaneously processed.

Rendering images to lower bits per pixel formats using reduced numbers of registers

Methods and systems for processing a low resolution image via a low resolution error diffusion algorithm. A low resolution binary output pixel can be generated utilizing a general error diffusion algorithm. The low-resolution binary output pixel can be converted to a predetermined number of interpolated sub-pixel values and each interpolated sub-pixel values can be compared with a threshold in order to generate a scaled slow-scan output pixel utilizing the low resolution error diffusion algorithm. The low resolution error diffusion algorithm includes a highlight threshold adjustment module that adjusts level in order to prevent the scaled slow-scan output pixel from firing around the low resolution binary output pixel within a highlight region. A slow scan error with respect to the scaled slow-scan output pixel can be distributed and stored via error diffusion filter coefficients within an error buffer without additional memory.

Methods and systems for processing low resolution images via error diffusion

The present invention generally relates to systems and methods for error diffusion, e.g., for use in a halftone process. The described techniques are faster than known techniques in that they utilize fewer computations on average. Consequently, the described techniques can be more readily implemented in software, for example, though the described techniques are not limited to software.

Multi-bit error diffusion

Methods and systems methods calculate an initial matrix by correlating scanned test device-dependent values to device-independent color space values of a test sheet. The initial matrix converts device-dependent values to device-independent color space values; however, the initial matrix does include some conversion error. Such methods then calculate an inverse matrix from the initial matrix. These methods and systems then calculate target device-dependent values by applying the device-independent color space values to the inverse matrix. The methods and systems derive final one-dimensional look-up tables that isolate the conversion error by correlating the scanned test device-dependent values to the device-independent color space values. These methods and systems then derive a final matrix specific to the scanner by first applying the scanned test device-dependent values to the final one-dimensional look-up tables before creating the final matrix. Then, this final matrix can be substituted for the previously calculated conversion matrix used for non-calibration operation.

Scanner calibration using inverse matrix

A method for producing an electronic annotated document including embedding annotation information into an electronic annotation object having image data including an annotation, and producing the electronic annotated document using pixel information from the electronic document and the electronic annotation object. The annotation embedded information may be used to select corresponding pixel information from one of the electronic annotated object and electronic document to be provided in the electronic annotated document. A system and tangible storage medium having machine-readable instructions for producing an electronic annotated document are also disclosed.

Method and system for document annotation

A method for performing binary image reduction on binary image data includes receiving binary input image data; determining a conversion factor to scale (i) an input resolution to an output resolution and/or (ii) an input size to an output size; applying the conversion factor to the input image data to obtain intermediate data, where each intermediate data corresponds to at least one input pixel and at least a portion of another input pixel; obtaining a binary output image data comprising a plurality of output pixels by thresholding the corresponding intermediate data; determining an error value for each output pixel, the error value is a non-integer value obtained as a result of thresholding the intermediate data corresponding to the output pixel; and propagating the obtained error value to an adjacent output pixel in a scanline, where the output image data is scaled to the output resolution and/or the output size.

Binary reduction with structure preservation and density control

Disclosed herein is a method for detecting thin lines in image data. The method is performed by a processor to process contone image data. The processing includes thresholding a window of pixels established in the contone domain to generate a binary window of image data, and then determining characteristics associated with on pixels or runs of the binary data. The characteristics (start and end locations, length of on runs) are then thresholded. The processing in the contone and binary domain are used to determine if a thin line exists in the window of image data. The disclosed method produces better quality output images and reduces the addition of false lines in an image.

Thin line detection in scanned image data

Disclosed herein is a method for detecting thin lines in image data. The method is performed by a processor to process contone image data. The processing includes thresholding a window of pixels using a first set of thresholds established in the contone domain, and then counting and thresholding the binary pixels using a second set of thresholds. The processing in the contone and binary domain are used to determine if a thin line exists and if a pixel of interest in the window is an edge pixel that is part of a thin line. The disclosed method produces better quality output images and reduces the addition of false lines in an image.

Fuzzy method to detect thin lines in scanned image

Disclosed herein is a method for detecting thin lines in image data. The method is performed by a processor to process contone image data. The processing includes combining a first result of thin line detection using a first method and a second result of thin line detection using a second method to produce an improved thin line determination in the image data. The methods include processing and thresholding in the contone and binary domain to determine if a thin line exists in the window of image data. The thin line determination may also be merged with the image data as processed using other image segmentation techniques. The disclosed method produces better quality output images and reduces the addition of false lines in an image.

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Fairport, NY, United States of America

Ryan David Metcalfe