Abstract: A method, diagnostic tool and system are provided for evaluating printing defects. A captured image of a printed sheet is received and a profile generated of the printed sheet, this profile being representative of tonal variations in the printed sheet. The profile is analyzed in a plurality of different scales and an assessment made of the significance of one or more features of a printing defect throughout the plurality of different scales. From the assessed significance of the one or more features, an evaluation is carried out, in each of the plurality of different scales, of the severity of the printing defect.

Abstract: Imaging systems, articles of manufacture, and imaging methods are described according to aspects of the disclosure. According to one embodiment, an imaging system includes processing circuitry configured to access image data of a plurality of pixels of an image, to provide a plurality of representations of the image data, to determine contrast difference between the representations of the image data for a respective one of the pixels, to identify the one of the pixels as being defective using the determined contrast difference, and to provide replacement image data for the one of the pixels identified as defective and which is different than the accessed image data for the one of the pixels identified as defective.

Abstract: A method for estimating image noise. Image data for at least one column of pixels and at least one row of pixels of the image is sampled. At least one filter is applied on sampled image data resulting in at least one sequence of filtered values. A feature set of pixels of the image is selected. Image noise estimation is determined based on at least one of the filtered values of the feature set of pixels.

Abstract: Systems and methods of face and skin sensitive image enhancement are disclosed. In one aspect, a face map that includes for each pixel of an input image a respective face probability value indicating a degree to which the pixel corresponds to a human face is calculated. A skin map that includes for each pixel of the input image a respective skin probability value indicating a degree to which the pixel corresponds to human skin is ascertained. The input image is enhanced with an enhancement level that varies pixel-by-pixel in accordance with the respective face probability values and the respective skin probability values. In another aspect, a facial content measurement value indicating a degree to which an input image contains human face content is ascertained. A tone-correction process is tuned in accordance with the facial content measurement value. The input image is enhanced in accordance with the tuned tone-correction process.

Abstract: This invention provides a method for automated image enhancement. Attribute measurements are extracted from a digital image and used for the generation of at least a noise threshold parameter, a sharpness parameter and a radicality parameter. The noise threshold parameter and sharpness parameter are evaluated to determine the degree of noise reduction and the degree of sharpening to be performed, collectively a determined enhancement. The determined enhancement is applied to derive a nominally enhanced image. With respect to the radicality parameter, the output image is the weighted average between the initial image and the nominally enhanced image.

Abstract: A system and method for estimating compression image noise. Image data for an image is received. A compression ratio, an image size ratio, and an activity for the image are determined. Compression artifact estimation is determined based on the compression ratio, the image size ratio, and the activity.

Abstract: Noise level estimation includes examining color correlation in a digital image.

Abstract: Imaging systems, articles of manufacture, and imaging methods are described according to aspects of the disclosure. According to one embodiment, an imaging system includes processing circuitry configured to access image data of a plurality of pixels of an image, to provide a plurality of representations of the image data, to determine contrast difference between the representations of the image data for a respective one of the pixels, to identify the one of the pixels as being defective using the determined contrast difference, and to provide replacement image data for the one of the pixels identified as defective and which is different than the accessed image data for the one of the pixels identified as defective.

Abstract: Methods, machines, and computer-readable media storing machine-readable instructions for processing video frames are described. In one aspect, a respective set of three-dimensional forward transform coefficients is computed for each of multiple positions of a three-dimensional blocking grid relative to an input video block comprising a selected set of video frames. The sets of three-dimensional forward transform coefficients are processed. A respective three-dimensional inverse transform is computed from each set of processed forward transform coefficients. An output video block is generated based on the computed three-dimensional inverse transforms.

Abstract: Methods, machines, and computer-readable media for processing an input image with a bi-selective filter in a transform domain are described. In one aspect, a forward transform of an input image is computed. A bi-selective filter smoothes image regions with low magnitude coefficients and sharpens image regions with high magnitude coefficients. An output image is generated from the filtered forward transform.

Abstract: A system and method for estimating compression image noise. Image data for an image is received. A compression ratio, an image size ratio, and an activity for the image are determined. Compression artifact estimation is determined based on the compression ratio, the image size ratio, and the activity.

Abstract: A method for estimating image noise. Image data for at least one column of pixels and at least one row of pixels of the image is sampled. At least one filter is applied on sampled image data resulting in at least one sequence of filtered values. A feature set of pixels of the image is selected. Image noise estimation is determined based on at least one of the filtered values of the feature set of pixels.