Abstract: A video noise analyzer for detecting residual point noise in a video generates a list of candidate defective pixels by joining results of a temporal invariance detector and a spatial outlier detector. A data store is structured to contain data describing the candidate defective pixels and/or the blocks from which the pixels were selected. In other embodiments, a video noise analyzer includes a first salient analyzer structured to compare pixels within a block to a mean value of other pixels within the same block, a first threshold processor to generate a first pixel candidate list including pixels that differ significantly from their neighboring pixels, a second salient analyzer structured to generate a difference value between a subject pixel, its neighboring pixels, and neighbors of the neighboring pixels, and an identifier to label as a candidate dead pixel those pixels having a frequency occurrence of visual saliency greater than a predefined frequency threshold.

Abstract: A video noise analyzer for detecting residual point noise in a video generates a list of candidate defective pixels by joining results of a temporal invariance detector and a spatial outlier detector. A data store is structured to contain data describing the candidate defective pixels and/or the blocks from which the pixels were selected. In other embodiments, a video noise analyzer includes a first salient analyzer structured to compare pixels within a block to a mean value of other pixels within the same block, a first threshold processor to generate a first pixel candidate list including pixels that differ significantly from their neighboring pixels, a second salient analyzer structured to generate a difference value between a subject pixel, its neighboring pixels, and neighbors of the neighboring pixels, and an identifier to label as a candidate dead pixel those pixels having a frequency occurrence of visual saliency greater than a predefined frequency threshold.

Abstract: Methods for detecting structured artifacts in a video may begin by accepting a video frame at a video input and then generating a filtered frame from the video frame. After a differenced image is produced, groups of connected pixels (blobs) within the differenced frame are identified, and isolated blobs are removed to produce a cleaned image. A Fourier transform is performed on the cleaned image, and structured artifacts may be identified based on a comparison of magnitudes of the Fourier components of the cleaned image. Devices that detect and identify structured artifacts are also described.

Abstract: Methods for detecting structured artifacts in a video may begin by accepting a video frame at a video input and then generating a filtered frame from the video frame. After a differenced image is produced, groups of connected pixels (blobs) within the differenced frame are identified, and isolated blobs are removed to produce a cleaned image. A Fourier transform is performed on the cleaned image, and structured artifacts may be identified based on a comparison of magnitudes of the Fourier components of the cleaned image. Devices that detect and identify structured artifacts are also described.

Abstract: A unique test pattern is used for measuring timing parameters of RGBHV analog video signals. For horizontal timing a horizontal line includes within an active video region left and right portions at a first level and a central portion at a second level, the left, right and central portions having a defined percentage relationship to the number of pixels of the horizontal line within the active video region. Also optionally included are left and right border portions just prior to and just after the left and right portions respectively. Measurements are done in pixels for the horizontal line from a horizontal sync pulse to each transition of the unique test pattern, and the horizontal timing parameters are derived from such measurements.