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.