Abstract: A method and system detection of inherent noise present within a video source prior to digital video compression is disclosed. A noise image is extracted by subtracting a current image from its filtered version. Each pixel of the extracted noise image is normalized based on a determined principal edge image and the analog noise pixels are accumulated to generate an intermediate noise confidence value. Analog noise may be detected based on an analog noise confidence value generated based on the intermediate noise confidence value and a ringing metric, a blockiness metric, a motion vector cost of the current image, a blurriness exception weight, a flashiness exception weight, and a pan blur exception weight. The method may further comprise detection of high frequency noise based on determining a high frequency noise confidence value that may be based on a high frequency noise value and a frequency component with highest magnitude.

Abstract: A method and system for ghost detection in an image are described. Initially, a mode for detecting ghosting artifact is determined for deciding whether to perform analog or inter field ghosting analysis on the image. Based on the determined mode, a plurality of fields is determined to generate a field overlay image. The image is updated based on the field overlay image and an inter-field average of absolute difference is computed. A significant edge image, a principal edge image, and a delta gradient image are generated. A confidence score indicative of a likelihood of ghosting artifact in the image, is computed based on at least the inter-field average of absolute difference, a first and a second count of pixels in the delta gradient image, a third count of pixels in the principal edge image, and a count of rows of the significant edge image that correspond to the ghosting artifact.

Abstract: A method and system for determining blur in digital video due to high quantization adopted during encoding of an image. The image is preprocessed to filter the noise and an edge image is extracted from the preprocessed image. A gradient sign change is determined for each pixel of the edge image. A ringing metric is determined based on the gradient magnitude and the gradient sign change. Further, a blockiness metric is determined based on a block grid periodicity and offset that is determined based on block grid analysis of the edge image. Further, a perceptual blurriness metric is determined using a mean and kurtosis computed based on a gradient histogram analysis of the block grid analysed edge image. A composite blurring metric is determined based on the blockiness metric, the perceptual blurriness metric, and ringing metric.