Abstract: A system and a method for searching an image within another image are disclosed. The method includes producing template edge images and target edge images, having image scales, based on determination of edge gradients of a template image and a target image in one or more directions. The template image indicates an image to be searched. The target image indicates another image within which the image needs to be searched. Further, images comprising correlation coefficient values are produced for each of the directions by computing correlation coefficients between the template edge images and the target edge images. At least one local peak is identified from each of the images comprising the correlation coefficient values. Spatial locations along with the correlation coefficients corresponding to the local peak are determined. Thereafter, a presence of the template image in the target image is identified based upon an intersection of the spatial locations.

Abstract: A system and a method for adjusting luminance flashes in a video stream are disclosed. The method includes identifying a sequence of frames, having the luminance flashes, from the video stream. The sequence of frames is extended at both sides (start and end) based on a predefined threshold. The method further comprises dividing an extended sequence of frames into at least three segments. Further, the method includes determining a correction factor and a correction constant for each of the at least three segments. Thereafter, the method includes modifying luminance values of pixels of each of the at least three segments based on the correction factor and the correction constant, thereby adjusting the luminance flashes in the video stream.

Abstract: A system and a method for searching an image within another image are disclosed. The method includes producing template edge images and target edge images, having image scales, based on determination of edge gradients of a template image and a target image in one or more directions. The template image indicates an image to be searched. The target image indicates another image within which the image needs to be searched. Further, images comprising correlation coefficient values are produced for each of the directions by computing correlation coefficients between the template edge images and the target edge images. At least one local peak is identified from each of the images comprising the correlation coefficient values. Spatial locations along with the correlation coefficients corresponding to the local peak are determined. Thereafter, a presence of the template image in the target image is identified based upon an intersection of the spatial locations.

Abstract: A system and a method for adjusting luminance flashes in a video stream are disclosed. The method includes identifying a sequence of frames, having the luminance flashes, from the video stream. The sequence of frames is extended at both sides (start and end) based on a predefined threshold. The method further comprises dividing an extended sequence of frames into at least three segments. Further, the method includes determining a correction factor and a correction constant for each of the at least three segments. Thereafter, the method includes modifying luminance values of pixels of each of the at least three segments based on the correction factor and the correction constant, thereby adjusting the luminance flashes in the video stream.

Abstract: A system and a method for detecting presence of a living hold in a video stream are disclosed. The method comprises pre-processing N consecutive frames for eliminating noise. An edge score may be calculated for the N consecutive frames, and a clip threshold primary and a clip threshold secondary may be defined. The method further comprises identifying primary edges and secondary edges in the N consecutive frames. Successively, a percentage of fixed primary edges and percentage of fixed secondary edges may be determined. Presence of the living hold may be confirmed based on the value of the percentage of fixed primary edges and the percentage of fixed secondary edges.

Abstract: A method and system for detection of artifacts in a video after application of an error concealment strategy by a decoder is disclosed. An absolute difference image is determined by subtraction of a current image and a previously decoded image. A threshold marked buffer is determined to replace the pixel values of the absolute difference image with a first pixel value or a second pixel value, based on comparison of pixel values with a first predefined threshold. A candidate region is determined by determining a pair of edges of the threshold marked buffer having length above a second predefined threshold, distance between them above a third predefined threshold, and pixel values between them in the absolute difference image, less than a fourth predefined threshold. Validation of candidate region is based on comparison of characteristics of the candidate region with characteristics of the current image and/or previously decoded images.

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 detection of artifacts in a video after application of an error concealment strategy by a decoder is disclosed. An absolute difference image is determined by subtraction of a current image and a previously decoded image. A threshold marked buffer is determined to replace the pixel values of the absolute difference image with a first pixel value or a second pixel value, based on comparison of pixel values with a first predefined threshold. A candidate region is determined by determining a pair of edges of the threshold marked buffer having length above a second predefined threshold, distance between them above a third predefined threshold, and pixel values between them in the absolute difference image, less than a fourth predefined threshold. Validation of candidate region is based on comparison of characteristics of the candidate region with characteristics of the current image and/or previously decoded images.

Abstract: Systems and Methods for efficient and reliable detection of error blocks in a video based on detecting one or more candidate blocks in a region of interest and then verifying the block error on the basis of the patterns formed inside the candidate block and its distinction from the surrounding blocks spatially and/or temporally.

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.

Abstract: Methods and systems for detecting block based video dropouts in one or more fields associated with various video frames is provided. A current field is divided into a plurality of blocks. A set of activity blocks is identified from the plurality of blocks. The activity blocks are then processed to identify horizontal and vertical lines which are then further processed to form one or more candidate error blocks. The candidate error blocks are validated for start and end to determine a count of video dropout errors associated with the current field.

Abstract: System and method for identifying erroneous videos and assessing video quality is provided. Feature vectors are generated corresponding to a plurality of frames associated with the one or more videos. The feature vectors are subsequently subjected to anomaly detection to obtain first and second normalized path lengths and normalized anomaly measures. The first and second normalized path lengths and normalized anomaly measures are provided to a regression model to identify the erroneous video.

Abstract: Systems and Methods for efficient and reliable detection of error blocks in a video based on detecting one or more candidate blocks in a region of interest and then verifying the block error on the basis of the patterns formed inside the candidate block and its distinction from the surrounding blocks spatially and/or temporally.

Abstract: A system and method for detecting artifacts in a video having a sequence of pictures is described. The method comprises the steps of calculating a motion vector cost and a variable threshold for each of the pictures. In case, the motion vector cost for the picture is greater than the variable threshold of the picture, then a first scene change profile and a second scene change profile for the picture is computed and analyzed for determining the character of the variation in the motion vector cost. An artifact metric for the picture is also calculated and the artifact metric for the picture is compared with a programmable artifact reporting threshold for ascertaining if an artifact is present in the video. The described approach is generic in nature and takes into account various exceptions that may be present within a picture that may be similar in character as an artifact.

Abstract: Systems and methods for detecting defective pixels in a video sequence having a plurality of frames, the method comprising determining one or more frames of a single scene and determining a plurality of buffers of each frame related to the single scene, detecting one or more candidate pixels for a first predefined number of frames, tracking the detected one or more candidate pixels for a second predetermined number of frames, validating the one or more candidate pixels for presence of defect and marking a candidate pixel from the one or more candidate pixels as a defective pixel based on one or more predefined conditions.

Abstract: System and method for identifying erroneous videos and assessing video quality is provided. Feature vectors are generated corresponding to a plurality of frames associated with the one or more videos. The feature vectors are subsequently subjected to anomaly detection to obtain first and second normalized path lengths and normalized anomaly measures. The first and second normalized path lengths and normalized anomaly measures are provided to a regression model to identify the erroneous video.

Abstract: Methods and systems for detecting block based video dropouts in one or more fields associated with various video frames is provided. A current field is divided into a plurality of blocks. A set of activity blocks is identified from the plurality of blocks. The activity blocks are then processed to identify horizontal and vertical lines which are then further processed to form one or more candidate error blocks. The candidate error blocks are validated for start and end to determine a count of video dropout errors associated with the current field.

Abstract: Systems and methods for detecting chroma dropout errors in one or more fields associated with various video frames are provided. Pixels associated with a current field are divided into a set of pixel pairs. Co-occurrences matrices are calculated for previous and subsequent fields. A first pixel pair associated with the current field is selected. First and second set of entries are selected from the co-occurrence matrices corresponding to the previous and subsequent fields. The first pixel pair is searched in the first and second set of entries. An absence of the first pixel pair in the first and second set of entries satisfies a first criterion of chroma dropout error. Other criteria in addition to the first criterion are evaluated to label the first pixel pair as erroneous.

Abstract: A system and method for the identification and analysis of cadence pattern is disclosed. The method uses previous and current fields to generate the difference between the field values. The difference of these values along with the field relations is passed to the state machine to generate the state of the top and bottom fields. Based on the top and bottom state the cadence signature is generated and by using the Fourier analysis the principle frequency of repeated cadence pattern signature sequence is identified. Each of the cadence signatures present in the cadence pattern signature sequence is decoded to calculate the pull-down value of the cadence pattern. The pull down value then gives the actual cadence pattern.