Arris Enterprises LLC

Olga Malysheva

Pierre A Brice

Gautam Babbar

Daniel Hillegass

Wayne D Woodruff

Robert Begley

Dan Hillegass

Exemplary embodiments are directed to methods and systems for annotating generating training data for video quality impairment detection. A video stream recorder extracts one or more image frames from a reference video stream as it is played. A video image labeler embeds a unique label into each of the one or more extracted image frames. The video stream recorder records the one or more labeled image frames as a labeled video stream. The video stream player then plays the labeled video stream through an impaired communication channel to generate a degraded video stream. A video image comparator compares one or more corresponding frames of the labeled video stream and the degraded video stream to generate one or more difference frames. An impaired image recorder annotates at least one of the one or more difference frames according to a corrupted region of the at least one difference frame.

Automatic annotation of video quality impairment training data for generating machine learning models of video quality prediction

The automatic video comparison system for measuring the quality of decoded data described herein provides a method for measuring the quality of decoded data at the level of sub-units of a unit of data, for instance at the level of sub-blocks of a video frame. The system can therefore locate defects that may not otherwise be detected by an automated system that measures quality at the level of the entire frame. Processing encoded media is computationally intensive, thus the automatic video comparison system uses a distributed computing system in order to distribute the computations across many compute resources that are capable of operating in parallel.

Automatic video comparison of the output of a video decoder

A computer implemented method for hashing an image is provided that includes: receiving a reference image frame; deriving an array of reference hash values to represent the reference image frame, wherein the array of reference hash values includes a first reference hash value, a second reference hash value, and a third reference hash value; receiving an test image frame; deriving an array of hash values to represent the test image frame, wherein the array of hash values includes a first test hash value, a second test hash value, and a third test hash value; subtracting the array of test hash values representing the test image frame from the array of reference hash values representing the reference image frame to produce a delta array, wherein the delta array is indicative of an area where the test image frame differs from the reference image frame.

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Lansdale, PA, United States of America

Olga Malysheva