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公开(公告)号：US20190258902A1
公开(公告)日：2019-08-22
申请号：US16216699
申请日：2018-12-11
摘要： The disclosed technology teaches training a NR VMOS score generator by generating synthetically impaired images from FR video using filters tuned to generate impaired versions and applying a FR VMOS generator to pairs of unimpaired FR images from the FR video and the impaired versions of the FR images to create ground truth scores for the impaired versions. The disclosed method also includes training by machine learning model an image evaluation classifier using the ground truth scores and the impaired versions to generate NR VMOS scores, and storing coefficients of the image evaluation classifier for use as the NR VMOS score generator. Also disclosed is generating a NR VMOS score by invoking the trained NR VMOS score generator, with stored coefficients generated by feeding the trained NR VMOS score generator with images captured from scenes in a video to be scored, and evaluating the images to generate NR VMOS scores.
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2.发明申请公开(公告)号：US20220368995A1
公开(公告)日：2022-11-17
申请号：US17878813
申请日：2022-08-01
IPC分类号： H04N21/647 , H04N21/234
摘要： At least three uses of the technology disclosed are immediately recognized. First, a video stream classifier can be trained that has multiple uses. Second, a trained video stream classifier can be applied to monitor a live network. It can be extended by the network provider to customer relations management or to controlling video bandwidth. Third, a trained video stream classifier can be used to infer bit rate switching of codecs used by video sources and content providers. Bit rate switching and resulting video quality scores can be used to balance network loads and to balance quality of experience for users, across video sources. Balancing based on bit rate switching and resulting video quality scores also can be used when resolving network contention.
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