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公开(公告)号：US20240187618A1
公开(公告)日：2024-06-06
申请号：US18440013
申请日：2024-02-13
Applicant: GOOGLE LLC
Inventor： Sam John , Balineedu Adsumilli , Akshay Gadde
IPC: H04N19/40 , H04N19/119 , H04N19/147 , H04N19/184 , H04N19/192
CPC classification number: H04N19/40 , H04N19/119 , H04N19/147 , H04N19/184 , H04N19/192
Abstract: A learning model is trained for rate-distortion behavior prediction against a corpus of a video hosting platform and used to determine optimal bitrate allocations for video data given video content complexity across the corpus of the video hosting platform. Complexity features of the video data are processed using the learning model to determine a rate-distortion cluster prediction for the video data, and transcoding parameters for transcoding the video data are selected based on that prediction. The rate-distortion clusters are modeled during the training of the learning model, such as based on rate-distortion curves of video data of the corpus of the video hosting platform and based on classifications of such video data. This approach minimizes total corpus egress and/or storage while further maintaining uniformity in the delivered quality of videos by the video hosting platform.
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Patent Agency Ranking
公开(公告)号：US11924449B2
公开(公告)日：2024-03-05
申请号：US17908352
申请日：2020-05-19
Applicant: Google LLC
Inventor： Sam John , Balineedu Adsumilli , Akshay Gadde
IPC: H04N19/40 , H04N19/119 , H04N19/147 , H04N19/184 , H04N19/192
CPC classification number: H04N19/40 , H04N19/119 , H04N19/147 , H04N19/184 , H04N19/192
Abstract: A learning model is trained for rate-distortion behavior prediction against a corpus of a video hosting platform and used to determine optimal bitrate allocations for video data given video content complexity across the corpus of the video hosting platform. Complexity features of the video data are processed using the learning model to determine a rate-distortion cluster prediction for the video data, and transcoding parameters for transcoding the video data are selected based on that prediction. The rate-distortion clusters are modeled during the training of the learning model, such as based on rate-distortion curves of video data of the corpus of the video hosting platform and based on classifications of such video data. This approach minimizes total corpus egress and/or storage while further maintaining uniformity in the delivered quality of videos by the video hosting platform.
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公开(公告)号：US20230068026A1
公开(公告)日：2023-03-02
申请号：US17462591
申请日：2021-08-31
Applicant: Google LLC
Inventor： Ching Yin Derek Pang , Kyrah Felder , Akshay Gadde , Paul Wilkins , Cheng Chen , Yao-Chung Lin
Abstract: Methods and systems for encoder parameter setting optimization. A media item to be provided to one or more users of a platform is identified. The media item is associated with a media class. An indication of the identified media item is provided as input to a first machine learning model. The first machine learning model is trained to predict, for a given media item, a set of encoder parameter settings that satisfy a performance criterion in view of a respective media class associated with the given media item. One or more outputs of the first machine learning model are obtained. The one or more obtained outputs include encoder data identifying one or more sets of encoder parameter settings and, for each of the sets of encoder parameter settings, an indication of a level of confidence that a respective set of encoder parameter settings satisfies the performance criterion in view of the media class associated with the identified media item. The identified media item is encoded using the respective set of encoding parameter settings associated with the level of confidence that satisfies a confidence criterion.
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公开(公告)号：US20230101806A1
公开(公告)日：2023-03-30
申请号：US17908352
申请日：2020-05-19
Applicant: Google LLC
Inventor： Sam John , Balineedu Adsumilli , Akshay Gadde
IPC: H04N19/40 , H04N19/184 , H04N19/119 , H04N19/192 , H04N19/147
Abstract: A learning model is trained for rate-distortion behavior prediction against a corpus of a video hosting platform and used to determine optimal bitrate allocations for video data given video content complexity across the corpus of the video hosting platform. Complexity features of the video data are processed using the learning model to determine a rate-distortion cluster prediction for the video data, and transcoding parameters for transcoding the video data are selected based on that prediction. The rate-distortion clusters are modeled during the training of the learning model, such as based on rate-distortion curves of video data of the corpus of the video hosting platform and based on classifications of such video data. This approach minimizes total corpus egress and/or storage while further maintaining uniformity in the delivered quality of videos by the video hosting platform.
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公开(公告)号：US20240137400A1
公开(公告)日：2024-04-25
申请号：US18403659
申请日：2024-01-03
Applicant: Google LLC
Inventor： Ching Yin Derek Pang , Kyrah Felder , Akshay Gadde , Paul Wilkins , Cheng Chen , Yao-Chung Lin
CPC classification number: H04L65/70 , G06N20/00 , H04L65/61 , H04L65/80 , H04N21/251
Abstract: A media item to be provided to users of a platform is identified. The media item is associated with a media class of one or more media classes. An indication of the media item is provided as input to a machine learning model trained based on historical encoding data to predict, for a given media item, a set of encoder parameter settings that satisfy a performance criterion in view of a respective media class of the given media item. The historical encoding data includes a prior set of encoder parameter settings that satisfied the performance criterion with respect to a prior media item associated with the respective class. Encoder parameter settings that satisfy the performance criterion in view of the media class is determined based on an output of the model. The media item is caused to be encoded using the determined encoder parameter settings.
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公开(公告)号：US11870833B2
公开(公告)日：2024-01-09
申请号：US17462591
申请日：2021-08-31
Applicant: Google LLC
Inventor： Ching Yin Derek Pang , Kyrah Felder , Akshay Gadde , Paul Wilkins , Cheng Chen , Yao-Chung Lin
CPC classification number: H04L65/70 , G06N20/00 , H04L65/61 , H04L65/80 , H04N21/251
Abstract: Methods and systems for encoder parameter setting optimization. A media item to be provided to one or more users of a platform is identified. The media item is associated with a media class. An indication of the identified media item is provided as input to a first machine learning model. The first machine learning model is trained to predict, for a given media item, a set of encoder parameter settings that satisfy a performance criterion in view of a respective media class associated with the given media item. One or more outputs of the first machine learning model are obtained. The one or more obtained outputs include encoder data identifying one or more sets of encoder parameter settings and, for each of the sets of encoder parameter settings, an indication of a level of confidence that a respective set of encoder parameter settings satisfies the performance criterion in view of the media class associated with the identified media item. The identified media item is encoded using the respective set of encoding parameter settings associated with the level of confidence that satisfies a confidence criterion.
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