公开(公告)号：US12206914B2

公开(公告)日：2025-01-21

申请号：US18021636

申请日：2022-06-08

Applicant: Google LLC

Inventor： Yilin Wang ,  Balineedu Adsumilli ,  Junjie Ke ,  Hossein Talebi ,  Joong Yim ,  Neil Birkbeck ,  Peyman Milanfar ,  Feng Yang

IPC: H04N21/266 ,  G06N3/045 ,  H04N17/02 ,  H04N19/154 ,  H04N21/234 ,  H04N21/434 ,  H04N21/44 ,  H04N21/466

Abstract: Methods, systems, and media for determining perceptual quality indicators of video content items are provided. In some embodiments, the method comprises: receiving a video content item; extracting a plurality of frames from the video content item; determining, using a first subnetwork of a deep neural network, a content quality indicator for each frame of the plurality of frames of the video content item; determining, using a second subnetwork of the deep neural network, a video distortion indicator for each frame of the plurality of frames of the video content item; determining, using a third subnetwork of the deep neural network, a compression sensitivity indicator for each frame of the plurality of frames of the video content item; generating a quality level for each frame of the plurality of frames of the video content item that concatenates the content quality indicator, the video distortion indicator, and the compression sensitivity indicator for that frame of the video content item; generating an overall quality level for video content item by aggregating the quality level of each frame of the plurality of frames; and causing a video recommendation to be presented based on the overall quality level of the video content item.

公开(公告)号：US20230319327A1

公开(公告)日：2023-10-05

申请号：US18021636

申请日：2022-06-08

Applicant: Google LLC

Inventor： Yilin Wang ,  Balineedu Adsumilli ,  Junjie Ke ,  Hossein Talebi ,  Joong Yim ,  Neil Birkbeck ,  Peyman Milanfar ,  Feng Yang

IPC: H04N21/234 ,  H04N19/154 ,  H04N21/466

CPC classification number: H04N21/23418 ,  H04N19/154 ,  H04N21/4668

Abstract: Methods, systems, and media for determining perceptual quality indicators of video content items are provided. In some embodiments, the method comprises: receiving a video content item; extracting a plurality of frames from the video content item; determining, using a first subnetwork of a deep neural network, a content quality indicator for each frame of the plurality of frames of the video content item; determining, using a second subnetwork of the deep neural network, a video distortion indicator for each frame of the plurality of frames of the video content item; determining, using a third subnetwork of the deep neural network, a compression sensitivity indicator for each frame of the plurality of frames of the video content item; generating a quality level for each frame of the plurality of frames of the video content item that concatenates the content quality indicator, the video distortion indicator, and the compression sensitivity indicator for that frame of the video content item; generating an overall quality level for video content item by aggregating the quality level of each frame of the plurality of frames; and causing a video recommendation to be presented based on the overall quality level of the video content item.