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公开(公告)号：US11551060B2
公开(公告)日：2023-01-10
申请号：US16677161
申请日：2019-11-07
Applicant: Netflix, Inc.
Inventor： Dong Liu , Nagendra Kamath , Rohit Puri , Subhabrata Bhattacharya
Abstract: The disclosed computer-implemented method may include generating a three-dimensional (3D) feature map for a digital image using a fully convolutional network (FCN). The 3D feature map may be configured to identify features of the digital image and identify an image region for each identified feature. The method may also include generating a region composition graph that includes the identified features and image regions. The region composition graph may be configured to model mutual dependencies between features of the 3D feature map. The method may further include performing a graph convolution on the region composition graph to determine a feature aesthetic value for each node according to the weightings in the node's weighted connecting segments, and calculating a weighted average for each node's feature aesthetic value to provide a combined level of aesthetic appeal for the digital image. Various other methods, systems, and computer-readable media are also disclosed.
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公开(公告)号：US11107206B2
公开(公告)日：2021-08-31
申请号：US16143386
申请日：2018-09-26
Applicant: NETFLIX, INC.
Inventor： Subhabrata Bhattacharya , Adithya Prakash , Rohit Puri
Abstract: In various embodiments, a defective pixel detection application automatically detects defective pixels in video content. In operation, the defective pixel detection application computes a first set of pixel intensity gradients based on a first frame of video content and a first neighborhood of pixels associated with a first pixel. The defective pixel detection application also computes a second set of pixel intensity gradients based on the first frame and a second neighborhood of pixels associated with the first pixel. Subsequently, the defective pixel detection application computes a statistical distance between the first set of pixel intensity gradients and the second set of pixel intensity gradients. The defective pixel detection application then determines that the first pixel is defective based on the statistical distance.
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公开(公告)号：US20200151546A1
公开(公告)日：2020-05-14
申请号：US16677161
申请日：2019-11-07
Applicant: Netflix, Inc.
Inventor： Dong Liu , Nagendra Kamath , Rohit Puri , Subhabrata Bhattacharya
Abstract: The disclosed computer-implemented method may include generating a three-dimensional (3D) feature map for a digital image using a fully convolutional network (FCN). The 3D feature map may be configured to identify features of the digital image and identify an image region for each identified feature. The method may also include generating a region composition graph that includes the identified features and image regions. The region composition graph may be configured to model mutual dependencies between features of the 3D feature map. The method may further include performing a graph convolution on the region composition graph to determine a feature aesthetic value for each node according to the weightings in the node's weighted connecting segments, and calculating a weighted average for each node's feature aesthetic value to provide a combined level of aesthetic appeal for the digital image. Various other methods, systems, and computer-readable media are also disclosed.
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公开(公告)号：US20190114761A1
公开(公告)日：2019-04-18
申请号：US16143386
申请日：2018-09-26
Applicant: NETFLIX, INC.
Inventor： Subhabrata Bhattacharya , Adithya Prakash , Rohit Puri
Abstract: In various embodiments, a defective pixel detection application automatically detects defective pixels in video content. In operation, the defective pixel detection application computes a first set of pixel intensity gradients based on a first frame of video content and a first neighborhood of pixels associated with a first pixel. The defective pixel detection application also computes a second set of pixel intensity gradients based on the first frame and a second neighborhood of pixels associated with the first pixel. Subsequently, the defective pixel detection application computes a statistical distance between the first set of pixel intensity gradients and the second set of pixel intensity gradients. The defective pixel detection application then determines that the first pixel is defective based on the statistical distance.
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