公开(公告)号：US20230186435A1

公开(公告)日：2023-06-15

申请号：US17551087

申请日：2021-12-14

Applicant: NETFLIX, INC.

Inventor： Christos G. BAMPIS ,  Li-Heng CHEN ,  Aditya MAVLANKAR ,  Anush MOORTHY

IPC: G06T5/00 ,  G06T7/90 ,  G06T3/40

CPC classification number: G06T5/002 ,  G06T3/40 ,  G06T7/90 ,  G06T2207/10024 ,  G06T2207/20081

Abstract: In various embodiments, an image preprocessing application preprocesses images. To preprocess an image, the image preprocessing application executes a trained machine learning model on first data corresponding to both the image and a first set of components of a luma-chroma color space to generate first preprocessed data. The image preprocessing application executes at least a different trained machine learning model or a non-machine learning algorithm on second data corresponding to both the image and a second set of components of the luma-chroma color space to generate second preprocessed data. Subsequently, the image preprocessing application aggregates at least the first preprocessed data and the second preprocessed data to generate a preprocessed image.

公开(公告)号：US20240119575A1

公开(公告)日：2024-04-11

申请号：US17937024

申请日：2022-09-30

Applicant: NETFLIX, INC.

Inventor： Christos G. BAMPIS ,  Zhi LI

IPC: G06T7/00 ,  G06N20/10

CPC classification number: G06T7/0002 ,  G06N20/10 ,  G06T2207/10016

Abstract: In various embodiments, a training application generates a trained perceptual quality model that estimates perceived video quality for reconstructed video. The training application computes a pixels-per-degree value based on a normalized viewing distance and a display resolution. The training application computes a set of feature values corresponding to a set of visual quality metrics based on a reconstructed video sequence, a source video sequence, and the pixels-per-degree value. The training application executes a machine learning algorithm on the first set of feature values to generate the trained perceptual quality model. The trained perceptual quality model maps a particular set of feature values corresponding to the set of visual quality metrics to a particular perceptual quality score.

公开(公告)号：US20230143389A1

公开(公告)日：2023-05-11

申请号：US17981292

申请日：2022-11-04

Applicant: NETFLIX, INC.

Inventor： Christos G. BAMPIS ,  Zhi LI

IPC: H04N19/436 ,  H04N19/30

CPC classification number: H04N19/436 ,  H04N19/30

Abstract: In various embodiments an endpoint application reconstructs downscaled videos. The endpoint application accesses metadata associated with a portion of a downscaled video that has a first resolution and was generated using a trained downscaling convolutional neural network (CNN). The endpoint application determines, based on the metadata, an upscaler that should be used when upscaling the portion of the downscaled video. The endpoint application executes the upscaler on the portion of the downscaled video to generate a portion of a reconstructed video that is accessible for playback and has a second resolution that is greater than the first resolution.

公开(公告)号：US20240233076A1

公开(公告)日：2024-07-11

申请号：US18617162

申请日：2024-03-26

Applicant: NETFLIX, INC.

Inventor： Li-Heng CHEN ,  Christos G. BAMPIS ,  Zhi LI

IPC: G06T3/4046 ,  G06N3/084 ,  G06T9/00

CPC classification number: G06T3/4046 ,  G06N3/084 ,  G06T9/002

Abstract: In various embodiments, a training application trains a convolutional neural network to downsample images in a video encoding pipeline. The convolution neural network includes at least two residual blocks and is associated with a downsampling factor. The training application executes the convolutional neural network on a source image to generate a downsampled image. The training application then executes an upsampling algorithm on the downsampled image to generate a reconstructed image having the same resolution as the source image. The training application computes a reconstruction error based on the reconstructed image and the source image. The training application updates at least one parameter of the convolutional neural network based on the reconstruction error to generate a trained convolutional neural network. Advantageously, the trained convolution neural network can be implemented in a video encoding pipeline to mitigate visual quality reductions typically experienced with conventional video encoding pipelines that implement conventional downsampling techniques.

公开(公告)号：US20220198607A1

公开(公告)日：2022-06-23

申请号：US17133206

申请日：2020-12-23

Applicant: NETFLIX, INC.

Inventor： Li-Heng CHEN ,  Christos G. BAMPIS ,  Zhi LI

IPC: G06T3/40 ,  G06T9/00 ,  G06N3/08

Abstract: In various embodiments, a training application trains a convolutional neural network to downsample images in a video encoding pipeline. The convolution neural network includes at least two residual blocks and is associated with a downsampling factor. The training application executes the convolutional neural network on a source image to generate a downsampled image. The training application then executes an upsampling algorithm on the downsampled image to generate a reconstructed image having the same resolution as the source image. The training application computes a reconstruction error based on the reconstructed image and the source image. The training application updates at least one parameter of the convolutional neural network based on the reconstruction error to generate a trained convolutional neural network. Advantageously, the trained convolution neural network can be implemented in a video encoding pipeline to mitigate visual quality reductions typically experienced with conventional video encoding pipelines that implement conventional downsampling techniques.

公开(公告)号：US20240121402A1

公开(公告)日：2024-04-11

申请号：US17937033

申请日：2022-09-30

Applicant: NETFLIX, INC.

Inventor： Christos G. BAMPIS ,  Zhi LI

IPC: H04N19/154 ,  H04N19/136 ,  H04N19/172 ,  H04N19/182 ,  H04N19/184 ,  H04N19/42

CPC classification number: H04N19/154 ,  H04N19/136 ,  H04N19/172 ,  H04N19/182 ,  H04N19/184 ,  H04N19/42

Abstract: In various embodiments, a quality inference application estimates perceived video quality for reconstructed video. The quality inference application computes a set of feature values corresponding to a set of visual quality metrics based on a reconstructed frame, a source frame, a display resolution, and a normalized viewing distance. The quality inference application executes a trained perceptual quality model on the set of feature values to generate a perceptual quality score that indicates a perceived visual quality level for the reconstructed frame. The quality inference application performs one or more operations associated with an encoding process based on the perceptual quality score.

公开(公告)号：US20230144735A1

公开(公告)日：2023-05-11

申请号：US17981281

申请日：2022-11-04

Applicant: NETFLIX, INC.

Inventor： Christos G. BAMPIS ,  Zhi LI

IPC: G06T3/40

CPC classification number: G06T3/4046

Abstract: In various embodiments a training application trains convolutional neural networks (CNNs) to reduce reconstruction errors. The training application executes a first CNN on a source image having a first resolution to generate a downscaled image having a second resolution. The training application executes a second CNN on the downscaled image to generate a reconstructed image having the first resolution. The training application computes a reconstruction error based on the reconstructed image and the source image. The training application updates a first learnable parameter value included in the first CNN based on the reconstruction error to generate at least a partially trained downscaling CNN. The training application updates a second learnable parameter included in the second CNN based on the reconstruction error to generate at least a partially trained upscaling CNN.