公开(公告)号：US12167000B2

公开(公告)日：2024-12-10

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

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：US11750821B2

公开(公告)日：2023-09-05

申请号：US17174121

申请日：2021-02-11

Applicant: NETFLIX, INC.

Inventor： Anush Moorthy ,  Zhi Li ,  Liwei Guo ,  Aditya Mavlankar ,  Anne Aaron

IPC: H04N19/146 ,  H04N19/154 ,  H04N19/184 ,  H04N19/30

CPC classification number: H04N19/146 ,  H04N19/154 ,  H04N19/184 ,  H04N19/30

Abstract: In various embodiments, an encoding ladder application generates encoding ladders for encoding media titles. In operation, the encoding ladder application generates a first convex hull representing encoding tradeoffs between quality and bitrate when encoding a media title at a first resolution; The encoding ladder application generates a second convex hull representing encoding tradeoffs between quality and bitrate when encoding the media title at a second resolution. Based on the first convex hull and the second convex hull, the encoding ladder application generates an overall convex hull. Subsequently, the encoding ladder application generates an encoding ladder for the media title based on at least the overall convex hull and a ladder requirement. Advantageously, the tradeoffs between quality and bitrate represented by the encoding ladder are customized for the media title. Consequently, encoding inefficiencies attributable to conventional fixed-bitrate ladders can be reduced.

公开(公告)号：US11361416B2

公开(公告)日：2022-06-14

申请号：US16352757

申请日：2019-03-13

Applicant: NETFLIX, INC.

Inventor： Christos Bampis ,  Zhi Li ,  Lavanya Sharan ,  Julie Novak ,  Martin Tingley

IPC: H04N19/154 ,  G06T7/00 ,  G06K9/62 ,  G06N20/20 ,  H04N21/25 ,  H04N19/147 ,  H04N17/00

Abstract: In various embodiments, an encoding metric comparison application computes a first set of quality scores associated with a test encoding configuration based on a set of bootstrap quality models. Each bootstrap quality model is trained based on a different subset of a training database. The encoding metric comparison application computes a second set of quality scores associated with a reference encoding configuration based on the set of bootstrap quality models. Subsequently, the encoding metric comparison application generates a distribution of bootstrap values for an encoding comparison metric based on the first set of quality scores and the second set of quality scores. The distribution quantifies an accuracy of a baseline value for the encoding comparison metric generated by a baseline quality model.

公开(公告)号：US10887602B2

公开(公告)日：2021-01-05

申请号：US15890709

申请日：2018-02-07

Applicant: NETFLIX, INC.

Inventor： Zhi Li ,  Christos Bampis

IPC: H04N19/154 ,  H04N19/91 ,  H04N21/235 ,  G06N3/08 ,  G06K9/62 ,  G06N3/04 ,  G06N20/20 ,  G06N20/10 ,  G06N5/00 ,  H04N17/00

Abstract: In various embodiments, a prediction application computes a quality score for re-constructed visual content that is derived from visual content. The prediction application generates a frame difference matrix based on two frames included in the re-constructed video content. The prediction application then generates a first entropy matrix based on the frame difference matrix and a first scale. Subsequently, the prediction application computes a first value for a first temporal feature based on the first entropy matrix and a second entropy matrix associated with both the visual content and the first scale. The prediction application computes a quality score for the re-constructed video content based on the first value, a second value for a second temporal feature associated with a second scale, and a machine learning model that is trained using subjective quality scores. The quality score indicates a level of visual quality associated with streamed video content.

公开(公告)号：US11948271B2

公开(公告)日：2024-04-02

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 ,  G06N3/084 ,  G06T3/4046 ,  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：US11729396B2

公开(公告)日：2023-08-15

申请号：US17141075

申请日：2021-01-04

Applicant: NETFLIX, INC.

Inventor： Zhi Li ,  Christos Bampis

IPC: H04N19/154 ,  H04N19/91 ,  H04N21/235 ,  G06N3/08 ,  G06N20/20 ,  G06N20/10 ,  H04N17/00 ,  G06F18/2411 ,  G06N3/045 ,  G06N5/01

CPC classification number: H04N19/154 ,  G06F18/2411 ,  G06N3/045 ,  G06N3/08 ,  G06N5/01 ,  G06N20/10 ,  G06N20/20 ,  H04N17/004 ,  H04N19/91 ,  H04N21/235

Abstract: In various embodiments, a prediction application computes a quality score for re-constructed visual content that is derived from visual content. The prediction application generates a frame difference matrix based on two frames included in the re-constructed video content. The prediction application then generates a first entropy matrix based on the frame difference matrix and a first scale. Subsequently, the prediction application computes a first value for a first temporal feature based on the first entropy matrix and a second entropy matrix associated with both the visual content and the first scale. The prediction application computes a quality score for the re-constructed video content based on the first value, a second value for a second temporal feature associated with a second scale, and a machine learning model that is trained using subjective quality scores. The quality score indicates a level of visual quality associated with streamed video content.

公开(公告)号：US11700383B2

公开(公告)日：2023-07-11

申请号：US17141081

申请日：2021-01-04

Applicant: NETFLIX, INC.

Inventor： Zhi Li ,  Christos Bampis

IPC: H04N19/154 ,  H04N19/91 ,  H04N21/235 ,  G06N3/08 ,  G06N20/20 ,  G06N20/10 ,  H04N17/00 ,  G06F18/2411 ,  G06N3/045 ,  G06N5/01

CPC classification number: H04N19/154 ,  G06F18/2411 ,  G06N3/045 ,  G06N3/08 ,  G06N5/01 ,  G06N20/10 ,  G06N20/20 ,  H04N17/004 ,  H04N19/91 ,  H04N21/235

Abstract: In various embodiments, a prediction application computes a quality score for re-constructed visual content that is derived from visual content. The prediction application generates a frame difference matrix based on two frames included in the re-constructed video content. The prediction application then generates a first entropy matrix based on the frame difference matrix and a first scale. Subsequently, the prediction application computes a first value for a first temporal feature based on the first entropy matrix and a second entropy matrix associated with both the visual content and the first scale. The prediction application computes a quality score for the re-constructed video content based on the first value, a second value for a second temporal feature associated with a second scale, and a machine learning model that is trained using subjective quality scores. The quality score indicates a level of visual quality associated with streamed video content.

公开(公告)号：US11557025B2

公开(公告)日：2023-01-17

申请号：US16995677

申请日：2020-08-17

Applicant: NETFLIX, INC.

Inventor： Li-Heng Chen ,  Christos G. Bampis ,  Zhi Li

IPC: G06T7/00 ,  G06T7/90

Abstract: In various embodiments, a training application generates a perceptual video model. The training application computes a first feature value for a first feature included in a feature vector based on a first color component associated with a first reconstructed training video. The training application also computes a second feature value for a second feature included in the feature vector based on a first brightness component associated with the first reconstructed training video. Subsequently, the training application performs one or more machine learning operations based on the first feature value, the second feature value, and a first subjective quality score for the first reconstructed training video to generate a trained perceptual quality model. The trained perceptual quality model maps a feature value vector for the feature vector to a perceptual quality score.

公开(公告)号：US11503304B2

公开(公告)日：2022-11-15

申请号：US17093456

申请日：2020-11-09

Applicant: NETFLIX, INC.

Inventor： Zhi Li ,  Anne Aaron ,  Anush Moorthy ,  Christos Bampis

IPC: H04N19/154 ,  H04N19/59 ,  H04N21/647 ,  H04N19/146 ,  H04N21/234 ,  H04N21/2343 ,  H04N17/00 ,  H04N19/00 ,  H04N19/593

Abstract: In various embodiments, a perceptual quality application computes an absolute quality score for encoded video content. In operation, the perceptual quality application selects a model based on the spatial resolution of the video content from which the encoded video content is derived. The model associates a set of objective values for a set of objective quality metrics with an absolute quality score. The perceptual quality application determines a set of target objective values for the objective quality metrics based on the encoded video content. Subsequently, the perceptual quality application computes the absolute quality score for the encoded video content based on the selected model and the set of target objective values. Because the absolute quality score is independent of the quality of the video content, the absolute quality score accurately reflects the perceived quality of a wide range of encoded video content when decoded and viewed.

公开(公告)号：US11284140B2

公开(公告)日：2022-03-22

申请号：US16401066

申请日：2019-05-01

Applicant: NETFLIX, INC.

Inventor： Christos Bampis ,  Zhi Li

IPC: H04N21/24 ,  H04N21/25 ,  H04N21/658 ,  H04N21/4425 ,  H04N21/6547 ,  H04N21/475

Abstract: In various embodiments, a quality of experience (QoE) prediction application computes a visual quality score associated with a stream of encoded video content. The QoE prediction application also determines a rebuffering duration associated with the stream of encoded video content. Subsequently, the QoE prediction application computes an overall QoE score associated with the stream of encoded video content based on the visual quality score, the rebuffering duration, and an exponential QoE model. The exponential QoE model is generated using a plurality of subjective QoE scores and a linear regression model. The overall QoE score indicates a quality level of a user experience when viewing reconstructed video content derived from the stream of encoded video content.