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：US20220383348A1

公开(公告)日：2022-12-01

申请号：US17840544

申请日：2022-06-14

Applicant: NETFLIX, INC.

Inventor： Zhi LI

IPC: G06Q30/02

Abstract: In various embodiments, a data optimization application mitigates scoring inaccuracies in subjective quality experiments. In operation, the data optimization application generates a model that includes a first set of individual scores and a first set of parameters. The first set of parameters includes a first subjective score set and a first set of subjective factor sets. The data optimization application performs one or more optimization operations on the first set of parameters to generate a second set of parameters. The second set of parameters includes a second subjective score set and a second set of subjective factor sets, wherein the second subjective score set compensates for at least a first subjective factor set included in the second set of subjective factor sets. The data optimization application also computes a participant evaluation report based on at least a second subjective factor sets included in the second set of subjective factor sets

公开(公告)号：US20220256168A1

公开(公告)日：2022-08-11

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 ,  H04N19/30 ,  H04N19/184

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：US20210160301A1

公开(公告)日：2021-05-27

申请号：US17164523

申请日：2021-02-01

Applicant: NETFLIX, INC.

Inventor： Zhi LI ,  Te-Yuan HUANG

IPC: H04L29/06

Abstract: In various embodiments, a hindsight application computes a hindsight metric value for evaluation of a video rate selection algorithm. The hindsight application determines a first encoding option associated with a source chunk of a media title based on a network throughput trace and a buffer trellis. The hindsight application determines that the first encoding option is associated with a buffered duration range. The buffered duration range is also associated with a second encoding option that is stored in the buffer trellis. After determining that the first encoding option is associated with a higher visual quality than the second encoding option, the hindsight application stores the first encoding option instead of the second encoding option in the buffer trellis to generate a modified buffer trellis. Finally, the hindsight application computes a hindsight metric value associated with a sequence of encoded chunks of the media title based on the modified buffer trellis.

公开(公告)号：US20210127119A1

公开(公告)日：2021-04-29

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 ,  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：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：US20210058626A1

公开(公告)日：2021-02-25

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：US20210058625A1

公开(公告)日：2021-02-25

申请号：US17093449

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 determines an absolute quality score for encoded video content viewed on a target viewing device. In operation, the perceptual quality application determines a baseline absolute quality score for the encoded video content viewed on a baseline viewing device. Subsequently, the perceptual quality application determines that a target value for a type of the target viewing device does not match a base value for the type of the baseline viewing device. The perceptual quality application computes an absolute quality score for the encoded video content viewed on the target viewing device based on the baseline absolute quality score and the target value. Because the absolute quality score is independent of the viewing device, the absolute quality score accurately reflects the perceived quality of a wide range of encoded video content when decoded and viewed on a viewing device.

公开(公告)号：US20190297329A1

公开(公告)日：2019-09-26

申请号：US16352755

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

Abstract: In various embodiments, a bootstrapping training subsystem performs sampling operation(s) on a training database that includes subjective scores to generate resampled dataset. For each resampled dataset, the bootstrapping training subsystem performs machine learning operation(s) to generate a different bootstrap perceptual quality model. The bootstrapping training subsystem then uses the bootstrap perceptual quality models to quantify the accuracy of a perceptual quality score generated by a baseline perceptual quality model for a portion of encoded video content. Advantageously, relative to prior art solutions in which the accuracy of a perceptual quality score is unknown, the bootstrap perceptual quality models enable developers and software applications to draw more valid conclusions and/or more reliably optimize encoding operations based on the perceptual quality score.

公开(公告)号：US20190295242A1

公开(公告)日：2019-09-26

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: G06T7/00 ,  H04N19/154

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.