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：US20200252666A1

公开(公告)日：2020-08-06

申请号：US16780752

申请日：2020-02-03

Applicant: NETFLIX, INC.

Inventor： Glenn Van WALLENDAEL ,  Anne AARON ,  Kyle SWANSON ,  Jan DE COCK ,  Liwei GUO ,  Sonia BHASKAR

IPC: H04N21/2343 ,  H04L29/06 ,  H04N19/587

Abstract: In various embodiments, an interpolation-based encoding application encodes a first subsequence included in a media title at each encoding point included in a first set of encoding points to generate encoded subsequences. Subsequently, the interpolation-based encoding application performs interpolation operation(s) based on the encoded subsequences to estimate a first media metric value associated with a first encoding point that is not included in the first set of encoding points. The interpolation-based encoding application then generates an encoding recipe based on the encoded subsequences and the first media metric value. The encoding recipe specifies a different encoding point for each subsequence included in the media title. After determining that the encoding recipe specifies the first encoding point for the first subsequence, the interpolation-based encoding application encodes the first subsequence at the first encoding point to generate at least a portion of an encoded version of the media title.

公开(公告)号：US20180302456A1

公开(公告)日：2018-10-18

申请号：US16016432

申请日：2018-06-22

Applicant: NETFLIX, INC.

Inventor： Ioannis KATSAVOUNIDIS ,  Anne AARON ,  Jan DE COCK

IPC: H04L29/06 ,  H04N19/179 ,  H04N19/146

CPC classification number: H04L65/607 ,  H04L65/4069 ,  H04L65/4084 ,  H04L65/605 ,  H04N19/115 ,  H04N19/124 ,  H04N19/132 ,  H04N19/146 ,  H04N19/147 ,  H04N19/179

Abstract: In various embodiments, an iterative encoding application generates shot encode points based on a first set of encoding points and a first shot sequence associated with a media title. The iterative encoding application performs convex hull operations across the shot encode points to generate a first convex hull. Subsequently, the iterative encoding application generates encoded media sequences based on the first convex hull and a second convex hull that is associated with both a second shot sequence associated with the media title and a second set of encoding points. The iterative encoding application determines a first optimized encoded media and a second optimized encoded media sequence from the encoded media sequences based on, respectively, a first target metric value and a second target metric value for a media metric. Portions of the optimized encoded media sequences are subsequently streamed to endpoint devices during playback of the media title.

公开(公告)号：US20170295374A1

公开(公告)日：2017-10-12

申请号：US15207468

申请日：2016-07-11

Applicant: NETFLIX, Inc.

Inventor： Anne AARON ,  Zhi LI ,  Todd GOODALL

IPC: H04N19/154 ,  H04N17/00 ,  G06K9/00

Abstract: In various embodiments, a quality trainer trains a model that computes a value for a perceptual video quality metric for encoded video content. During a pre-training phase, the quality trainer partitions baseline values for metrics that describe baseline encoded video content into partitions based on genre. The quality trainer then performs cross-validation operations on the partitions to optimize hyperparameters associated with the model. Subsequently, during a training phase, the quality trainer performs training operations on the model that includes the optimized hyperparameters based on the baseline values for the metrics to generate a trained model. The trained model accurately tracks the video quality for the baseline encoded video content. Further, because the cross-validation operations minimize any potential overfitting, the trained model accurately and consistently predicts perceived video quality for non-baseline encoded video content across a wide range of genres.