公开(公告)号：US20200351533A1

公开(公告)日：2020-11-05

申请号：US16401066

申请日：2019-05-01

Applicant: NETFLIX, INC.

Inventor： Christos BAMPIS ,  Zhi LI

IPC: H04N21/24 ,  H04N21/25

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.

公开(公告)号：US20190364084A1

公开(公告)日：2019-11-28

申请号：US16036606

申请日：2018-07-16

Applicant: NETFLIX, INC.

Inventor： Te-Yuan HUANG ,  Chaitanya EKANADHAM ,  Andrew J. BERGLUND ,  Zhi LI

IPC: H04L29/06 ,  H04L12/26

Abstract: In various embodiments, a hindsight application computes a total download size for a sequence of encoded chunks associated with a media title for evaluation of at least one aspect of a video streaming service. The hindsight application computes a feasible download end time associated with a source chunk of the media title based on a network throughput trace and a subsequent feasible download end time associated with a subsequent source chunk of the media title. The hindsight application then selects an encoded chunk associated with the source chunk based on the network throughput trace, the feasible download end time, and a preceding download end time associated with a preceding source chunk of the media title. Subsequently, the hindsight application computes the total download size based on the number of encoded bits included in the first encoded chunk. The total download size correlates to an upper bound on visual quality.

公开(公告)号：US20180167620A1

公开(公告)日：2018-06-14

申请号：US15782590

申请日：2017-10-12

Applicant: NETFLIX, INC.

Inventor： Zhi LI ,  Anne AARON ,  Anush MOORTHY ,  Christos BAMPIS

IPC: H04N19/154 ,  H04N19/146 ,  H04N21/647 ,  H04N19/59

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：US20180121730A1

公开(公告)日：2018-05-03

申请号：US15406617

申请日：2017-01-13

Applicant: NETFLIX, INC.

Inventor： Zhi LI

IPC: G06K9/00 ,  H04N21/475 ,  G06T7/00 ,  G06T3/40 ,  H04N21/44

CPC classification number: G06K9/00718 ,  G06K9/00711 ,  G06K9/00765 ,  G06K9/036 ,  G06T3/4092 ,  G06T7/0002 ,  G06T2207/30168 ,  H04N17/004 ,  H04N21/44008 ,  H04N21/4662 ,  H04N21/4756

Abstract: In various embodiments, a subjective modeling engine mitigates inaccuracies in subjective content assessments. The subjective modeling engine generates a model that includes the subjective content assessments in addition to parameters for subjective scores and subjective factors. The subjective modeling engine initializes the parameters and then performs optimization operations that increase the likelihood that the optimized subjective scores compensate for the optimized subjective factors. Advantageously, because the subjective modeling engine jointly optimizes the subjective scores and the subjective factors, the optimized subjective scores provide unbiased and consistent digital content assessments.

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：US20200021634A1

公开(公告)日：2020-01-16

申请号：US16036600

申请日：2018-07-16

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：US20190246112A1

公开(公告)日：2019-08-08

申请号：US15890710

申请日：2018-02-07

Applicant: NETFLIX, INC.

Inventor： Zhi LI ,  Christos BAMPIS

IPC: H04N19/154 ,  H04N21/235

CPC classification number: H04N19/154 ,  G06K9/00744 ,  G06K9/036 ,  G06K9/6269 ,  G06N3/08 ,  G06N20/20 ,  G06T7/0002 ,  G06T2207/10016 ,  G06T2207/30168 ,  H04N19/136 ,  H04N19/91 ,  H04N21/235

Abstract: In various embodiments, an ensemble prediction application computes a quality score for re-constructed visual content that is derived from visual content. The ensemble prediction application computes a first quality score for the re-constructed video content based on a first set of values for a first set of features and a first model that associates the first set of values with the first quality score. The ensemble prediction application computes a second quality score for the re-constructed video content based on a second set of values for a second set of features and a second model that associates the second set of values with the second quality score. Subsequently, the ensemble prediction application determines an overall quality score for the re-constructed video content based on the first quality score and the second quality score. The overall quality score indicates a level of visual quality associated with streamed video content.