公开(公告)号：US11778010B2

公开(公告)日：2023-10-03

申请号：US17164523

申请日：2021-02-01

Applicant: NETFLIX, INC.

Inventor： Zhi Li ,  Te-Yuan Huang

IPC: H04L65/70 ,  H04L65/80 ,  H04L65/613

CPC classification number: H04L65/70 ,  H04L65/613 ,  H04L65/80

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：US11683545B2

公开(公告)日：2023-06-20

申请号：US17700231

申请日：2022-03-21

Applicant: NETFLIX, INC.

Inventor： Christos Bampis ,  Zhi Li

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

CPC classification number: H04N21/2407 ,  H04N21/251 ,  H04N21/4425 ,  H04N21/4756 ,  H04N21/6547 ,  H04N21/6582

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：US11532077B2

公开(公告)日：2022-12-20

申请号：US16995680

申请日：2020-08-17

Applicant: NETFLIX, INC.

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

IPC: G06K9/00 ,  G06T7/00 ,  G06T7/90 ,  H04N21/234

Abstract: In various embodiments, a quality inference application estimates the perceived quality of reconstructed videos. The quality inference application computes a first feature value for a first feature included in a feature vector based on a color component associated with a reconstructed video. The quality inference application also computes a second feature value for a second feature included in the feature vector based on a brightness component associated with the reconstructed video. Subsequently, the quality inference application computes a perceptual quality score based on the first feature value and the second feature value. The perceptual quality score indicates a level of visual quality associated with at least one frame included in the reconstructed video.

公开(公告)号：US11522935B2

公开(公告)日：2022-12-06

申请号：US17113884

申请日：2020-12-07

Applicant: NETFLIX, INC.

Inventor： Te-Yuan Huang ,  Chaitanya Ekanadham ,  Andrew J. Berglund ,  Zhi Li

IPC: H04L65/70 ,  H04L43/08

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：US10798387B2

公开(公告)日：2020-10-06

申请号：US15782586

申请日：2017-10-12

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：US10586110B2

公开(公告)日：2020-03-10

申请号：US15406617

申请日：2017-01-13

Applicant: NETFLIX, INC.

Inventor： Zhi Li

IPC: H04N11/02 ,  G06K9/00 ,  H04N21/466 ,  H04N17/00 ,  G06K9/03 ,  G06T3/40 ,  G06T7/00 ,  H04N21/44 ,  H04N21/475

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.