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    Iterative techniques for generating multiple encoded versions of a media title
    11.
    发明授权
    Iterative techniques for generating multiple encoded versions of a media title 有权

    公开(公告)号：US10742708B2

    公开(公告)日：2020-08-11

    申请号：US16016432

    申请日：2018-06-22

    Applicant: NETFLIX, INC.

    Inventor： Ioannis Katsavounidis ,  Anne Aaron ,  Jan De Cock

    IPC: H04L29/06 ,  H04N19/146 ,  H04N19/179 ,  H04N19/132 ,  H04N19/124 ,  H04N19/147 ,  H04N19/115

    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.

    Techniques for optimizing bitrates and resolutions during encoding
    12.
    发明授权
    Techniques for optimizing bitrates and resolutions during encoding 有权

    公开(公告)号：US10404986B2

    公开(公告)日：2019-09-03

    申请号：US14673621

    申请日：2015-03-30

    Applicant: NETFLIX, Inc

    Inventor： Anne Aaron ,  David Ronca ,  Ioannis Katsavounidis ,  Andy Schuler

    IPC: H04N19/146 ,  H04N19/124 ,  H04N19/154 ,  H04N21/234 ,  H04N21/2343

    Abstract: In one embodiment of the present invention, an encoding bitrate ladder selector tailors bitrate ladders to the complexity of source data. Upon receiving source data, a complexity analyzer configures an encoder to repeatedly encode the source data-setting a constant quantization parameter to a different value for each encode. The complexity analyzer processes the encoding results to determine an equation that relates a visual quality metric to an encoding bitrate. The bucketing unit solves this equation to estimate a bucketing bitrate at a predetermined value of the visual quality metric. Based on the bucketing bitrate, the bucketing unit assigns the source data to a complexity bucket having an associated, predetermined bitrate ladder. Advantageously, sagaciously selecting the bitrate ladder enables encoding that optimally reflects tradeoffs between quality and resources (e.g., storage and bandwidth) across a variety of source data types instead of a single, “typical” source data type.

    TECHNIQUES FOR PREDICTING PERCEPTUAL VIDEO QUALITY
    14.
    发明申请
    TECHNIQUES FOR PREDICTING PERCEPTUAL VIDEO QUALITY 有权
    预测视觉质量的技术

    公开(公告)号：US20160335754A1

    公开(公告)日：2016-11-17

    申请号：US14709230

    申请日：2015-05-11

    Applicant: NETFLIX, Inc

    Inventor： Anne Aaron ,  Dae Kim ,  Yu-Chieh Lin ,  David Ronca ,  Andy Schuler ,  Kuyen Tsao ,  Chi-Hao Wu

    IPC: G06T7/00 ,  G06T7/20

    CPC classification number: G06T7/0002 ,  G06T7/20 ,  G06T9/002 ,  G06T2207/10016 ,  G06T2207/20084 ,  G06T2207/30168 ,  H04N19/154 ,  H04N21/466 ,  H04N21/4666

    Abstract: In one embodiment of the present invention, a quality trainer and quality calculator collaborate to establish a consistent perceptual quality metric via machine learning. In a training phase, the quality trainer leverages machine intelligence techniques to create a perceptual quality model that combines objective metrics to optimally track a subjective metric assigned during viewings of training videos. Subsequently, the quality calculator applies the perceptual quality model to values for the objective metrics for a target video, thereby generating a perceptual quality score for the target video. In this fashion, the perceptual quality model judiciously fuses the objective metrics for the target video based on the visual feedback processed during the training phase. Since the contribution of each objective metric to the perceptual quality score is determined based on empirical data, the perceptual quality score is a more accurate assessment of observed video quality than conventional objective metrics.

    Abstract translation: 在本发明的一个实施例中，质量培训者和质量计算器通过机器学习协作建立一致的知觉质量度量。 在培训阶段，质量培训师利用机器智能技术创建感知质量模型，结合客观指标，最佳地跟踪在观看培训视频期间分配的主观度量。 随后，质量计算器将感知质量模型应用于目标视频的客观指标的值，从而生成目标视频的感知质量得分。 以这种方式，感知质量模型基于在训练阶段处理的视觉反馈，明智地融合了目标视频的客观指标。 由于每个客观度量对知觉质量得分的贡献是基于经验数据确定的，感知质量得分是比传统客观指标更准确地评估观察到的视频质量。

    Optimizing encoding operations when generating encoded versions of a media title
    15.
    发明授权
    Optimizing encoding operations when generating encoded versions of a media title 有权

    公开(公告)号：US12200235B2

    公开(公告)日：2025-01-14

    申请号：US18486986

    申请日：2023-10-13

    Applicant: NETFLIX, INC.

    Inventor： Ioannis Katsavounidis ,  Anne Aaron ,  Jan De Cock

    IPC: H04N19/42 ,  H04N19/103 ,  H04N19/142 ,  H04N19/146 ,  H04N19/147 ,  H04N19/154 ,  H04N19/177 ,  H04N19/179

    Abstract: In various embodiments, a sequence-based encoding application partitions a set of shot sequences associated with a media title into multiple clusters based on at least one feature that characterizes media content and/or encoded media content associated with the media title. The clusters include at least a first cluster and a second cluster. The sequence-based encoding application encodes a first shot sequence using a first operating point to generate a first encoded shot sequence. The first shot sequence and the first operating point are associated with the first cluster. By contrast, the sequence-based encoding application encodes a second shot sequence using a second operating point to generate a second encoded shot sequence. The second shot sequence and the second operating point are associated with the second cluster. Subsequently, the sequence-based encoding application generates an encoded media sequence based on the first encoded shot sequence and the second encoded shot sequence.

    Iterative techniques for generating multiple encoded versions of a media title
    17.
    发明授权
    Iterative techniques for generating multiple encoded versions of a media title 有权

    公开(公告)号：US11444999B2

    公开(公告)日：2022-09-13

    申请号：US16987331

    申请日：2020-08-06

    Applicant: NETFLIX, INC.

    Inventor： Ioannis Katsavounidis ,  Anne Aaron ,  Jan De Cock

    IPC: H04N19/147 ,  H04L65/70 ,  H04N19/146 ,  H04N19/179 ,  H04N19/132 ,  H04N19/124 ,  H04N19/115 ,  H04L65/61 ,  H04L65/612 ,  H04L65/75

    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.

    Device-consistent techniques for predicting absolute perceptual video quality
    18.
    发明授权
    Device-consistent techniques for predicting absolute perceptual video quality 有权

    公开(公告)号：US10834406B2

    公开(公告)日：2020-11-10

    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 ,  H04N19/59 ,  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.

    Techniques for predicting perceptual video quality
    19.
    发明授权
    Techniques for predicting perceptual video quality 有权

    公开(公告)号：US10475172B2

    公开(公告)日：2019-11-12

    申请号：US16017929

    申请日：2018-06-25

    Applicant: NETFLIX, INC.

    Inventor： Anne Aaron ,  Dae Kim ,  Yu-Chieh Lin ,  David Ronca ,  Andy Schuler ,  Kuyen Tsao ,  Chi-Hao Wu

    IPC: G06K9/00 ,  G06T7/00 ,  G06T9/00 ,  H04N21/466 ,  H04N19/154 ,  G06T7/20

    Abstract: In one embodiment of the present invention, a quality trainer and quality calculator collaborate to establish a consistent perceptual quality metric via machine learning. In a training phase, the quality trainer leverages machine intelligence techniques to create a perceptual quality model that combines objective metrics to optimally track a subjective metric assigned during viewings of training videos. Subsequently, the quality calculator applies the perceptual quality model to values for the objective metrics for a target video, thereby generating a perceptual quality score for the target video. In this fashion, the perceptual quality model judiciously fuses the objective metrics for the target video based on the visual feedback processed during the training phase. Since the contribution of each objective metric to the perceptual quality score is determined based on empirical data, the perceptual quality score is a more accurate assessment of observed video quality than conventional objective metrics.

    Techniques for predicting perceptual video quality
    20.
    发明授权
    Techniques for predicting perceptual video quality 有权

    公开(公告)号：US10438335B2

    公开(公告)日：2019-10-08

    申请号：US16017929

    申请日：2018-06-25

    Applicant: NETFLIX, INC.

    Inventor： Anne Aaron ,  Dae Kim ,  Yu-Chieh Lin ,  David Ronca ,  Andy Schuler ,  Kuyen Tsao ,  Chi-Hao Wu

    IPC: G06K9/00 ,  G06T7/00 ,  G06T9/00 ,  H04N21/466 ,  H04N19/154 ,  G06T7/20

    Abstract: In one embodiment of the present invention, a quality trainer and quality calculator collaborate to establish a consistent perceptual quality metric via machine learning. In a training phase, the quality trainer leverages machine intelligence techniques to create a perceptual quality model that combines objective metrics to optimally track a subjective metric assigned during viewings of training videos. Subsequently, the quality calculator applies the perceptual quality model to values for the objective metrics for a target video, thereby generating a perceptual quality score for the target video. In this fashion, the perceptual quality model judiciously fuses the objective metrics for the target video based on the visual feedback processed during the training phase. Since the contribution of each objective metric to the perceptual quality score is determined based on empirical data, the perceptual quality score is a more accurate assessment of observed video quality than conventional objective metrics.

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