Techniques for generating per-title encoding ladders

    公开(公告)号:US11750821B2

    公开(公告)日:2023-09-05

    申请号:US17174121

    申请日:2021-02-11

    Applicant: NETFLIX, INC.

    CPC classification number: H04N19/146 H04N19/154 H04N19/184 H04N19/30

    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.

    Source-consistent techniques for predicting absolute perceptual video quality

    公开(公告)号:US11503304B2

    公开(公告)日:2022-11-15

    申请号:US17093456

    申请日:2020-11-09

    Applicant: NETFLIX, INC.

    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.

    Source-consistent techniques for predicting absolute perceptual video 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.

    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.

    Machine learning techniques for component-based image preprocessing

    公开(公告)号:US11563986B1

    公开(公告)日:2023-01-24

    申请号:US17551086

    申请日:2021-12-14

    Applicant: NETFLIX, INC.

    Abstract: In various embodiments, a training application trains a machine learning model to preprocess images. In operation, the training application computes a chroma sampling factor based on a downscaling factor and a chroma subsampling ratio. The training application executes a machine learning model that is associated with the chroma sampling factor on data that corresponds to both an image and a first chroma component to generate preprocessed data corresponding to the first chroma component. Based on the preprocessed data, the training application updates at least one parameter of the machine learning model to generate a trained machine learning model that is associated with the first chroma component.

    Techniques for optimizing encoding tasks

    公开(公告)号:US11539966B2

    公开(公告)日:2022-12-27

    申请号:US17141067

    申请日:2021-01-04

    Applicant: NETFLIX, INC.

    Abstract: In various embodiments, a shot collation application causes multiple encoding instances to encode a source video sequence that includes at least two shot sequences. The shot collation application assigns a first shot sequence to a first chunk. Subsequently, the shot collation application determines that a second shot sequence does not meet a collation criterion with respect to the first chunk. Consequently, the shot collation application assigns the second shot sequence or a third shot sequence derived from the second shot sequence to a second chunk. The shot collation application causes a first encoding instance to independently encode each shot sequence assigned to the first chunk. Similarly, the shot collation application causes a second encoding instance to independently encode each shot sequence assigned to the second chunk. Finally, a chunk assembler combines the first encoded chunk and the second encoded chunk to generate an encoded video sequence.

    Techniques for optimizing encoding tasks

    公开(公告)号:US10887609B2

    公开(公告)日:2021-01-05

    申请号:US15840998

    申请日:2017-12-13

    Applicant: NETFLIX, INC.

    Abstract: In various embodiments, a shot collation application causes multiple encoding instances to encode a source video sequence that includes at least two shot sequences. The shot collation application assigns a first shot sequence to a first chunk. Subsequently, the shot collation application determines that a second shot sequence does not meet a collation criterion with respect to the first chunk. Consequently, the shot collation application assigns the second shot sequence or a third shot sequence derived from the second shot sequence to a second chunk. The shot collation application causes a first encoding instance to independently encode each shot sequence assigned to the first chunk. Similarly, the shot collation application causes a second encoding instance to independently encode each shot sequence assigned to the second chunk. Finally, a chunk assembler combines the first encoded chunk and the second encoded chunk to generate an encoded video sequence.

    Device-consistent techniques for predicting absolute perceptual video quality

    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.

    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.

Patent Agency Ranking