中科微点-全球专利检索

  1. Login
  2. Sign Up

Search Results

860 records (took 0.031 seconds)

    OPTIMIZING ENCODING OPERATIONS WHEN GENERATING ENCODED VERSIONS OF A MEDIA TITLE
    521.
    发明公开
    OPTIMIZING ENCODING OPERATIONS WHEN GENERATING ENCODED VERSIONS OF A MEDIA TITLE 审中-公开

    公开(公告)号：US20240163465A1

    公开(公告)日：2024-05-16

    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 ,  H04N19/177 ,  H04N19/179

    CPC classification number: H04N19/42 ,  H04N19/103 ,  H04N19/142 ,  H04N19/177 ,  H04N19/179 ,  H04N19/154

    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.

    TECHNIQUES FOR GENERATING A PERCEPTUAL QUALITY MODEL FOR PREDICTING VIDEO QUALITY ACROSS DIFFERENT VIEWING PARAMETERS
    522.
    发明公开
    TECHNIQUES FOR GENERATING A PERCEPTUAL QUALITY MODEL FOR PREDICTING VIDEO QUALITY ACROSS DIFFERENT VIEWING PARAMETERS 审中-公开

    公开(公告)号：US20240119575A1

    公开(公告)日：2024-04-11

    申请号：US17937024

    申请日：2022-09-30

    Applicant: NETFLIX, INC.

    Inventor： Christos G. BAMPIS ,  Zhi LI

    IPC: G06T7/00 ,  G06N20/10

    CPC classification number: G06T7/0002 ,  G06N20/10 ,  G06T2207/10016

    Abstract: In various embodiments, a training application generates a trained perceptual quality model that estimates perceived video quality for reconstructed video. The training application computes a pixels-per-degree value based on a normalized viewing distance and a display resolution. The training application computes a set of feature values corresponding to a set of visual quality metrics based on a reconstructed video sequence, a source video sequence, and the pixels-per-degree value. The training application executes a machine learning algorithm on the first set of feature values to generate the trained perceptual quality model. The trained perceptual quality model maps a particular set of feature values corresponding to the set of visual quality metrics to a particular perceptual quality score.

    Machine learning techniques for video downsampling
    523.
    发明授权
    Machine learning techniques for video downsampling 有权

    公开(公告)号：US11948271B2

    公开(公告)日：2024-04-02

    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 ,  G06N3/084 ,  G06T3/4046 ,  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.

    Scalable media file transfer
    524.
    发明授权
    Scalable media file transfer 有权

    公开(公告)号：US11936717B2

    公开(公告)日：2024-03-19

    申请号：US17528028

    申请日：2021-11-16

    Applicant: NETFLIX, INC.

    Inventor： Shunfei Chen ,  Christopher Ginter ,  Victor Yelevich

    IPC: H04L67/06 ,  G06F21/64 ,  H04L1/08 ,  H04L67/1074

    CPC classification number: H04L67/06 ,  G06F21/64 ,  H04L1/08 ,  H04L67/108

    Abstract: Various embodiments of the present application set forth a computer-implemented method comprising determining a set of digital assets to transfer to a destination device, generating, from the set of digital assets, a corresponding set of chunks, where each chunk is a pre-defined size, for each chunk in the set of chunks, transmitting the chunk to a service node included in a set of service nodes, and verifying that the service node received the chunk, where the set of service nodes receives at least two chunks of the set of chunks in parallel, and after the set of service nodes send the at least two chunks in parallel to the destination device, verifying that the destination device received the set of chunks.

    Comparing video encoders/decoders using shot-based encoding and a perceptual visual quality metric
    527.
    发明授权
    Comparing video encoders/decoders using shot-based encoding and a perceptual visual quality metric 有权

    公开(公告)号：US11870945B2

    公开(公告)日：2024-01-09

    申请号：US17516525

    申请日：2021-11-01

    Applicant: NETFLIX, INC.

    Inventor： Ioannis Katsavounidis ,  Liwei Guo

    IPC: H04N19/196 ,  H04N19/85

    CPC classification number: H04N19/196 ,  H04N19/85

    Abstract: In various embodiments, an encoder comparison application compares the performance of different configured encoders. In operation, the encoder comparison application generates a first global convex hull of video encode points based on a first configured encoder and a set of subsequences included in a source video sequence. Each video encode point is associated with a different encoded version of the source video sequence. The encoder comparison application also generates a second global convex hull of video encode points based on a second configured encoder and the subsequences. Subsequently, the encoder configuration application computes a performance value for an encoding comparison metric based on the first global convex hull and the second global convex hull. Notably, the first performance value estimates a difference in performance between the first configured encoder and the second configured encoder.

Patent Agency Ranking