公开(公告)号：US20250024064A1

公开(公告)日：2025-01-16

申请号：US18899403

申请日：2024-09-27

Applicant: Huawei Technologies Co., Ltd.

Inventor： Yibo Shi ,  Jing Wang ,  Yunying Ge

IPC: H04N19/51 ,  H04N19/137 ,  H04N19/61

Abstract: A method includes determining a current feature and a reference feature. The current feature is of a to-be-encoded current image, and the reference feature is of a reference image of the to-be-encoded current image. A correlation matrix of the reference feature relative to the current feature is determined, and an inter motion feature is determined based on the correlation matrix. The inter motion feature is encoded into a bitstream.

公开(公告)号：US20240105193A1

公开(公告)日：2024-03-28

申请号：US18526406

申请日：2023-12-01

Applicant: Huawei Technologies Co., Ltd.

Inventor： Jue Mao ,  Yin Zhao ,  Ning Yan ,  Haitao Yang ,  Lian Zhang ,  Jing Wang ,  Yibo Shi

IPC: G10L19/08

CPC classification number: G10L19/08

Abstract: This application provides picture or audio encoding and decoding methods and apparatuses, and relates to the field of artificial intelligence (AI)—based picture or audio encoding and decoding technologies, and specifically, to the field of neural network-based picture feature map or audio feature variable encoding and decoding technologies. The encoding method includes: obtaining a to-be-encoded target, where the to-be-encoded target includes a plurality of feature elements, and the plurality of feature elements include a first feature element. The method further includes: obtaining a probability estimation result of the first feature element; determining, based on the probability estimation result of the first feature element, whether to perform entropy encoding on the first feature element; and performing entropy encoding on the first feature element only when it is determined that entropy encoding needs to be performed on the first feature element.

公开(公告)号：US20250037317A1

公开(公告)日：2025-01-30

申请号：US18914881

申请日：2024-10-14

Applicant: Huawei Technologies Co., Ltd.

Inventor： Yunying Ge ,  Jing Wang ,  Yibo Shi

IPC: G06T9/00 ,  G06T3/18 ,  G06T7/20 ,  G06V10/771 ,  G06V10/80

Abstract: A method includes that a decoder side processes, based on a group of feature domain optical flows corresponding to an image frame, a first feature map of a reference frame to obtain a group of intermediate feature maps. The decoder side fuses the group of intermediate feature maps to obtain a predicted feature map, and the decoder side decodes the image frame based on the predicted feature map to obtain a target image. The predicted feature map of the image frame is determined by the decoder side by fusing a plurality of intermediate feature maps, and the predicted feature map includes more image information.

公开(公告)号：US20240422332A1

公开(公告)日：2024-12-19

申请号：US18820582

申请日：2024-08-30

Applicant: Huawei Technologies Co., Ltd.

Inventor： Yibo Shi ,  Yunying Ge ,  Jing Wang

IPC: H04N19/196 ,  G06T9/00 ,  H04N19/159 ,  H04N19/172

Abstract: An encoding method includes obtaining a to-be-encoded frame, where the to-be-encoded frame is a P-frame, determining, from M preset network parameter sets, a network parameter set corresponding to the to-be-encoded frame, where the M preset network parameter sets respectively correspond to different compression performance information, and M is an integer greater than one, and encoding, by an encoding network, and based on the network parameter set corresponding to the to-be-encoded frame, the to-be-encoded frame to obtain a bitstream representative of the to-be-encoded frame.

公开(公告)号：US20230281881A1

公开(公告)日：2023-09-07

申请号：US18316750

申请日：2023-05-12

Applicant: Huawei Technologies Co., Ltd.

Inventor： Yibo Shi ,  Jing Wang ,  Yunying Ge

IPC: G06T9/00

CPC classification number: G06T9/002

Abstract: A video frame compression method includes determining a target neural network from a plurality of neural networks according to a network selection policy; and generating, by using the target neural network, compression information corresponding to a current video frame. If the compression information is obtained by using a first neural network, the compression information includes first compression information of a first feature of the current video frame, and a reference frame of the current video frame is used for a compression process of the first feature of the current video frame. If the compression information is obtained by using a second neural network, the compression information includes second compression information of a second feature of the current video frame, and a reference frame of the current video frame is used for a generation process of the second feature of the current video frame.