公开(公告)号：US20240037802A1

公开(公告)日：2024-02-01

申请号：US18479507

申请日：2023-10-02

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Timofey Mikhailovich Solovyev ,  Biao Wang ,  Elena Alexandrovna Alshina ,  Han Gao ,  Panqi Jia ,  Esin Koyuncu ,  Alexander Alexandrovich Karabutov ,  Mikhail Vyacheslavovich Sosulnikov ,  Semih Esenlik ,  Sergey Yurievich Ikonin

IPC: G06T9/00 ,  G06T3/40

CPC classification number: G06T9/002 ,  G06T3/4046

Abstract: This application provides methods and apparatuses for processing of picture data or picture feature data using a neural network with two or more layers. The present disclosure may be applied in the field of artificial intelligence (AI)-based video or picture compression technologies, and in particular, to the field of neural network-based video compression technologies. According to some embodiments, position within the neural network, at which auxiliary information may be entered for processing is selectable based on a gathering condition. The gathering condition may assess whether some prerequisite is fulfilled. Some of the advantages may include better performance in terms of rate and/or disclosure due to the effect of increased flexibility in neural network configurability.

公开(公告)号：US20230336784A1

公开(公告)日：2023-10-19

申请号：US18336735

申请日：2023-06-16

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Semih Esenlik ,  Panqi Jia ,  Elena Alexandrovna Alshina

IPC: H04N19/132 ,  H04N19/70 ,  H04N19/119 ,  H04N19/42 ,  H04N19/167 ,  H04N19/172

CPC classification number: H04N19/70 ,  H04N19/119 ,  H04N19/132 ,  H04N19/167 ,  H04N19/172 ,  H04N19/42

Abstract: For picture decoding and encoding of neural-network-based bitstreams, a picture is represented by an input set of samples which is obtained from the bitstream. The picture is reconstructed from output subsets, which are generated as a result of processing the input set L. The input set is divided into multiple input subsets Li. The input subsets are each subject to processing with a neural network having one or more layers. The neural network uses as input multiple samples of an input subset and generates one sample of an output subset. By combining the output subsets, the picture is reconstructed. In particular, the size of at least one input subset is smaller than a size that is required to obtain the size of the respective output subset, after processing by the one or more layers of the neural network.

公开(公告)号：US20250142066A1

公开(公告)日：2025-05-01

申请号：US19007327

申请日：2024-12-31

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Johannes Sauer ,  Panqi Jia ,  Elena Alexandrovna Alshina ,  Atanas Boev

IPC: H04N19/119 ,  H04N19/176 ,  H04N19/186

Abstract: The present disclosure relates to picture encoding and decoding of image regions on tile-basis. In particular, multiple components of an input tensor including a first and second component in spatial dimensions is processed within multiple pipelines. The processing of the first component includes dividing the first component in the spatial dimensions into a first plurality of tiles. Likewise, the processing of the second component includes dividing the second component in the spatial dimensions into a second plurality of tiles. The respective first and second plurality of tiles are then processed each separately. Among the first and second plurality of tiles there are at least two respective collocated tiles differing in size. In case of compression, the processing of the first and/or second component includes picture encoding, rate distortion optimization quantization, and picture filtering. In case of decompression, the processing includes picture decoding and picture filtering.

公开(公告)号：US12284390B2

公开(公告)日：2025-04-22

申请号：US18336735

申请日：2023-06-16

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Semih Esenlik ,  Panqi Jia ,  Elena Alexandrovna Alshina

IPC: H04N19/70 ,  H04N19/119 ,  H04N19/132 ,  H04N19/167 ,  H04N19/172 ,  H04N19/42

Abstract: For picture decoding and encoding of neural-network-based bitstreams, a picture is represented by an input set of samples which is obtained from the bitstream. The picture is reconstructed from output subsets, which are generated as a result of processing the input set L. The input set is divided into multiple input subsets Li. The input subsets are each subject to processing with a neural network having one or more layers. The neural network uses as input multiple samples of an input subset and generates one sample of an output subset. By combining the output subsets, the picture is reconstructed. In particular, the size of at least one input subset is smaller than a size that is required to obtain the size of the respective output subset, after processing by the one or more layers of the neural network.

公开(公告)号：US20250142099A1

公开(公告)日：2025-05-01

申请号：US19007203

申请日：2024-12-31

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Johannes Sauer ,  Panqi Jia ,  Elena Alexandrovna Alshina ,  Atanas Boev

IPC: H04N19/436 ,  H04N19/119 ,  H04N19/172 ,  H04N19/70 ,  H04N19/80 ,  H04N19/85

Abstract: Neural-network-based picture encoding and decoding of image regions may be performed on a tile-basis. An input tensor representing picture data is processed by the neural network, which includes at least a first and second subnetwork. The first subnetwork is applied to a first tensor where the first tensor is divided in a spatial dimensions into a first plurality of tiles. The first tiles are then further processed by the first subnetwork. After application of the first subnetwork, the second subnetwork is applied to a second tensor where the second tensor is divided in the spatial dimensions into a second plurality of tiles. The second tiles are then further processed by the second subnetwork. Among the first and second plurality of tiles there are at least two respective collocated tiles differing in size.

公开(公告)号：US20240296593A1

公开(公告)日：2024-09-05

申请号：US18661245

申请日：2024-05-10

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Alexander Alexandrovich Karabutov ,  Panqi Jia ,  Atanas Boev ,  Han Gao ,  Biao Wang ,  Elena Alexandrovna Alshina ,  Johannes Sauer

IPC: G06T9/00 ,  H04N19/13 ,  H04N19/167 ,  H04N19/186

CPC classification number: G06T9/002 ,  H04N19/13 ,  H04N19/167 ,  H04N19/186

Abstract: A conditional coding of components of an image is described. A method of encoding at least a portion of an image is provided, which comprises encoding a primary component of the image independently from at least one secondary component and encoding the at least one secondary component of the image using information from the primary component. Further, it is provided a method of encoding at least a portion of an image, comprising providing a residual comprising a primary residual component for a primary component of the image and at least one secondary residual component for at least one secondary component of the image that is different from the primary component, encoding the primary residual component independently from the at least one secondary residual component and encoding the at least one secondary residual component using information from the primary residual component.

公开(公告)号：US20240161488A1

公开(公告)日：2024-05-16

申请号：US18479611

申请日：2023-10-02

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Timofey Mikhailovich Solovyev ,  Elena Alexandrovna Alshina ,  Biao Wang ,  Alexander Alexandrovich Karabutov ,  Mikhail Vyacheslavovich Sosulnikov ,  Georgy Petrovich Gaikov ,  Han Gao ,  Panqi Jia ,  Esin Koyuncu ,  Sergey Yurievich Ikonin ,  Semih Esenlik

IPC: G06V10/82 ,  G06V10/77 ,  G06V20/40 ,  H04N19/513 ,  H04N19/91

CPC classification number: G06V10/82 ,  G06V10/7715 ,  G06V20/46 ,  H04N19/521 ,  H04N19/91

Abstract: This application provides methods and apparatuses for processing of picture data or picture feature data using a neural network with two or more layers. The present disclosure may be applied in the field of artificial intelligence (AI)-based video or picture compression technologies, and in particular, to the field of neural network-based video compression technologies. According to some embodiments, two kinds of data are combined during the processing including processing by the neural network. The two kinds of data are obtained from different stages of processing by the network. Some of the advantages may include greater scalability and a more flexible design of the neural network architecture which may further lead to better encoding/decoding performance.