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1.发明公开公开(公告)号：US20240037802A1
公开(公告)日：2024-02-01
申请号：US18479507
申请日：2023-10-02
发明人： 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
CPC分类号： G06T9/002 , G06T3/4046
摘要： 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.
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公开(公告)号：US20230336784A1
公开(公告)日：2023-10-19
申请号：US18336735
申请日：2023-06-16
IPC分类号： H04N19/132 , H04N19/70 , H04N19/119 , H04N19/42 , H04N19/167 , H04N19/172
CPC分类号： H04N19/70 , H04N19/119 , H04N19/132 , H04N19/167 , H04N19/172 , H04N19/42
摘要： 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.
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公开(公告)号：US20240296593A1
公开(公告)日：2024-09-05
申请号：US18661245
申请日：2024-05-10
发明人： Alexander Alexandrovich Karabutov , Panqi Jia , Atanas Boev , Han Gao , Biao Wang , Elena Alexandrovna Alshina , Johannes Sauer
IPC分类号： G06T9/00 , H04N19/13 , H04N19/167 , H04N19/186
CPC分类号： G06T9/002 , H04N19/13 , H04N19/167 , H04N19/186
摘要： 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.
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4.发明公开公开(公告)号：US20240161488A1
公开(公告)日：2024-05-16
申请号：US18479611
申请日：2023-10-02
发明人： 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分类号： G06V10/82 , G06V10/77 , G06V20/40 , H04N19/513 , H04N19/91
CPC分类号： G06V10/82 , G06V10/7715 , G06V20/46 , H04N19/521 , H04N19/91
摘要： 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.
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