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Sub-block MV inheritance between color components

專利號(hào)
US11659192B2
公開日期
2023-05-23
申請(qǐng)人
Beijing Bytedance Network Technology Co., Ltd.; Bytedance Inc.(CN Beijing US CA Los Angeles)
發(fā)明人
Kai Zhang; Li Zhang; Hongbin Liu; Yue Wang
IPC分類
H04N19/52; H04N19/176; H04N19/186; H04N19/55; H04N19/119; H04N19/136
技術(shù)領(lǐng)域
mv,motion,signshift,sub,mv0x,mv1x,mv0y,vectors,cu,mv1y
地域: Beijing

摘要

Devices, systems and methods for sub-block based prediction are described. In a representative aspect, a method for video processing includes partitioning a first component of a current video block into a first set of sub-blocks and partitioning a second component of the current video block into a second set of sub-blocks. A sub-block of the second component corresponds to one or more sub-blocks of the first component. The method also includes deriving, based on a color format of the current video block, motion vectors for a sub-block of the second component based on motion vectors for one or more corresponding sub-blocks of the first color component.

說(shuō)明書

MV refinement is a pattern based MV search with the criterion of bilateral matching cost or template matching cost. In the JEM, two search patterns are supported—an unrestricted center-biased diamond search (UCBDS) and an adaptive cross search for MV refinement at the CU level and sub-CU level, respectively. For both CU and sub-CU level MV refinement, the MV is directly searched at quarter luma sample MV accuracy, and this is followed by one-eighth luma sample MV refinement. The search range of MV refinement for the CU and sub-CU step are set equal to 8 luma samples.

In the bilateral matching merge mode, bi-prediction is applied because the motion information of a CU is derived based on the closest match between two blocks along the motion trajectory of the current CU in two different reference pictures. In the template matching merge mode, the encoder can choose among uni-prediction from list0, uni-prediction from list1, or bi-prediction for a CU. The selection ca be based on a template matching cost as follows:

If costBi<=factor*min (cost0, cost1)

    • bi-prediction is used;
    • Otherwise, if cost0<=cost1
    • uni-prediction from list0 is used;
    • Otherwise,
    • uni-prediction from list1 is used;

權(quán)利要求

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