Sub-PU based Motion Vector Prediction

本文介绍HEVC(高效视频编码)标准中预测单元(PU)的运动信息预测方法,包括ATMVP(高级时域运动矢量预测)和STMVP(时空运动矢量预测)。ATMVP通过找到运动源图片及其对应块来获取运动信息;STMVP则通过缩放和平移等操作从相邻块获取运动矢量。

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  • HEVC
    • PU can have at most one set of motion for each prediction direction
  • JEM
    • PU will be splitted by sub-PUs
    • Adds 2 merge modes from sub-PUs, including ATMVP and STMVP. So, do not need to encode other mvs, just enlarge the number of merge candidates.
    • each PU has multiple sets of motion information from multiple blocks using ATMVP(advanced temporal motion vector prediction)
    • STMVP(spatial-temporal motion vector prediction), mvs of sub-PUs are derived recursively by using the temporal motion vector predictor and spatial neighboring mv

  • ATMVP
    • Fisrt, get motion source picture and corresponding block
    • Second, get converted mv and reference indices from corresponding blocks(not very clear)
  • STMVP
    • For each sub-PU, first, get all the spatial(2) and temporal(1) mvs with scaling and retrieving; then, average all the mvs for each reference list, and this averaged mv is assigned as the mv of this sub-PU
  • code
    • #define COM16_C806_VCEG_AZ10_SUB_PU_TMVP
    • ATMVP
      • abstain the temporal vector, to find the motion sourve picture and corresponding block -> check function: get1stTvFromSpatialNeighbor(use spatial neighboring[left/top/topleft/topright/leftbottom & refList0/refList1] mvs)
      • get the ATMVP merge candidate, with the given temporal vector -> check function:getInterMergeSubPUTmvpCandidate(1.use 1st temporal vector to derive the centor cu for all the refPics in all refList[do search till find the first available], 2. if the predMode of these centor cus is not equal to MODE_INTRA, derive the colMv using scale operations according to the distance between colPic & curPic with function of deriveScaledMotionTemporalForOneDirection -> xGetDistScaleFactor[colPicPoc, colPicRefPoc, curPicPoc, curPicPoc], 3. if the centor cu has motion, then the advanced TMVP candidate is considered, for each sub PU in current PU, we should derive its col-cu in the colPic, when the col-cu is not intraCU, then deriveScaledMotionTemporalForOneDirection for each sub PU)
    • if Enable ATMVP, then get the STMVP
      • get extend temporal and spatial merge candidates with the function:getInterMergeSubPURecursiveCandidate(for each sub PU, get the extend left and top neighboring block mvs & temporal neighboring mvs, then, generate the STMVP for this sub PU with function: generateMvField)
HIVT(Hierarchical Vector Transformer for Multi-Agent Motion Prediction)是一种用于多智能体运动预测的分层向量变换器。该模型使用了向量变换器(Vector Transformer)的层级架构,用于对多智能体的运动轨迹进行预测。 HIVT模型旨在解决多智能体之间相互影响和合作的问题。在多智能体系统中,智能体之间的运动和行为往往会相互影响,因此准确预测智能体的运动轨迹变得非常重要。传统的方法往往难以捕捉到智能体之间的复杂相互作用和外部环境的影响,而HIVT模型通过分层向量变换器的架构,可以更好地捕捉到多智能体系统中的相互作用。 HIVT模型首先使用一个全局的向量变换器来处理整个多智能体系统的运动轨迹,以捕捉全局的趋势和相互作用。然后,对于每个智能体,模型使用一个局部的向量变换器来预测其个体的运动轨迹,以考虑个体特定的动态特征和周围智能体的影响。 通过分层向量变换器的架构,HIVT模型能够更好地处理多智能体系统中的动态变化和相互作用,提高了运动轨迹预测的准确性。同时,该模型还可以应用于多个领域,如智能交通、无人机团队协作等。 总而言之,HIVT模型是一种基于分层向量变换器的多智能体运动预测方法,通过捕捉多智能体系统中的相互作用和全局趋势,提高了运动轨迹预测的准确性和适用性。该模型在多个领域具有广泛的应用前景。
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