A novel motion compensated prediction framework using weighted AMVP prediction for HEVC

Li Yu, Guangtao Fu, Aidong Men, Binji Luo, Huiling Zhao
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Abstract

In this paper, we propose a novel motion compensated prediction (MCP) framework which combines the properties of motion vector restriction and weighted advanced motion vector prediction (AMVP) to achieve higher prediction accuracy for High Efficiency Video Coding (HEVC). In our framework, motion vectors of the prediction units (PUs) surrounding the current PU are checked first by a motion field model, and the geometric relationship between motion vectors of the current PU and its neighboring coded PUs is analyzed. Then whether to use the weighted AMVP prediction method is determined by the motion vector restriction criterion. True motion vectors therefore can be obtained. Experimental results show that the proposed framework achieves BD-PSNR increments ranging from 0.03dB to 0.22dB and the BD-rate saving is up to 6.4%.
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基于加权AMVP预测的HEVC运动补偿预测框架
为了提高高效视频编码(HEVC)的预测精度,提出了一种新的运动补偿预测(MCP)框架,该框架结合了运动矢量约束和加权高级运动矢量预测(AMVP)的特性。在我们的框架中,首先通过运动场模型检查当前PU周围的预测单元(PU)的运动向量,并分析当前PU与其相邻编码PU的运动向量之间的几何关系。然后根据运动矢量约束准则确定是否采用加权AMVP预测方法。因此可以得到真正的运动向量。实验结果表明,该框架实现了0.03 ~ 0.22dB的BD-PSNR增量,降低了6.4%的BD-PSNR。
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