论文标题
动态点云属性压缩的运动估计和过滤预测
Motion estimation and filtered prediction for dynamic point cloud attribute compression
论文作者
论文摘要
在点云压缩中，由于不规则的几何形状，用于相互预测编码的时间冗余是具有挑战性的。本文提出了一种有效的基于块的颜色属性压缩方案。该方案包括整数运动估计和基于自适应图的基于自适应的环内过滤方案，以改进属性预测。提出的基于块的运动估计方案由初始运动搜索组成，该搜索利用了几何和颜色属性，然后进行运动改进，仅将颜色预测误差最小化。为了进一步改善颜色预测，我们提出了一个顶点域低通图滤波方案，该方案可以从运动估计中以不同的精度从运动估计计算的预测变量中自适应地消除噪声。我们的实验表明，与最先进的编码方法相比，编码显着。
In point cloud compression, exploiting temporal redundancy for inter predictive coding is challenging because of the irregular geometry. This paper proposes an efficient block-based inter-coding scheme for color attribute compression. The scheme includes integer-precision motion estimation and an adaptive graph based in-loop filtering scheme for improved attribute prediction. The proposed block-based motion estimation scheme consists of an initial motion search that exploits geometric and color attributes, followed by a motion refinement that only minimizes color prediction error. To further improve color prediction, we propose a vertex-domain low-pass graph filtering scheme that can adaptively remove noise from predictors computed from motion estimation with different accuracy. Our experiments demonstrate significant coding gain over state-of-the-art coding methods.
