IEEE Trans Image Process. 2023;32:2428-2437. doi: 10.1109/TIP.2023.3265264. Epub 2023 May 1.
We study the use of predictive approaches alongside the region-adaptive hierarchical transform (RAHT) in attribute compression of dynamic point clouds. The use of intra-frame prediction with RAHT was shown to improve attribute compression performance over pure RAHT and represents the state-of-the-art in attribute compression of point clouds, being part of MPEG's geometry-based test model. We studied a combination of inter-frame and intra-frame prediction for RAHT for the compression of dynamic point clouds. An adaptive zero-motion-vector (ZMV) scheme and an adaptive motion-compensated scheme are developed. The simple adaptive ZMV approach is able to achieve sizable gains over pure RAHT and over the intra-frame predictive RAHT (I-RAHT) for point clouds with little or no motion while ensuring similar compression performance to I-RAHT for point clouds with intense motion. The motion-compensated approach, more complex and more powerful, is able to achieve large gains across all of the tested dynamic point clouds.
我们研究了在动态点云的属性压缩中，预测方法与区域自适应层次变换（RAHT）的结合使用。研究表明，与纯 RAHT 相比，使用 RAHT 进行帧内预测可以提高属性压缩性能，这是点云属性压缩的最新技术，也是 MPEG 基于几何的测试模型的一部分。我们研究了 RAHT 在动态点云压缩中的帧间和帧内预测的组合。开发了一种自适应零运动矢量（ZMV）方案和一种自适应运动补偿方案。简单的自适应 ZMV 方法能够在几乎没有运动或只有少量运动的点云上获得比纯 RAHT 和帧内预测 RAHT（I-RAHT）更大的收益，同时确保对于运动剧烈的点云，与 I-RAHT 具有相似的压缩性能。运动补偿方法更复杂、更强大，能够在所有测试的动态点云上获得很大的收益。
