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.

We propose an intra frame predictive strategy for compression of 3D point cloud attributes. Our approach is integrated with the region adaptive graph Fourier transform (RAGFT), a multi-resolution transform formed by a composition of localized block transforms, which produces a set of low pass (approximation) and high pass (detail) coefficients at multiple resolutions. Since the transform operations are spatially localized, RAGFT coefficients at a given resolution may still be correlated. To exploit this phenomenon, we propose an intraprediction strategy, in which decoded approximation coefficients are used to predict uncoded detail coefficients. The prediction residuals are then quantized and entropy coded. For the 8i dataset, we obtain gains up to 0. 5db as compared to intra predicted point cloud compresion based on the region adaptive Haar transform (RAHT).

We propose an embedded attribute encoding method for point clouds based on set partitioning in hierarchical trees (SPIHT). The encoder is used with the region-adaptive hierarchical transform which has been a popular transform for point cloud coding, even included in the standard geometry-based point cloud coder (G-PCC). The result is an encoder that is efficient, scalable, and embedded. That is, higher compression is achieved by trimming the full bit-stream. G-PCCs RAHT coefficient prediction prevents the straightforward incorporation of SPIHT into G-PCC. However, our results over other RAHT-based coders are promising, improving over the original, nonpredictive RAHT encoder, while providing the key functionality of being embedded.

We studied predictive coding applied to the region-adaptive hierarchical transform (RAHT) which is used for point cloud compression (PCC). RAHT is part of MPEG’s geometry-based PCC test model and an intra-frame prediction scheme for RAHT (URAHT), wherein the prediction residual is encoded rather than the voxel attributes themselves, has been shown to deliver large gains. We extend the scheme to inter-frame prediction and show that a combination of simple zero-motion-vector (ZMV) inter-frame and intra-frame predictions can provide sizeable gains over pure RAHT or over intra-frame-only prediction when compressing dynamic point clouds. An adaptive method is used such that sections where ZMV does not yield good prediction switch to intra-frame prediction, assuring the performance to be at least that of the intra-frame case. Be the gains large (in steady parts) or very small (where there is rapid motion) results show consistent positive gains coming from a simple inter- and intra-frame prediction combination.