Set Partitioning in Hierarchical Trees for Point Cloud Attribute Compression

Abstract

We propose an embedded attribute encoding method for point clouds based on set partitioning in hierarchical trees (SPIHT) [1]. The encoder is used with the region-adaptive hierarchical transform [2] which has been a popular transform for point cloud coding, even included in the standard geometry-based point cloud coder (G-PCC) [3], [4]. The result is an encoder that is efficient, scalable, and embedded. That is, higher compression is achieved by trimming the full bit-stream. G-PCC's RAHT coefficient prediction prevents the straightforward incorporation of SPIHT into G-PCC. However, our results over other RAHTbased coders are promising, improving over the original, nonpredictive RAHT encoder, while providing the key functionality of being embedded.

The Research on Fault Identification of Spatiotemporal Location With Compressed Grid Data Using the Stochastic Sliding Blocks

“…It adopts the structure of a spatial direction tree to transmit important nodes first [19]. Make full use of the characteristics of wavelet coefficients in different frequency bands and carry out sequential quantization encoding of wavelet coefficients [20]. A block-based parallel SPIHT algorithm improves the algorithm's speed while reducing the image compression effect, which reduces PSNR [21].…”