
<div>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 RAHT based coders are promising, improving over the original, nonpredictive RAHT encoder, while providing the key functionality of being embedded.</div>
Composite material, Trajectory Data Mining and Analysis, Computational Mechanics, Geometry, Set (abstract data type), Pattern Recognition, Engineering, Point (geometry), Graph Matching and Analysis Techniques, FOS: Mathematics, Cloud computing, Analysis of Three-Dimensional Shape Structures, Data mining, Point Clouds, Computer science, Materials science, Programming language, Algorithm, Operating system, Data compression, Computer Science, Physical Sciences, Signal Processing, Point Set Surfaces, Compression (physics), Computer Vision and Pattern Recognition, Mathematics
Composite material, Trajectory Data Mining and Analysis, Computational Mechanics, Geometry, Set (abstract data type), Pattern Recognition, Engineering, Point (geometry), Graph Matching and Analysis Techniques, FOS: Mathematics, Cloud computing, Analysis of Three-Dimensional Shape Structures, Data mining, Point Clouds, Computer science, Materials science, Programming language, Algorithm, Operating system, Data compression, Computer Science, Physical Sciences, Signal Processing, Point Set Surfaces, Compression (physics), Computer Vision and Pattern Recognition, Mathematics
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