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Set Partitioning in Hierarchical Trees for Point Cloud Attribute Compression
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  • Ricardo de Queiroz ,
  • Andre Souto ,
  • Victor Figueiredo ,
  • Philip Chou
Ricardo de Queiroz
Universidade de Brasilia

Corresponding Author:[email protected]

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Andre Souto
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Victor Figueiredo
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Philip Chou
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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.