Point Cloud Geometry Scalable Coding with a Quality-Conditioned Latents Probability Estimator
<|reference_start|>Point Cloud Geometry Scalable Coding with a Quality-Conditioned Latents Probability Estimator: The widespread usage of point clouds (PC) for immersive visual applications has resulted in the use of very heterogeneous receiving conditions and devices, notably in terms of network, hardware, and display capabilities. In this scenario, quality scalability, i.e., the ability to reconstruct a signal at different qualities by progressively decoding a single bitstream, is a major requirement that has yet to be conveniently addressed, notably in most learning-based PC coding solutions. This paper proposes a quality scalability scheme, named Scalable Quality Hyperprior (SQH), adaptable to learning-based static point cloud geometry codecs, which uses a Quality-conditioned Latents Probability Estimator (QuLPE) to decode a high-quality version of a PC learning-based representation, based on an available lower quality base layer. SQH is integrated in the future JPEG PC coding standard, allowing to create a layered bitstream that can be used to progressively decode the PC geometry with increasing quality and fidelity. Experimental results show that SQH offers the quality scalability feature with very limited or no compression performance penalty at all when compared with the corresponding non-scalable solution, thus preserving the significant compression gains over other state-of-the-art PC codecs.<|reference_end|>
@article{mari2024point,
    title={Point Cloud Geometry Scalable Coding with a Quality-Conditioned Latents
  Probability Estimator},
    author={Daniele Mari, Andr'e F. R. Guarda, Nuno M. M. Rodrigues, Simone
  Milani, Fernando Pereira},
    journal={arXiv preprint arXiv:2404.07698},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.07698},
    primaryClass={cs.LG cs.AI cs.CV}
}