Plenoptic Point Cloud Compression Using Multiview Extension of High Efficiency Video Coding

Li, Li; Zhu, Li; Liu, Shan; Li, Houqiang

doi:10.1109/tmm.2022.3142528

Cited by 4 publications

(1 citation statement)

References 31 publications

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“…With the rapid development of three-dimensional (3D) capturing and reconstruction technologies, 1 point clouds have been widely used in many applications, including virtual reality, augmented reality, immersive communication, architecture, and autonomous driving 2 – 7 Point clouds consist of millions of or even more points represented by geometry and attributes (such as color, reflectance, and normal vectors), 8 , 9 which can provide better immersive experience for free-viewpoint rendering 10 as well as the mixing of natural and synthetic objects. However, this also results in a large amount of data that needs to be efficiently stored and transmitted, thus point cloud compression is becoming an urgent task to be addressed 11 , 12 .…”

Section: Introductionmentioning

confidence: 99%

Rate-distortion optimized quantization for geometry-based point cloud compression

et al. 2023

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.Limited by the network bandwidth, three-dimensional (3D) point cloud needs to be efficiently compressed before transmission. As one of the three attribute coding methods adopted in the geometry-based point cloud compression (G-PCC) standard developed by MPEG, predicting transform (PT) has received increasing attention. To further improve the coding efficiency of PT, we propose a rate-distortion optimized quantization (RDOQ) in which an additional option for quantization results is added by setting the quantized residuals to zero forcedly. Rate-distortion optimization is then used to determine whether the final quantized residual should be zero or nonzero. Experimental results show that average BD rates of −0.5 % , −3.0 % , and −3.0 % can be achieved for Luma, Chroma Cb, and Chroma Cr components, respectively, with negligible increment of time complexity.

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