Abstract
The Moving Picture Experts Group (MPEG) video-based point cloud compression (V-PCC) standard encodes a dynamic point cloud by first converting it into one geometry video and one color video and then using a video coder to compress the two video sequences. We first propose analytical models for the distortion and bitrate of the V-PCC reference software, where the models’ variables are the quantization step sizes used in the encoding of the geometry and color videos. Unlike previous work, our analytical models are functions of the quantization step sizes of all frames in a group of frames. Then, we use our models and an implementation of the differential evolution algorithm to efficiently minimize the distortion subject to a constraint on the bitrate. Experimental results on six dynamic point clouds show that, compared to the state-of-the-art, our method achieves an encoding with a smaller error to the target bitrate (4.65% vs. 11.94% on average) and a slightly lower rate-distortion performance (on average, the increase in Bjontegaard delta (BD) distortion is 0.27, and the increase in BD rate is 8.40%).
Highlights
A static point cloud is a representation of a three-dimensional object, where in addition to the spatial coordinates of a sample of points on the surface of the object, attributes such as color, reflectance, transparency, and normal direction may be used
We observe that the bitrates and distortions computed by our models have a high squared correlation coefficient (SCC) and a low root mean squared error (RMSE) with the actual values computed by encoding and decoding point clouds
We proposed analytical distortion and rate models for video-based point cloud compression (V-PCC) that include the geometry and color quantization steps of all frames in a group of frames
Summary
A static point cloud is a representation of a three-dimensional object, where in addition to the spatial coordinates of a sample of points on the surface of the object, attributes such as color, reflectance, transparency, and normal direction may be used. In V-PCC, the input point cloud is first decomposed into a set of patches, which are independently mapped to a two-dimensional grid of uniform blocks. This mapping is used to store the geometry and color information as one geometry video and one color video. QM−1} and a dynamic point cloud consisting of N frames, an optimal encoding can be obtained by determining for each frame i QM−1} that minimize the distortion subject to a constraint RT on the total number of bits This can be formulated as the multi-objective optimization problem min [Dg(Qg, Qc), Dc(Qg, Qc)]. In the latest MPEG V-PCC test model [8], for example, the QPs for the geometry and color are selected manually: one chooses the QPs of the first frame, and the QP values of the following frames are set according to some fixed rules (e.g., by using the same values for the low delay configuration)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
