Observational analysis reduced coding complexity in geometric videos, indicating significant time savings with low quality impact.
Video-based point cloud compression (V-PCC) is a 3D point cloud compression standard that first projects the point cloud from 3D space onto 2D space, thereby generating geometric and attribute videos, and then encodes the geometric and attribute videos using high-efficiency video coding (HEVC). In the whole coding process, the coding of geometric videos is extremely time-consuming, mainly because the division of geometric video coding units has high computational complexity. In order to effectively reduce the coding complexity of geometric videos in video-based point cloud compression, we propose a fast segmentation algorithm based on the occupancy type of coding units. First, the CUs are divided into three categories—unoccupied, partially occupied, and fully occupied—based on the occupancy graph. For unoccupied CUs, the segmentation is terminated immediately; for partially occupied CUs, a geometric visual perception factor is designed based on their spatial depth variation characteristics, thus realizing early depth range skipping based on visual sensitivity; and, for fully occupied CUs, a lightweight fully connected network is used to make the fast segmentation decision. The experimental results show that, under the full intra-frame configuration, this algorithm significantly reduces the coding time complexity while almost maintaining the coding quality; i.e., the BD rate of D1 and D2 only increases by an average of 0.11% and 0.28% under the total coding rate, where the geometric video coding time saving reaches up to 58.71% and the overall V-PCC coding time saving reaches up to 53.96%.
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Li et al. (2025) studied this question.
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