Enhanced Temporal Consistency for Global Patch Allocation in Video-Based Point Cloud Compression

Jui-Chiu Chiang, Yu-Tze Wu, Hsin-Yun Hsieh, Yun-Chang Tsai

Abstract: Video-based point cloud compression (V-PCC) is a promising technique for compressing 3D point clouds. V-PCC projects the 3D point cloud into patches and encodes the generated 2D images using state-of-the-art video codecs. To maintain temporal consistency between frames, V-PCC supports global patch packing methods and one notable approach is Global Patch Allocation (GPA), which packs the global matched patches into the same location in each frame across the sequence. Additionally, frames are subdivided into groups (i.e., sub-contexts) to balance packing compactness and patch similarity within the groups. While video coding typically employs a Group of Picture (GOP) as the basic unit for encoding, GPA in V-PCC currently does not consider the reference relationship between images within or between GOPs, resulting in limited similarity between the current and the reference images, ultimately leading to reduced encoding efficiency. This paper presents an improved technique for GPA. We propose a dynamic sub-context and GOP determination technique, enhancing the similarity between images within the same GOP. Furthermore, we introduce a priority-based patch packing (PBPP) technique to reduce differences between frames in adjacent GOPs. Experimental results demonstrate the superiority of our proposed method over the anchor, achieving an average BD-rate savings of 3.09%, 3.04%, and 2.33% for D1-PSNR, D2-PSNR, and Y-PSNR, respectively.