Efficient Inter-Prediction Method Using Reference Frame Accumulation for MPEG G-PCC
Xin Li, Eun-Young Chang, Jihun Cha, Euee Seon Jang · IEEE Access · 2025
This paper presents a novel hybrid reference frame generation method to enhance the interprediction performance of geometry-based point cloud compression (G-PCC), a recent point cloud coding standard developed by the moving picture experts group (MPEG) that currently employs a single past reference frame for prediction. However, this single-reference approach may not fully capture the temporal and spatial correlation between frames, potentially limiting prediction performance. To address this limitation, we propose a hybrid mode selection scheme that chooses the suitable reference frame from a set of candidate frames, thereby leveraging the spatial and temporal correlation among consecutive past frames more effectively and improving coding efficiency. We introduce and evaluate three coding modes for the hybrid reference frame concept within the G-PCC test model: (1) entropy estimation-based mode selection, which selects the reference frame(s) that minimize the estimated entropy during inter-frame coding; (2) translation-based mode selection, which selects the reference frame(s) requiring the least translation to align with the current frame; and (3) lightweight entropy estimation-based mode selection, which minimizes entropy under a predefined translation constraint. Experimental results demonstrate that all proposed techniques outperform the current G-PCC standard: entropy estimation-based mode selection achieved average improvements of 0.86% in geometric coding efficiency and 0.81% overall. Translation-based mode selection improved these metrics by 0.59% and 0.56%, respectively, and the lightweight entropy estimation approach consistently yielded a 0.7% gain. In specific driving scenarios, all the proposed techniques showed significant improvements, reaching approximately 2.62% to 2.86% in geometric coding efficiency.