Improving Optimal Binarization with Update On-the-fly in G-PCC Entropy Coding: Probability Initialization and Adaptive Bounds Setting for Context Models

Geometry-based point cloud compression (G-PCC) uses Context-based Adaptive Binary Arithmetic Coding to encode the geometry and attribute information. The context information is built in context models for entropy coding. G-PCC adopts the Optimal Binarization with Update On-the-fly (OBUF) to reduce the number of context models. In the current design, however, the probability initialization for both fine-and coarse-grained contexts does not follow the principle of entropy continuation. Moreover, the mapping process to produce coarse-grained contexts is a combination of several fine-grained contexts, leading to an unstable update of probability for coarse-grained contexts, which affects the accuracy of the fine-grained context model in probability estimation.To address the underlying problems, we propose two approaches to improve OBUF: initializing the probabilities for fine-grained and coarse-grained contexts according to entropy continuation and setting the probability update upper and lower bounds for coarse-grained contexts adaptively. The experimental results demonstrate that the proposed technique is more consistent with the underlying principles of OBUF and significantly improves the performance of both octree-based and Trisoup-based geometry coding. Due to the theoretical consistency and outstanding performance, the proposed methods have been adopted into the state-of-the-art G-PCC.