Texture-aware fast mode decision and complexity allocation for VVC based point cloud compression

Video-Based Point Cloud Compression (V-PCC) leverages Versatile Video Coding (VVC) to compress point clouds efficiently, yet suffers from high computational complexity that challenges real-time applications. To address this critical problem, we propose a texture-aware fast Coding Unit (CU) mode decision algorithm and a complexity allocation strategy for VVC-based V-PCC. By analyzing CU distributions and complexity characteristics, we introduce adaptive early termination thresholds that incorporate spatial, parent–child, and intra/inter CU correlations. Furthermore, we established a complexity allocation method by formulating and solving an optimization problem to determine relaxation factors for optimal complexity-efficiency trade-offs. Experimental results demonstrate that the proposed fast mode decision achieves an average of 33.89% and 44.59% complexity reductions compared to the anchor VVC-based V-PCC, which are better than those of the state-of-the-art fast mode decision schemes. Meanwhile, the average Bjónteggard Delta Bit Rate (BDBR) loss is 1.04% and 1.64%, which are negligible. • We propose a texture-aware fast mode decision algorithm for geometry and attribute coding in V-PCC by jointly exploiting texture properties and correlations in parent–child, inter-frames, and spatial domain. • We propose a complexity allocation algorithm for the fast mode decision, where a relaxation factor is used to allocate complexity among CU layers. • The proposed mode decision and complexity allocation algorithm is able to achieve a good trade-off between computational complexity and compression efficiency. Experimental results show that the proposed algorithm reduces encoding complexities 33.89% and 44.59% on average while the bit rate only increases 1% and 1.6%, respectively.