A Quantization-Adaptive Early Termination Method for Fast Coding Unit Partitioning in VVC

Versatile Video Coding (VVC) achieves higher compression efficiency than the previous High Efficiency Video Coding (HEVC) standard by employing advanced coding tools, including Quad Tree (QT) and Multi-Type Tree (MTT) block partitioning, extended intra prediction modes, and affine motion compensation. Among these tools, the QT-MTT hierarchical partitioning structure significantly increases encoder complexity, since Rate-Distortion Optimization (RDO) must be performed over an exponentially growing number of partition candidates. To mitigate this complexity, a quantization-adaptive early termination method is proposed that combines neural network-based and rule-based partitioning strategies. The proposed decision mechanism significantly reduces the number of Coding Unit (CU) partition candidates, which directly lowers the number of required RDO evaluations and overall encoder complexity. Experimental results demonstrate that the proposed method achieves a 38.28% reduction in encoding time with only a 0.85% increase in Bjøntegaard Delta Bitrate (BD-BR) under the VVC common test conditions. These results indicate that the proposed method effectively balances computational complexity and rate-distortion performance.