A GCN-based fast CU partition method of intra-mode VVC

Abstract

In this paper, a global convolutional network (GCN)-based fast coding unit (CU) partition method of intra-mode VVC is proposed. By using the GCN module with large kernel size convolutions, the proposed method can capture global information in CUs, leading to an accurate partition mode prediction in the quad-tree plus multi-type tree (QTMT) structure. Ranked according to predicted probabilities, the partition modes with lower probabilities are discarded, which reduces the computational complexity of VVC. Additionally, tradeoffs between performance and complexity can be achieved with different strategies. Experimental results demonstrated that the proposed method can reduce encoding time by 51.06%∼61.15% while increasing Bjøntegaard delta bit-rate (BD-BR) by 0.84%∼1.52% when implemented in VTM 10.0, outperforming the state-of-the-art methods, and that the proposed method can be used to accelerate VVenC 1.0 at the preset slower, achieving higher performance and lower complexity compared with the original VVenC 1.0 at the presets slow and medium.