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.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Journal of Visual Communication and Image Representation
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
