针对多功能视频编码(versatile video coding,VVC)帧内编码中编码单元(coding units,CU)划分存在计算复杂度过高的问题,本文提出了一种基于DenseNet+FPN(feature pyramid network)的CU快速划分算法。该算法能够大幅度降低VVC的编码复杂度,减少编码时间。首先,本文提出了一种基于纹理复杂度的CU分类算法,来评估CU块的纹理复杂度。其次,提出一种基于DenseNet+FPN的网络模型,利用多尺度信息来优化CU划分,以适应多尺度情况下的编码需求。最后,设计了一个新的自适应的质量复杂度均衡损失函数,用于平衡编码质量和计算复杂度。所提算法进行了大量的实验分析,结果证明,与公共参考软件(WC test model 10.0,VTM10.0)相 比,所提算法的帧内编码平均时间减少了44.268％,而BDBR(bjntegaard delta bit rate)仅增加了0.94％。

To address the issue of high computational complexity in coding units(CU)partitioning for versatile video coding(VVC)intra-frame coding,this paper proposes a CU fast partitioning algorithm based on DenseNet+FPN (feature pyramid network).The algorithm significantly reduces the encoding complexity of VVC,resulting in reduced encoding time.Firstly,a CU classification algorithm based on texture complexity is proposed to evaluate the texture complexity of CU blocks.Secondly,a network model based on DenseNet+FPN is introduced,utilizing multi-scale information to optimize CU partitioning to adapt to encoding requirements in various scales.Lastly,a novel adaptive quality-complexity balanced loss function is designed to balance encoding quality and computational complexity.Extensive experimental analysis is conducted for the proposed algorithm,and the results demonstrate that compared to VVC test model(VTM)10.0,the average encoding time of the proposed algorithm is reduced by 44.268%,while the bjntegaard delta bit rate (BDBR) only increases by 0.94%.

TN919.8