相比于高效视频编码(high efficiency video coding,HEVC)标准,新一代编码标准多功能视频编码(versatile video coding,VVC)引入了很多新的技术,其中包括四叉树(quadtree,QT)和多类型树(multi-type tree,MTT)划分,MTT划分由HEVC中的QT划分延伸而来.新划分方法提高了压缩效率,但导致编码时间急剧增加.为了降低编码复杂度,提出了一种结合深度学习方法和MTT方向早期判决的快速帧内编码算法.首先使用轻量级的卷积神经网络(convolutional neural network,CNN)对QT和部分MTT进行预测划分,其余MTT则采用提前预测MTT划分方向的方法作进一步的优化.实验结果表明,所提方法能够大幅降低编码复杂度,相比于原始编码器的编码时间减少了74.3％,且只有3.3％的码率损失,性能优于对比的方法.
A VVC intra coding method based on fast partition for coding unit
Compared to the high efficiency video coding (HEVC) standard,the latest generation coding standard,ver-satile video coding (VVC) has introduced many new technologies,including quadtree (QT) and multi-type tree (MTT) partitioning. MTT partition is extended from QT partition in HEVC. The new partition method increases en-coding complexity,leading to a sharp increase in encoding time. To reduce encoding complexity,a fast intra coding method combining deep learning methods and early decision in the MTT direction was proposed. Firstly,a light-weight convolutional neural network (CNN) network was used to predict partition for QT and partial MTT. Then,an early prediction for MTT partition direction method was adopted for further optimization of residual MTT. Experi-mental results show that the proposed method can significantly reduce encoding complexity,with a 74.3％ reduction in encoding time compared to the original encoder with only 3.3％ rate loss. Moreover,the performance of proposed method is superior to other comparative algorithms.
VVCintra codingconvolutional neural networkfast codingquadtreemulti-type tree
万方数据
维普
