In the recent Future Video Coding (FVC) standard developed by the Joint Video Exploration Team (JVET), the quad-tree binary-tree (QTBT) block partition module makes use of rectangular block forms and additional square block sizes compared to quad-tree (QT) block partitioning module proposed in the predecessor High-Efficiency Video Coding (HEVC) standard. This block flexibility, induced with the QTBT module, significantly improves compression performance while it dramatically increases coding complexity due to the brute force search for Rate Distortion Optimization (RDO). To cope with this issue, it is necessary to consider the unique characteristics of QTBT in FVC. In this paper, we propose a fast QT partitioning algorithm based on a deep convolutional neural network (CNN) model to predict coding unit (CU) partition instead of RDO which enhances considerably QTBT performance for intra-mode coding. Based on a suitable diversified CU partition patterns database, the optimization process is set up with three levels CNN structure developed to learn the split or non-split decision from the established database. Experimental results reveal that the proposed algorithm can accelerate the QTBT block partition structure by reducing the intra-mode encoding time by an average of 35% with a bit rate increase of 1.7%, allowing its application in practical scenarios.

在联合视频探索小组（JVET）制定的最新的未来视频编码（FVC）标准中，四叉树二叉树（QTBT）块分区模块与四叉树相比，使用矩形块形式和其他正方形块大小先前的高效视频编码（HEVC）标准中提出的（QT）块分区模块。QTBT模块带来的这种块灵活性，由于对速率失真优化（RDO）的蛮力搜索，大大提高了压缩性能，同时极大地提高了编码复杂性。为了解决这个问题，有必要考虑FVC中QTBT的独特特性。在本文中，我们提出了一种基于深度卷积神经网络（CNN）模型的快速QT分区算法，以预测编码单元（CU）分区而不是RDO，从而大大提高了帧内编码的QTBT性能。基于合适的多样化CU分区模式数据库，通过开发三个级别的CNN结构来建立优化过程，以从已建立的数据库中学习拆分或非拆分决策。实验结果表明，该算法可将帧内编码时间平均缩短35％，比特率提高1.7％，从而加快了QTBT块的分区结构，可在实际应用中应用。优化过程由三级CNN结构建立，以从已建立的数据库中学习拆分或非拆分决策。实验结果表明，该算法可将帧内编码时间平均缩短35％，比特率提高1.7％，从而加快了QTBT块的分区结构，可在实际应用中应用。优化过程由三级CNN结构建立，以从已建立的数据库中学习拆分或非拆分决策。实验结果表明，该算法可将帧内编码时间平均缩短35％，比特率提高1.7％，从而加快了QTBT块的分区结构，可在实际应用中应用。