Title: Dual Learning-based Video Coding with Inception Dense Blocks Show Chinese title

Title: 基于双学习的视频编码与Inception密集块

Abstract: In this paper, a dual learning-based method in intra coding is introduced for PCS Grand Challenge. This method is mainly composed of two parts: intra prediction and reconstruction filtering. They use different network structures, the neural network-based intra prediction uses the full-connected network to predict the block while the neural network-based reconstruction filtering utilizes the convolutional networks. Different with the previous filtering works, we use a network with more powerful feature extraction capabilities in our reconstruction filtering network. And the filtering unit is the block-level so as to achieve a more accurate filtering compensation. To our best knowledge, among all the learning-based methods, this is the first attempt to combine two different networks in one application, and we achieve the state-of-the-art performance for AI configuration on the HEVC Test sequences. The experimental result shows that our method leads to significant BD-rate saving for provided 8 sequences compared to HM-16.20 baseline (average 10.24% and 3.57% bitrate reductions for all-intra and random-access coding, respectively). For HEVC test sequences, our model also achieved a 9.70% BD-rate saving compared to HM-16.20 baseline for all-intra configuration. Show Chinese abstract

Abstract: 在本文中，引入了一种基于双学习的内部编码方法用于PCS大挑战。 该方法主要由两部分组成：内部预测和重建滤波。 它们使用不同的网络结构，基于神经网络的内部预测使用全连接网络来预测块，而基于神经网络的重建滤波则利用卷积网络。 不同于之前的滤波工作，我们在重建滤波网络中使用了一个具有更强特征提取能力的网络。 并且滤波单元是块级别的，以实现更精确的滤波补偿。 据我们所知，在所有基于学习的方法中，这是首次在一个应用中结合两种不同的网络，我们在HEVC测试序列上实现了AI配置的最先进性能。 实验结果表明，与HM-16.20基线相比，我们的方法在提供的8个序列中显著降低了BD率（平均所有帧内编码减少了10.24%，随机访问编码减少了3.57%）。 对于HEVC测试序列，我们的模型在所有帧内配置中也比HM-16.20基线提高了9.70%的BD率节省。