Dense Inception Attention Neural Network for InLoop Filter Recently, deep learning technology has made significant progresses in high efficiency video coding (HEVC), especially in inloop filter. In this paper, we propose a dense inception attention network (DIA_Net) to delve into image information and model capacity. The DIA_Net contains multiple inception blocks which have different size kernels so as to dig out various scales information. Meanwhile, attention mechanism including spatial attention and channel attention is utilized to fully exploit feature information. Further we adopt a dense residual structure to deepen the network. We attach DIA_Net to the end of inloop filter part in HEVC as a postprocessor and apply it to luma components. The experimental results demonstrate the proposed DIA_Net has remarkable improvement over the standard HEVC. With allintra(AI) and random access(RA) configurations, It achieves 8.2% bdrate reduction in AI configuration and 5.6% bdrate reduction in RA configuration.

Dual Learningbased Video Coding with Inception Dense Blocks In this paper, a dual learningbased 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 networkbased intra prediction uses the fullconnected network to predict the block while the neural networkbased 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 blocklevel so as to achieve a more accurate filtering compensation. To our best knowledge, among all the learningbased methods, this is the first attempt to combine two different networks in one application, and we achieve the stateoftheart performance for AI configuration on the HEVC Test sequences. The experimental result shows that our method leads to significant BDrate saving for provided 8 sequences compared to HM16.20 baseline (average 10.24% and 3.57% bitrate reductions for allintra and randomaccess coding, respectively). For HEVC test sequences, our model also achieved a 9.70% BDrate saving compared to HM16.20 baseline for allintra configuration.