Adaptive CU Split Decision with Poolingvariable CNN for VVC Intra Encoding In the versatile video coding (VVC) proposed by the Joint Video Exploration Team (JVET), the quadtree with the nested multitype tree (QTMT) partition scheme has been adopted based on the quadtree structure in the high efficiency video coding (HEVC). The video coding quality of VVC is better than the HEVC, but the algorithm complexity has also increased greatly. In this work, we present an adaptive CU split decision for intra frame with the poolingvariable convolutional neural network (CNN), targeting at various coding unit (CU) shape. The shapeadaptive CNN is realized by the variable pooling layer size where we can make the most of the pooling layer in CNN and retain the original information. Based on the proposed CNN, the CU split or not will be decided by only one trained network, same architecture and parameters for the CUs with multiple sizes. Moreover, with the proposed shapebased CNN training scheme, the various training sample size can be processed successfully. The CUbased network can avoid the full ratedistortion optimization for the CU split and the CUlevel rate control can also be enabled. The experiment results show that the proposed method can save 33% coding time with only 0.99% Bjontegaard Delta bitrate (BDrate) increase.

Fast QTMT Partition Decision Algorithm in VVC Intra Coding based on Variance and Gradient Quadtree with nested multitype tree (QTMT) partition structure in Versatile Video Coding (VVC) contributes to superior encoding performance compared to the basic quadtree (QT) structure in High Efficiency Video Coding (HEVC). However, the improvement of performance leads to an unavoidable increase of computational complexity. To achieve a balance between coding efficiency and compression quality, we propose a fast intra partition algorithm based on variance and gradient to solve the rectangular partition problem in VVC. First, further splitting of smooth areas is terminated. Then, QT partition is chosen depending on the gradient features extracted by Sobel operator. Finally, one partition from five possible QTMT partitions is directly chosen by computing the variance of variance of subCUs. The theoretical basis of our method is that a homogeneous area tends to be predicted with a larger coding unit (CU), and subparts of a split CU are prone to have different textures from each other. To our knowledge, this is the first attempt to apply traditional method to accelerating the rectangular partition problem in VVC intra prediction. Experimental results show that the proposed method can save averagely 53.17% encoding time with only 1.62% BDBR increase and 0.09dB BDPSNR loss compared to anchor VTM4.0.