Intra Frame Prediction for Video Coding Using a Conditional Autoencoder Approach Intra prediction is a vital component of most modern image and video codecs. State of the art video codecs like High Efficiency Video Coding (HEVC) or the upcoming Versatile Video Coding (VVC) use a high number of directional modes. With the recent advances in deep learning, it is now possible to use artificial neural networks for intra frame prediction. Previously published approaches usually add additional ANN based modes or replace all modes by training several networks. In our approach, we use a single autoencoder network to first compress the original with help of already transmitted pixels to four parameters. We then use the parameters together with this support area to generate a prediction for the block. This way, we are able to replace all angular intra modes by a single ANN. In the experiments we compare our method with the intra prediction method currently used in the VVC Test Model (VTM). Using our method, we are able to gain up to 0.85 dB prediction PSNR with a comparable amount of side information or reduce the amount of side information by 2 bit per prediction unit with similar PSNR.

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.