Introducing Latent Space Correlation to Conditional Autoencoders for Intra Prediction
Fabian Brand, Jürgen Seiler, André Kaup · 2020
Intra prediction has been an integral part of image and video coders for a long time. A predominant method is angular prediction that extends the reference area in a certain angle into the block. Recently many deep-learning-based methods have been proposed. Since intra prediction uses multiple modes this usually requires training a large number of networks. With a conditional autoencoder we are able to generate an arbitrary number of modes with only one network. In this paper we introduce a novel loss function enforcing a spatially correlated latent space and extend the network structure to the same end. Thereby we are able to propose a simple spatial mode prediction scheme using most-probable-mode lists. By replacing matrix-based intra prediction in VVC with our method, we obtain average rate savings of 0.84% with peak gains of 2.37%.