Dictionary Learning based High Frequencynon-linear Prediction for Video Coding

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High detail prediction and reconstruction is a crucial task to increase efficiency of video coding algorithms. Within this project, it will be investigated how dictionary learning (DL) approaches going beyond linear signal processing theory can improve over the state of the art in video compression for providing improved prediction and reconstruction. Herein, DL based methods are preferred against deep neural networks, since their complexity is lower such that they can also be used in video decoders. In this context, we will investigate how DL / sparse coding methods are influenced by coding artifacts. On this basis, DL approaches for coding noise removal will be developed. Further, the project targets utilizing DL based super resolution (SR) for subpixel interpolation in motion compensated prediction. Moreover, a coding scheme along the concept of dynamic resolution conversion using SR will be set up and investigated. In the context of intra prediction, dictionary learning based approaches shall be developed as an alternative to conventional schemes. A last goal of the project is to develop methods further decreasing the complexity of DL based algorithms.