Sparse Coding-based Intra Prediction in VVC

Intra prediction is crucial to video coding as it is the only option for prediction when motion compensation either fails or no reference frames are available. Hence, current state-of-the-art video coding standards utilize numerous intra prediction modes in order to predict different angled structures as well as smooth areas. This contribution introduces an additional mode for intra prediction which is based on the concepts of Dictionary Learning (DL), Sparse Coding (SC) [1] and adjusted Anchored Neighborhood Regression (ANR) [2] to be able to adapt to more arbitrary structures. The general idea is built on trained dictionaries, which sparsely represent the reference area of a block to be predicted. Alongside learning the dictionaries, linear projection matrices, projecting the reference areas to the corresponding blocks, are trained with ANR. For the actual intra prediction step, each given reference area is then projected onto the to-be-predicted block by multiple linear projections, which are blended according to the sparse codes representing the reference area. Experimentally, offering the proposed mode to the state-of-the-art video coding standard Versatile Video Coding (VVC) outperforms the traditional VVC modes: In particular, -0.26% BD-rate gains in comparison to the VVC reference software VTM-9.3 and a usage percentage of 12.83% can be achieved on average for the All Intra (AI) coding configuration. Furthermore, a peak coding gain of -0.6% and a usage percentage of 26.66% is observed for the same setup.