Dictionary learning based high frequency inter-layer prediction for scalable HEVC
Jens Schneider, Johannes Sauer, Mathias Wien · 2017
Image scale-up is a crucial task in resolution varying scalable video coding, as the coding costs for the enhancement layer depend heavily on the prediction signal generated by inter-layer prediction. In order to generate a suitable prediction signal the missing high frequencies in the base layer picture have to be reconstructed. For this purpose upscaling methods which go beyond the classical sampling theory are required. In this paper, an image scale-up method based on dictionary learning and sparse coding techniques for inter-layer prediction in scalable video coding is presented. Experimental results show that the proposed method outperforms state of the art scalable coding models in the case of 2x upscaling. In more detail, 2.35 % BD-rate savings against SHM 12.0 reference software are observed on average for an All Intra coding configuration. The maximum achieved rate savings were 6 15 % for the sequence PeopleOnStreet.