Data-Driven Recursive Intra Prediction

Machine learning-based intra-prediction methods for video coding have gained profound attention in recent years. However, most of such approaches use complex non-linear networks that increase the coding complexity significantly. In this paper, a data-driven recursive intra-prediction (DRIP) method is proposed which applies a linear network at the sub-block level for intra-prediction. Briefly, a given block is partitioned into sub-blocks, and by multiplying the reconstructed or predicted samples present at the top and left of a sub-block with a weight matrix (learned offline) its prediction is obtained. The sub-blocks are predicted recursively, using the same weight matrix, until the prediction of the block is complete. For further coding improvement, multiple such matrices are used resulting in multiple prediction modes for a block. The index of the chosen mode is transmitted to the decoder in the bit-stream. An example implementation of DRIP with 32 modes and 4 × 4 sub-blocks on top of the VVC Test Model (VTM version 23.6) is presented in this paper. It achieves an average Bjøntegaard-delta rate of 1.02% (luma channel) against the anchor using the all-intra− (AI) picture configuration.