Adaptive Enhanced Global Intra Prediction for Efficient Video Coding in Beyond VVC

Global intra prediction (GIP), including intra-block copy and template matching prediction (TMP), exploits the global correlation of the same image to improve the coding efficiency. In Beyond VVC, TMP uses template matching to determine the reference blocks for efficient prediction. There usually exists an error between the coding block and reference blocks, caused by the content mismatch or the coding distortion of the reference blocks. We propose an enhancement over the reference blocks, namely enhanced GIP (EGIP). Specifically, we design an enhanced filter according to the templates of the coding block and the reference blocks, with the reconstructed template of the coding block as the label for supervised learning. To support different enhancements, we design two types of inputs, i.e., EGIP based on neighboring samples (N-EGIP) and EGIP based on multiple hypothesis references (M-EGIP). Experimental results show that, based on enhanced compression model (ECM) version 8.0, N-EGIP achieves BD-rate reductions of 0.37%, 0.42%, and 0.40%, and M-EGIP brings 0.34%, 0.37%, and 0.34% BD-rate savings for Y, Cb, and Cr components, respectively. A higher coding gain, 0.46%, 0.54%, and 0.52% BD-rate savings, can be achieved by integrating N-EGIP and M-EGIP together. Owing to the coding gain and small complexity increase, the proposed EGIP has been adopted in the exploration of Beyond VVC and integrated into its reference software.