Transform Set Merging for Neural Network-Based Intra Prediction in beyond VVC

Neural Network-Based Intra Prediction (NN-Intra) predicts a block using its reference samples with a neural network. It has been actively studied in Neural Network-Based Video Coding (NNVC) by JVET and was recently adopted in the Enhanced Compression Model (ECM) considering its high prediction performance. Despite efforts to reduce complexity of the NN-Intra made during its adaptation process from NNVC to ECM, its complexity still remains high, and more investigation is needed on the transform process after prediction. This paper explores a method to transform the NN-Intra-coded block using DCT-based transform sets in ECM. In this regard, we merge transform sets for directional and non-directional modes to generate a merged transform set from which a transform kernel pair is selected for the block. Experimental results ECM-15.0 demonstrate that our method achieves a coding gain of 0.01% in luma channel and has encoder and decoder complexity of 100.2% and 99.8%, respectively.