Effective VVC Intra Prediction Based on Ensemble Learning

This paper proposes a fast VVC coding unit partition algorithm based on ensemble convolutional neural network (CNN) by investigating and bagging spatial-temporal adjacent coding features. First, we propose an ensemble CNN framework to aggregate the reference features to predict the depths of uncoded CUs. The proposed model consists of three lightweight CNNs, which can compromise prediction accuracy with overhead. Then a majority voting mechanism is used to unify the predicted depth. By extracting the majority prediction of base learners, the outputs of three CNNs are integrated to obtain the final prediction. To avoid Rate Distortion (RD) loss caused by a small probability of prediction failure, we introduce the optimal depth strategy. During the encoding process, the optimal depth is used for the decision-making of coding unit partition, thus avoiding redundant rate distortion optimization process. Compared with the original encoder, the proposed algorithm saves 21.56% encoding time on average, with a BDBR loss of 0.39%. The performance is even superior in High-Definition (HD) and Ultra HD (UHD) sequences, up to 59.52%. This approach has a great efficiency of time reduction compared with state-of-the-arts with negligible RD performance loss.