A fast H.266/QTMT intra coding scheme based on predictions of learned models

Abstract

ABSTRACT H.266/VVC adopts a QuadTree-plus-MultiType tree (QTMT) coding-unit (CU) split structure to improve efficiency at the cost of high time complexity. Speeding up VVC coding while minimizing quality degradation is critical for practical applications. We propose predicting the coding depth and split type of an optimally coded 32 × 32 CU (CU32 × 32) to perform only a subset of exhaustive rate-distortion optimization (RDO) operations: (1) To predict the depth of an optimally coded CU32 × 32, we train a convolutional neural network (CNNdepth). CNNdepth outputs a label specifying a depth range subset by which the controller can execute EarlySkip or EarlyTerminate to reduce time complexity. (2) To predict the split type, we train random forest classifiers (RFCtype). The corresponding RDO operations can be omitted if the RFCtype classifies one CU32 × 32 as not of a specified split type. Experiments show that CNNdepth and RFCtype work seamlessly, reducing execution time by up to 69% and 39.16% on average, with a 0.7% increase in BDBR compared to the default VTM-7.0. Additionally, the proposed method yields the highest balanced time reduction rate of 61.5%.