Effective QTMT Partition Decision Algorithm for VVC Intercoding
Liquan Shen, Hao Yang, Shiwei Wang · 2022 IEEE 24th International Workshop on Multimedia Signal Processing (MMSP) · 2022
Aiming at the problem of high complexity of Versatile Video Coding (VVC) inter coding, this paper proposes a QTMT partition decision algorithm based on a multi-level decision framework. Specifically, the multi-level decision framework decomposes the multi-mode partition decision problem into multiple independent single-mode partition decision problems and then adopts a classification method based on machine learning to predict result of each single mode partition. To design more efficient classification features, fast motion estimation on 4×4 blocks is first performed to construct a motion field, and features on global/local motion and global/local consistency of its corresponding residuals are designed to measure motion activity and texture homogeneity. Furthermore, a misclassification protection mechanism is designed to decrease influences of misclassification on coding performance loss. Experimental results show that the proposed effective QTMT partition decision algorithm achieves a computational complexity reduction more than 51%, while incurring 1.65% BDBR increase compared with that of the original coding in the test model of VVC(VTM).