SVM Based Fast CU Partitioning Algorithm for VVC Intra Coding

Abstract

Recently, Joint Video Experts Team (JVET) has completed the new Versatile Video Coding (H.266/VVC) standard. VVC employs a new block partition structure named quad-tree with nested multi-type tree (QTMT) to improve coding efficiency. However, the new block partition structure increases huge encoding time compared with HEVC for brute-force ratedistortion (RD) optimization. To reduce encoding complexity, we propose a Support Vector Machine (SVM) based fast CU partitioning algorithm for VVC intra coding in this paper which terminates redundant partitions early by predicting the partition of CU using texture information. We trained classifiers for CUs of different sizes to improve accuracy and control the complexity of the classifiers themselves. Different thresholds are set for each classifier to achieve a trade-off between encoding complexity and RD performance. Experimental results show that the proposed method can save encoder time ranging from 30.78% to 63.16% with 1.10% to 2.71% BD-BR increase.

Publication
2021 IEEE International Symposium on Circuits and Systems (ISCAS)
Guoqing Wu
Guoqing Wu
Master Student
Yan Huang
Yan Huang
PhD Student
Chen Zhu
Chen Zhu
PhD Student

I’m a Research PHD candidate at SJTU Media Lab. My research interest is video coding optimization, under the direction of Prof. Li Song.

Li Song
Li Song
Professor, IEEE Senior Member

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