Fast Coding Unit Partition Decision for Intra Prediction in Versatile Video Coding




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Peer reviewed



In recent years, the state-of-the-art video coding standard - Versatile Video Coding (VVC) has been widely investigated. VVC achieves impressive performance by adopting more flexible partitioning method compared to its predecessor High Efficiency Video Coding (HEVC). However, the superior performance is realized at the expense of huge time consumption and increasing hardware costs, which obstructs its applications in real-time scenarios. To ad- dress this problem, we present a fast implementation for the decision process of the nested multi-type tree (QTMT) partitioning, and it significantly reduces the run-time of encoder while maintaining almost the same coding performance. Firstly, the inherent texture property of source frame is utilized to identify the prediction depth for Coding Tree Unit (CTU). Then, the spatial correlation is used to further narrow the depth range down. Finally, we skip unnecessary par- tition types according to the predicted Coding Unit (CU) depth, which is deter- mined by the above predicted CTU depth and adjacent CU’s depth together. Experimental results demonstrate the effectiveness of our proposed method in VVC Test Model (VTM). Compared with the original implementation of the VTM4.0 anchor, the proposed algorithm achieves an average of 49.01% encoding time savings, accompanied by only an increase of 2.18% in Bj ntegaard delta Bitrate (BDBR) and a loss of 0.138dB in Bjontegaard delta PSNR (BDPSNR).


The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.


Partition Decision, Spatial Correlation, Texture Property, Versatile Video Coding


Zhang, M., Chen, Y., Lu, X., Chen, H., Zhang, Y. (2021) Fast Coding Unit Partition Decision for Intra Prediction in Versatile Video Coding. In: Peng, Y., Hu, S.M., Gabbouj, M., Zhou, K., Elad, M., Xu, K. (Eds.) Image and Graphics, ICIG 2021, Cham: Springer.


Research Institute

Institute of Artificial Intelligence (IAI)