Edge-directed geometric partitioning for versatile video coding
2026-06-01 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors propose a new way to predict certain coding options in video compression to make it more efficient. They focus on using information about edges in images and nearby blocks in space and time to guess the best geometric partition mode without sending extra data overhead. This method improves compression performance and makes edges in the video look better. Their tests show a small but meaningful improvement in coding efficiency compared to a standard reference.
Geometric Partition (GEO)Versatile Video Coding (VVC)Coding Unit (CU)Most Probable Mode (MPM)Edge DetectionSpatio-temporal CorrelationBD-rateVideo CompressionPartition ModeVTM-6.0
Authors
Xuewei Meng, Xinfeng Zhang, Chuanmin Jia, Xia Li, Shanshe Wang, Siwei Ma
Abstract
To improve the coding performance, geometric partition (GEO) was proposed for the upcoming VVC standard. GEO provides 140 partition candidates. The index of optimal GEO mode needs to be signaled explicitly. Considering different structural characteristics of different CUs and the correlation between spatial adjacent blocks and temporal collocated blocks, we propose a GEO mode prediction strategy by constructing a Most Probable Mode (MPM) list to reduce the overhead of GEO index and improve coding efficiency. Based on the observation of the high correlation between the partition mode and object boundaries, an edge-directed geometric partition scheme is proposed to construct the MPM list according to spatio-temporal edge information. The proposed method provides an objective BD-rate gain of 0.58% and 1.00% on average for RA and LDB configurations compared to VTM-6.0. Besides, it also promotes the visual quality of object boundaries.