Edge-directed geometric partitioning for versatile video coding

2026-05-31

AI Summary

This work addresses the high bitrate overhead incurred by explicit index coding in geometric partitioning (GEO) modes. To mitigate this cost, the study introduces, for the first time, edge information from spatially and temporally neighboring blocks to construct a Most Probable Mode (MPM) list for predicting the optimal GEO mode. The proposed method integrates spatiotemporal edge detection, MPM list generation, and seamless incorporation into the VVC framework, effectively enhancing both coding efficiency and visual quality around object boundaries. Experimental results demonstrate consistent bitrate savings, achieving BD-rate gains of 0.58% and 1.00% under the Random Access (RA) and Low-Delay B (LDB) configurations, respectively, compared to VTM-6.0.

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.

Problem

• geometric partition
• video coding
• mode prediction
• coding efficiency
• edge information

Innovation

• geometric partitioning
• edge-directed
• most probable mode
• VVC
• coding efficiency