Luma Background Restoration for Semantic Segmentation in Video Coding for Machines

The Moving Picture Experts Group (MPEG) is developing the Video Coding for Machines (VCM) standard to support efficient video compression for machine vision tasks. The VCM standard primarily targets object detection, tracking, and semantic segmentation. Since VCM mainly focuses on object-centric tasks such as detection and tracking, it employs Region-of-Interest (ROI) coding to allocate more bits to foregrounds, while suppressing background regions. This suppression reduces segmentation accuracy by degrading contextual background information. To address this limitation, we propose a luma background restoration method that reconstructs degraded background regions by exploiting the structural correlation between decoded luma and chroma components without relying on complex chroma modeling. The proposed method integrates multi-channel linear modeling with context-based arithmetic coding to efficiently transmit grouped Linear Model (LM) indices for luma restoration. Under VCM test conditions, experimental results show that the proposed method achieves an average Bjøntegaard Delta mean Intersection-over-Union (BD-mIoU) of 7.70, compared with 7.41 achieved by the latest background preservation method. These results demonstrate that the proposed method effectively restores structural background details in luma regions essential for semantic segmentation in VCM frameworks.