A Lightweight Learning-Based QTMT Decision Framework for VVC Inter-Coding

The VVC standard achieves high compression efficiency through its flexible QTMT partitioning structure, at the cost of significantly increased encoding complexity. In this paper, a fast QTMT partition decision method for VVC inter-coding is proposed to reduce computational complexity while preserving rate–distortion efficiency. The proposed approach exploits texture characteristics derived from GLCM analysis to guide partitioning decisions. A feature selection process identifies homogeneity as the most relevant descriptor for characterizing partitioning behavior. Based on this descriptor, a GBM model is trained to learn adaptive decision thresholds that enable a homogeneity-driven restriction of QTMT partition candidates. By progressively limiting unnecessary partition evaluations according to local texture properties, the proposed method reduces the reliance on exhaustive rate–distortion optimization through a lightweight and content-aware decision strategy. Experimental results demonstrate that the proposed approach achieves substantial encoding time reduction with negligible impact on coding performance.