Efficient inter partitioning of versatile video coding based on supervised contrastive learning

Nowadays, Versatile Video Coding (VVC) has achieved a superior performance than previous video coding standard (High Efficiency Video Coding). The Quadtree with Nested Multi-Type Tree (QTMT) coding block structure can enhance the coding performance. Nevertheless, this technique also leads to the significantly increasing complexity of VVC inter coding. Therefore, complexity optimization is an urgent problem to be optimized in the market application of VVC. To solve this issue, we propose a Supervised-Contrastive-Learningbased Inter Partitioning (SCLIP) method in this paper. Firstly, we define the above complexity optimization problem as a supervised classification task. Next, we develop a SCLIP Estimation Network (SCLIPEst-Net) with a supervised contrastive learning module and a classification module. After training on a newly established dataset, the SCLIPEst-Net can reasonably predict the mode partitioning. Finally, we propose an overall SCLIP algorithm that effectively determines the inter partitions of VVC with a low computational overhead. Experimental results indicate that our method achieves 45.14% average Time Saving (TS) with a 2.40% Bj & oslash;ntegaard Delta Bit Rate (BDBR) in Random Access (RA), outperforming the benchmarks.