Efficient VVC Intra Partitioning With Shared Feature Extraction and Complexity-Aware Threshold Decision

Versatile Video Coding (VVC) achieves significant compression efficiency by adopting various techniques, especially the Quad-Tree Plus Multi-Type Tree (QTMT) partition structure, but this leads to a dramatic rise in encoding complexity. Some accelerating methods have relatively low accuracy in predicting partition modes, while others increase accuracy at the cost of larger network inference time. When setting thresholds for skipping partition modes, it is common to determine them empirically or based on prediction accuracy, often neglecting their impact on encoding complexity. To achieve a balance in accuracy and efficiency, we propose to only extract the feature map of each 32×32 CU once and share it among sub-CUs. Padding between layers is removed to keep consistency between training and inference. Considering that encoding complexity of each partition mode differs, an algorithm is designed to search for the optimal thresholds with a balance between RD performance and encoding complexity. Then, we integrate a gradient-based method and a novel partition mode reuse method into VTM, which achieve a better RD-complexity trade-off at a large time-saving rate. Lastly, we remove several existing accelerating methods in VTM and find that our method is a good substitute. Experiments implemented single-threaded on CPU demonstrate that our method can reduce 25.29%$\sim$73.98% complexity of VVC intra encoding with -0.04%$\sim$1.93% BDRate increase. All the code is available athttps://github.com/cppppp/FastIntra.