An Online SVM Based VVC Intra Fast Partition Algorithm With Pre-Scene-cut Detection
Chao Shu, Chao Yang, Ping An · 2022
The new generation of video coding standard, Versatile Video Coding (H.266/VVC), brings tremendous computational complexity by incorporating the quad-tree with nested multi-type tree (QTMT) partition structure. We propose an adaptive low loss fast algorithm to tackle this disadvantage by using the online Support Vector Machine (SVM) classifier. Firstly, we perform a pre-scene-cut detection before encoding the whole sequence to split it into several scenes, which divide frames into training-frame and predicting-frame. Then, the training-frame is used to construct the data set for SVM parameters training. Specifically, we extract partition-related features, i.e., gradient, entropy, and difference of neighbor area depth to train the SVM classifier. Lastly, the partition decision in predicting-frame is accelerated by the SVM classifier model in the same scene with the training-frame. Besides, we control our algorithm to maintain a low Bjontegaard Delta Bit Rate (BDBR) index by applying the SVM classifiers in the most suitable size 32x32. The experimental results show that our algorithm achieves about 15.76% encoding time saving on average with a negligible quality loss-0.23% BDBR increase under all-intra configuration.