Fast CU Decision Algorithm Based on Texture Complexity and CNN for VVC

Versatile Video Coding (VVC) introduces many advanced video coding techniques. These advanced video coding techniques not only improve video compression efficiency and video quality, but also greatly increase coding time and computational complexity. Therefore, this paper uses a fast CU partitioning decision algorithm based on texture complexity and convolutional neural networks (CNN). First, we performed statistics and analysis of CU segmentation patterns for videos in the standard training set, then processed large blocks of CU based on texture complexity. To realize the purpose of fast partitioning, we design different CNN models for CU with different segmentation modes. In the design of CNN model, we use the symmetric convolutional kernel and asymmetric convolutional kernel to extract features in different directions effectively. In the loss function, we used the cross-entropy function to train the CNN model to improve the accuracy of the model. Finally, a double threshold is set in the candidate list to achieve a compromise between coding performance and coding complexity. Experimental results show that, compared to the VTM10.0 anchoring algorithm, our fast scheme, in terms of encoding time, decreases by 55.90% and BDBR increases by 1.79%; our moderate scheme, in terms of encoding time, decreases by 47.90%. BDBR increases by only 1.29%.