A fast VVC intra-coding algorithm based on graph neural network and statistical analysis.

VVC as the latest generation of video coding standards, further improves video compression quality by introducing a variety of efficient coding tools. However, the VVC standard introduces the QTMT division structure and expands the intra prediction modes from 35 to 67, resulting in a sharp increase in coding complexity. Firstly, a fast algorithm for intra-frame coding unit (CU) division based on graph neural network was proposed, in order to reduce the complexity of intra-frame coding of VVC. An efficient graph neural network model was used to directly predict the optimal partition mode of CU, thus skipping redundant CU partition traversal. Secondly, a fast algorithm for intraframe mode selection based on spatial correlation and texture features was proposed. The average direction variance and Sobel gradient operator were used to determine the texture direction, some angle prediction modes were skipped, and the correlation between prediction modes to streamline the rate-distortion mode list were combined. Experimental results show that this algorithm can save 64.04% of encoding time at the cost of increasing BDBR by 2.29%. [ABSTRACT FROM AUTHOR]