Learning-Based QP Initialization for Versatile Video Coding

Versatile Video Coding (VVC) is a modern video compression standard designed to efficiently encode high definition video content, regardless of its diversity. It is expected to deliver superior compression performance compared to the previous standard, High Efficiency Video Coding (HEVC). However, the bit rate control problem for VVC can still be improved. To address this issue, a learning-based initial frame Quantization Parameter (QP) prediction algorithm has been proposed in this paper. This algorithm extracts information from image pixels and maps it to a feature matrix to reduce its additional cost. Furthermore, the problem of inaccurate determination of VVC QPs has been addressed by building a residual network to represent the frame complexity progressively and learning the optimal relationship between QPs and the target bit rate. Experimental results show that the proposed method reduces the control error from 10.74% to 7.19% compared to the original encoder.