Versatile video coding is a new video coding standard that has more capabilities and higher coding efficiency compared with its predecessor. Practical video storage and transmission applications face constrained buffer size and available bandwidth. It is necessary to design the appropriate rate control algorithm to overcome such challenges. In this paper, the non-linear relationship between consumed bits, buffer size, and quantization parameter is estimated by taking the advantages of artificial neural networks, and a rate control algorithm is developed for real-time variable bit rate applications of the versatile video coding standard. The proposed rate control algorithm performs the control action in only one step that results in faster control action. The experimental results show that the proposed algorithm controls the bit-rate as well as the buffer state. Also, the rate–distortion analysis shows that the well-known $\lambda$-domain algorithm has only 2.7% bit-rate reduction in comparison with the proposed method.

多功能视频编码是一种新的视频编码标准，与以前的视频编码标准相比，它具有更多的功能和更高的编码效率。实际的视频存储和传输应用面临受限的缓冲区大小和可用带宽。有必要设计适当的速率控制算法来克服这些挑战。本文利用人工神经网络的优势来估计消耗比特，缓冲区大小和量化参数之间的非线性关系，并为通用视频编码的实时可变比特率应用开发了一种速率控制算法。标准。所提出的速率控制算法仅一步执行控制动作，从而导致更快的控制动作。实验结果表明，该算法控制了比特率和缓存状态。此外，速率失真分析表明$\lambda$与提出的方法相比，域算法仅减少了2.7％的比特率。