Wang Taiyu, Li Fan, Cosman Pamela C
IEEE Trans Image Process. 2022;31:2175-2189. doi: 10.1109/TIP.2022.3152065. Epub 2022 Mar 8.
Due to limited transmission resources and storage capacity, efficient rate control is important in Video-based Point Cloud Compression (V-PCC). In this paper, we propose a learning-based rate control method to improve the rate-distortion (RD) performance of V-PCC. A low-latency synchronous rate control structure is designed to reduce the overhead of pre-coding. The basic unit (BU) parameters are predicted accurately based on our proposed CNN-LSTM neural network, instead of the online updating approach, which can be inaccurate due to low consistency between adjacent 2D frames in V-PCC. When determining the quantization parameters for the BU, a patch-based clipping method is proposed to avoid unnecessary clipping. This approach is able to improve the RD performance and subjective dynamic point cloud quality. Experiments show that our proposed rate control method outperforms present approaches.
由于传输资源和存储容量有限，高效的码率控制在基于视频的点云压缩（V-PCC）中至关重要。在本文中，我们提出了一种基于学习的码率控制方法，以提高V-PCC的率失真（RD）性能。设计了一种低延迟同步码率控制结构，以减少预编码开销。基于我们提出的CNN-LSTM神经网络准确预测基本单元（BU）参数，而不是采用在线更新方法，后者由于V-PCC中相邻二维帧之间的低一致性可能不准确。在确定BU的量化参数时，提出了一种基于块的裁剪方法，以避免不必要的裁剪。该方法能够提高RD性能和主观动态点云质量。实验表明，我们提出的码率控制方法优于现有方法。
