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Learning-Based Rate Control for Video-Based Point Cloud Compression
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AI
DOI：10.1109/TIP.2022.3152065.png)
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Abstract
En 中文
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.
Keywords:
Point cloud compression
Image coding
Three-dimensional displays
Geometry
Bit rate
Streaming media
Resource management
Video-based point cloud compression
rate control
bit allocation
deep neural network
Journal
IF:
13.7
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1.0W
Citations:
8.4W
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