Rate distortion optimization plays a very important role in image/video coding. But for 3D point cloud, this problem has not been investigated. In this paper, the rate and distortion characteristics of 3D point cloud are investigated in detail, and a typical and challenging rate distortion optimization problem is solved for 3D point cloud. Specifically, since the quality of the reconstructed 3D point cloud depends on both the geometry and color distortions, we first propose analytical rate and distortion models for the geometry and color information in video-based 3D point cloud compression platform, and then solve the joint bit allocation problem for geometry and color based on the derived models. To maximize the reconstructed quality of 3D point cloud, the bit allocation problem is formulated as a constrained optimization problem and solved by an interior point method. Experimental results show that the rate-distortion performance of the proposed solution is close to that obtained with exhaustive search but at only 0.68% of its time complexity. Moreover, the proposed rate and distortion models can also be used for the other rate-distortion optimization problems (such as prediction mode decision) and rate control technologies for 3D point cloud coding in the future.

The real-world applications of 3D point clouds have been growing rapidly in recent years, but not much effective work has been dedicated to perceptual quality assessment of colored 3D point clouds. In this work, we first build a large 3D point cloud database for subjective and objective quality assessment of point clouds. We construct 20 high quality, realistic, and omni-directional point clouds of diverse contents. We then apply downsampling, Gaussian noise, and three types of compression algorithms to create 740 distorted point clouds. We carry out a subjective experiment to evaluate the quality of distorted point clouds. Our statistical analysis finds that existing objective point cloud quality assessment (PCQA) models only achieve limited success in predicting subjective quality ratings. We propose a novel objective PCQA model based on the principle of information content weighted structural similarity. Our experimental results show that the proposed model well correlates with subjective opinions and significantly outperforms the existing PCQA models.

In rate-distortion optimization, the encoder settings are determined by maximizing a reconstruction quality measure subject to a constraint on the bitrate. One of the main challenges of this approach is to define a quality measure that can be computed with low computational cost and which correlates well with the perceptual quality. While several quality measures that fulfil these two criteria have been developed for images and videos, no such one exists for point clouds. We address this limitation for the video-based point cloud compression (V-PCC) standard by proposing a linear perceptual quality model whose variables are the V-PCC geometry and color quantization step sizes and whose coefficients can easily be computed from two features extracted from the original point cloud. Subjective quality tests with 400 compressed point clouds show that the proposed model correlates well with the mean opinion score, outperforming state-of-the-art full reference objective measures in terms of Spearman rankorder and Pearson linear correlation coefficient. Moreover, we show that for the same target bitrate, rate-distortion optimization based on the proposed model offers higher perceptual quality than rate-distortion optimization based on exhaustive search with a point-to-point objective quality metric. Our datasets are publicly available at https://github.com/qdushl/Waterloo-Point-Cloud-Database-2.0.

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