Reduced Reference Perceptual Quality Model and Application to Rate Control for 3D Point Cloud Compression
This work addresses the lack of a low-cost, perceptually correlated quality measure for 3D point cloud compression, which is a problem for researchers and developers working with V-PCC.
The authors developed a linear perceptual quality model for 3D point cloud compression, specifically for the V-PCC standard. This model uses geometry and color quantization parameters and two features from the original point cloud. Subjective tests on 400 compressed point clouds showed it outperformed state-of-the-art full reference objective measures in correlation with mean opinion scores.
In rate-distortion optimization, the encoder settings are determined by maximizing a reconstruction quality measure subject to a constraint on the bit rate. 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 perceptual quality. While several quality measures that fulfil these two criteria have been developed for images and video, no such one exists for 3D 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 parameters and whose coefficients can easily be computed from two features extracted from the original 3D point cloud. Subjective quality tests with 400 compressed 3D 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 rank-order and Pearsons linear correlation coefficient. Moreover, we show that for the same target bit rate, ratedistortion 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.