  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.
