With the rapid development of point cloud applications, we have witnessed the prosperity of point cloud coding techniques in recent years. These point cloud codecs yield various compression artifacts, posing challenges to the point cloud quality assessment. Current PCQA metrics cannot handle the complicated compression distortion effectively. To overcome the challenge, we attend the ICIP 2023 point cloud visual quality assessment (PCVQA) grand challenge[1], and our BPQA model[2] achieved a competitive result over the BASICS[3] dataset.

Our proposed BPQA model consists of three modules. First, it selects points of various salience degrees based on the color information. Second, it projects the local neighborhood of selected points along one of the three orthogonal axes to yield a five-channel map (namely, RGB, depth, and pairwise-point-distance-mean channels). Third, it extracts features using the channel-wise Saab transform (c/w Saab) and the relevant feature test (RFT) and trains an XGBoost regressor to predict the Mean Opinion Score (MOS). BPQA offers competitive performance in no-reference quality assessment tasks of the ICIP 2023 PCVQA Challenge.

[2]Q. Zhou, A. Feng, T.-S. Yang, S. Liu, and C.-C. J. Kuo, “Bpqa: A blind point cloud quality assessment method,” in 2023 IEEE International Conference on image processing (ICIP). IEEE, 2023.
[3] A. Ak, E. Zerman, M. Quach, A. Chetouani, A. Smolic, G. Valenzise, and P. L. Callet, “Basics: Broad quality assessment of static point clouds in compression scenarios,” ArXiv, vol. abs/2302.04796, 2023

-By Qingyang Zhou