Towards a Point Cloud Structural Similarity Metric

Point cloud is a 3D image representation that has recently emerged as a viable approach for advanced content modality in modern communication systems. In view of its wide adoption, quality evaluation metrics are essential. In this paper, we propose and assess a family of statistical dispersion measurements for the prediction of perceptual degradations. The employed features characterize local distributions of point cloud attributes reflecting topology and color. After associating local regions between a reference and a distorted model, the corresponding feature values are compared. The visual quality of a distorted model is then predicted by error pooling across individual quality scores obtained per region. The extracted features aim at capturing local changes, similarly to the well- known Structural Similarity Index. Benchmarking results using available datasets reveal best-performing attributes and features, under different neighborhood sizes. Finally, point cloud voxelization is examined as part of the process, improving the prediction accuracy under certain conditions.

[1]  Touradj Ebrahimi,et al.  Exploiting user interactivity in quality assessment of point cloud imaging , 2019, 2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX).

[2]  T. Ebrahimi,et al.  Watermarked 3-D Mesh Quality Assessment , 2007, IEEE Transactions on Multimedia.

[3]  Libor Vása,et al.  On the Efficiency of Image Metrics for Evaluating the Visual Quality of 3D Models , 2016, IEEE Transactions on Visualization and Computer Graphics.

[4]  Guillaume Lavoué,et al.  PCQM: A Full-Reference Quality Metric for Colored 3D Point Clouds , 2020, 2020 Twelfth International Conference on Quality of Multimedia Experience (QoMEX).

[5]  Libor Vása,et al.  Dihedral Angle Mesh Error: a fast perception correlated distortion measure for fixed connectivity triangle meshes , 2012, Comput. Graph. Forum.

[6]  Touradj Ebrahimi,et al.  Point Cloud Quality Assessment Metric Based on Angular Similarity , 2018, 2018 IEEE International Conference on Multimedia and Expo (ICME).

[7]  Guillaume Lavoué,et al.  A Multiscale Metric for 3D Mesh Visual Quality Assessment , 2011, Comput. Graph. Forum.

[8]  Guillaume Lavoué,et al.  PC-MSDM: A quality metric for 3D point clouds , 2019, 2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX).

[9]  Methods , metrics and procedures for statistical evaluation , qualification and comparison of objective quality prediction models , 2013 .

[10]  Touradj Ebrahimi,et al.  A novel methodology for quality assessment of voxelized point clouds , 2018, Optical Engineering + Applications.

[11]  Shishir Subramanyam,et al.  A Color-Based Objective Quality Metric for Point Cloud Contents , 2020, 2020 Twelfth International Conference on Quality of Multimedia Experience (QoMEX).

[12]  Catarina Brites,et al.  Point Cloud Rendering After Coding: Impacts on Subjective and Objective Quality , 2019, IEEE Transactions on Multimedia.

[13]  Dong Tian,et al.  Geometric distortion metrics for point cloud compression , 2017, 2017 IEEE International Conference on Image Processing (ICIP).

[14]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[15]  Ricardo L. de Queiroz,et al.  A comprehensive study of the rate-distortion performance in MPEG point cloud compression , 2019, APSIPA Transactions on Signal and Information Processing.