Geometric distortion metrics for point cloud compression

Yuan

IEEE Trans. on Broadcast.

et al. 2020

100

Point cloud based 3D visual representation is becoming popular due to its ability to exhibit the real world in a more comprehensive and immersive way. However, under a limited network bandwidth, it is very challenging to communicate this kind of media due to its huge data volume. Therefore, the MPEG have launched the standardization for point cloud compression (PCC), and proposed three model categories, i.e., TMC1, TMC2, and TMC3. Because the 3D geometry compression methods of TMC1 and TMC3 are similar, TMC1 and TMC3 are further merged into a new platform namely TMC13. In this paper, we first introduce some basic technologies that are usually used in 3D point cloud compression, then review the encoder architectures of these test models in detail, and finally analyze their rate distortion performance as well as complexity quantitatively for different cases (i.e., lossless geometry and lossless color, lossless geometry and lossy color, lossy geometry and lossy color) by using 16 benchmark 3D point clouds that are recommended by MPEG. Experimental results demonstrate that the coding efficiency of TMC2 is the best on average (especially for lossy geometry and lossy color compression) for dense point clouds while TMC13 achieves the optimal coding performance for sparse and noisy point clouds with lower time complexity.

Section: Experimental Results and Analysismentioning

confidence: 99%

A Comprehensive Study and Comparison of Core Technologies for MPEG 3-D Point Cloud Compression

Yuan

IEEE Trans. on Broadcast.

et al. 2020

100

2018 Picture Coding Symposium (PCS)

“…The normal-based metric, currently, consists of only one method, which is annotated as planeto-plane. The distance-based metrics can be further classified in three classes: (i) point-to-point, (ii) point-to-plane, and (iii) point-to-mesh [10]. The point-to-mesh metrics are based on projected distances between the point cloud under evaluation and the reconstructed reference object.…”

Section: Objective Quality Assessment Of Point Cloudsmentioning

confidence: 99%

“…The objective scores from every metric were calculated based on the same contents. The software described in [10] was used for the distance-based metrics. In particular, for the total error values based on the RMS and the Hausdorff distance, the implementation of the version 0.02 was adopted, while the version 0.09 was used to compute the error based on the MSE.…”

Section: Computation Of the Objective Scoresmentioning

confidence: 99%

Benchmarking of Objective Quality Metrics for Colorless Point Clouds

Alexiou

Ebrahimi

2018

Abstract-Recent advances in depth sensing and display technologies, along with the significant growth of interest for augmented and virtual reality applications, lay the foundation for the rapid evolution of applications that provide immersive experiences. In such applications, advanced content representations are required in order to increase the engagement of the user with the displayed imageries. Point clouds have emerged as a promising solution to this aim, due to their efficiency in capturing, storing, delivering and rendering of 3D immersive contents. As in any type of imaging, the evaluation of point clouds in terms of visual quality is essential. In this paper, benchmarking results of the state-of-the-art objective metrics in geometry-only point clouds are reported and analyzed under two different types of geometry degradations, namely Gaussian noise and octreebased compression. Human ratings obtained from two subjective experiments are used as the ground truth. Our results show that most objective quality metrics perform well in the presence of noise, whereas one particular method has high predictive power and outperforms the others after octree-based encoding.

2018 Tenth International Conference on Quality of Multimedia Experience (QoMEX)

“…The point-to-point (p2point) and point-to-plane (p2plane) metrics were used, as implemented in the software v. 0.11 described in [12], with the MeanSquared-Error (MSE) and the Hausdorff distance accounting for the geometric errors. The corresponding PSNR values, as defined in [12], were also computed using a factor of 1 in the numerator, leading to a total of 8 objective metrics.…”

Section: A Correlation Between Subjective and Objective Scoresmentioning

confidence: 99%

Point Cloud Subjective Evaluation Methodology based on 2D Rendering

Alexiou

Ebrahimi

Bernardo

et al. 2018

Abstract-Point clouds are one of the most promising technologies for 3D content representation. In this paper, we describe a study on quality assessment of point clouds, degraded by octreebased compression on different levels. The test contents were displayed using Screened Poisson surface reconstruction, without including any textural information, and they were rated by subjects in a passive way, using a 2D image sequence. Subjective evaluations were performed in five independent laboratories in different countries, with the inter-laboratory correlation analysis showing no statistical differences, despite the different equipment employed. Benchmarking results reveal that the state-of-the-art point cloud objective metrics are not able to accurately predict the expected visual quality of such test contents. Moreover, the subjective scores collected from this experiment were found to be poorly correlated with subjective scores obtained from another test involving visualization of raw point clouds. These results suggest the need for further investigations on adequate point cloud representations and objective quality assessment tools.