Quality Analysis of the Coding Bitrate Tradeoff Between Geometry and Attributes for Colored Point Clouds

Typically, point cloud encoders allocate a similar bitrate for geometry and attributes (usually RGB color components) information coding. This paper reports a quality study considering different coding bitrate tradeoff between geometry and attributes. A set of five point clouds, representing different characteristics and types of content was encoded with the MPEG standard Geometry Point Cloud Compression (G-PCC), using octree to encode geometry information, and both the Region Adaptive Hierarchical Transform and the Prediction Lifting transform for attributes. Furthermore, the JPEG Pleno Point Cloud Verification Model was also tested. Five different attributes/geometry bitrate tradeoffs were considered, notably 70%/30%, 60%/40%, 50%/50%, 40%/60%, 30%/70%. Three point cloud objective metrics were selected to assess the quality of the reconstructed point clouds, notably the PSNR YUV, the Point Cloud Quality Metric, and GraphSIM. Furthermore, for each encoder, the Bjonteegaard Deltas were computed for each tradeoff, using the 50%/50% tradeoff as a reference. The reported results indicate that using a higher bitrate allocation for attribute encoding usually yields slightly better results.

Performance analysis of Deep Learning-based Lossy Point Cloud Geometry Compression Coding Solutions

The quality evaluation of three deep learning-based coding solutions for point cloud geometry, notably ADLPCC, PCC GEO CNNv2, and PCGCv2, is presented. The MPEG G-PCC was used as an anchor. Furthermore, LUT SR, which uses multi-resolution Look-Up tables, was also considered. A set of six point clouds representing landscapes and objects were used. As point cloud texture has a great influence on the perceived quality, two different subjective studies that differ in the texture addition model are reported and statistically compared. In the first experiment, the dataset was first encoded with the identified codecs. Then, the texture of the original point cloud was mapped to the decoded point cloud using the Meshlab software, resulting in a point cloud with both geometry and texture information. Finally, the resulting point cloud was encoded with G-PCC using the lossless-geometry-lossy-atts mode, while in the second experiment the texture was mapped directly onto the distorted geometry. Moreover, both subjective evaluations were used to benchmark a set of objective point cloud quality metrics. The two experiments were shown to be statistically different, and the tested metrics revealed quite different behaviors for the two sets of data. The results reveal that the preferred method of evaluation is the encoding of texture information with G-PCC after mapping the texture of the original point cloud to the distorted point cloud. The results suggest that current objective metrics are not suitable to evaluate distortions created by machine learning-based codecs.

Subjective Quality Evaluation of Point Clouds Using a Head Mounted Display

This paper reports on a subjective quality evaluation of static point clouds encoded with the MPEG codecs V-PCC and G-PCC, the deep learning-based codec RS-DLPCC, and the popular Draco codec. 18 subjects visualized 3D representations of distorted point clouds using a Head Mounted Display, which allowed for a direct comparison with their reference. The Mean Opinion Scores (MOS) obtained in this subjective evaluation were compared with the MOS from two previous studies, where the same content was visualized either on a 2D display or a 3D stereoscopic display, through the Pearson Correlation, Spearman Rank Order Correlation, Root Mean Square Error, and the Outlier Ratio. The results indicate that the three studies are highly correlated with one another. Moreover, a statistical analysis between all evaluations showed no significant differences between them.

Fast and Efficient Lenslet Image Compression

Light field imaging is characterized by capturing brightness, color, and directional information of light rays in a scene. This leads to image representations with huge amount of data that require efficient coding schemes. In this paper, lenslet images are rendered into sub-aperture images. These images are organized as a pseudo-sequence input for the HEVC video codec. To better exploit redundancy among the neighboring sub-aperture images and consequently decrease the distances between a sub-aperture image and its references used for prediction, sub-aperture images are divided into four smaller groups that are scanned in a serpentine order. The most central sub-aperture image, which has the highest similarity to all the other images, is used as the initial reference image for each of the four regions. Furthermore, a structure is defined that selects spatially adjacent sub-aperture images as prediction references with the highest similarity to the current image. In this way, encoding efficiency increases, and furthermore it leads to a higher similarity among the co-located Coding Three Units (CTUs). The similarities among the co-located CTUs are exploited to predict Coding Unit depths.Moreover, independent encoding of each group division enables parallel processing, that along with the proposed coding unit depth prediction decrease the encoding execution time by almost 80% on average. Simulation results show that Rate-Distortion performance of the proposed method has higher compression gain than the other state-of-the-art lenslet compression methods with lower computational complexity.