A generalized Hausdorff distance based quality metric for point cloud geometry
This work addresses quality assessment for point cloud compression, which is incremental as it improves upon existing metrics for a specific domain.
The paper tackled the problem of assessing point cloud geometry quality for compression evaluation by proposing a novel metric based on a generalized Hausdorff distance, which outperformed existing MPEG metrics in correlation with subjective scores.
Reliable quality assessment of decoded point cloud geometry is essential to evaluate the compression performance of emerging point cloud coding solutions and guarantee some target quality of experience. This paper proposes a novel point cloud geometry quality assessment metric based on a generalization of the Hausdorff distance. To achieve this goal, the so-called generalized Hausdorff distance for multiple rankings is exploited to identify the best performing quality metric in terms of correlation with the MOS scores obtained from a subjective test campaign. The experimental results show that the quality metric derived from the classical Hausdorff distance leads to low objective-subjective correlation and, thus, fails to accurately evaluate the quality of decoded point clouds for emerging codecs. However, the quality metric derived from the generalized Hausdorff distance with an appropriately selected ranking, outperforms the MPEG adopted geometry quality metrics when decoded point clouds with different types of coding distortions are considered.