Inferring Point Cloud Quality via Graph Similarity
We propose the GraphSIM -- an objective metric to accurately predict the subjective quality of point cloud with superimposed geometry and color impairments. Motivated by the facts that human vision system is more sensitive to the high spatial-frequency components (e.g., contours, edges), and weighs...
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Main Authors | , , , , |
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Format | Journal Article |
Language | English |
Published |
31.05.2020
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Subjects | |
Online Access | Get full text |
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Summary: | We propose the GraphSIM -- an objective metric to accurately predict the
subjective quality of point cloud with superimposed geometry and color
impairments. Motivated by the facts that human vision system is more sensitive
to the high spatial-frequency components (e.g., contours, edges), and weighs
more to the local structural variations rather individual point intensity, we
first extract geometric keypoints by resampling the reference point cloud
geometry information to form the object skeleton; we then construct local
graphs centered at these keypoints for both reference and distorted point
clouds, followed by collectively aggregating color gradient moments (e.g.,
zeroth, first, and second) that are derived between all other points and
centered keypoint in the same local graph for significant feature similarity
(a.k.a., local significance) measurement; Final similarity index is obtained by
pooling the local graph significance across all color channels and by averaging
across all graphs. Our GraphSIM is validated using two large and independent
point cloud assessment datasets that involve a wide range of impairments (e.g.,
re-sampling, compression, additive noise), reliably demonstrating the
state-of-the-art performance for all distortions with noticeable gains in
predicting the subjective mean opinion score (MOS), compared with those
point-wise distance-based metrics adopted in standardization reference
software. Ablation studies have further shown that GraphSIM is generalized to
various scenarios with consistent performance by examining its key modules and
parameters. |
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DOI: | 10.48550/arxiv.2006.00497 |