31,627 research outputs found
A generalized Hausdorff distance based quality metric for point cloud geometry
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.Comment: This article is accepted to 12th International Conference on Quality
of Multimedia Experience (QoMEX
GPA-Net:No-Reference Point Cloud Quality Assessment with Multi-task Graph Convolutional Network
With the rapid development of 3D vision, point cloud has become an
increasingly popular 3D visual media content. Due to the irregular structure,
point cloud has posed novel challenges to the related research, such as
compression, transmission, rendering and quality assessment. In these latest
researches, point cloud quality assessment (PCQA) has attracted wide attention
due to its significant role in guiding practical applications, especially in
many cases where the reference point cloud is unavailable. However, current
no-reference metrics which based on prevalent deep neural network have apparent
disadvantages. For example, to adapt to the irregular structure of point cloud,
they require preprocessing such as voxelization and projection that introduce
extra distortions, and the applied grid-kernel networks, such as Convolutional
Neural Networks, fail to extract effective distortion-related features.
Besides, they rarely consider the various distortion patterns and the
philosophy that PCQA should exhibit shifting, scaling, and rotational
invariance. In this paper, we propose a novel no-reference PCQA metric named
the Graph convolutional PCQA network (GPA-Net). To extract effective features
for PCQA, we propose a new graph convolution kernel, i.e., GPAConv, which
attentively captures the perturbation of structure and texture. Then, we
propose the multi-task framework consisting of one main task (quality
regression) and two auxiliary tasks (distortion type and degree predictions).
Finally, we propose a coordinate normalization module to stabilize the results
of GPAConv under shift, scale and rotation transformations. Experimental
results on two independent databases show that GPA-Net achieves the best
performance compared to the state-of-the-art no-reference PCQA metrics, even
better than some full-reference metrics in some cases
On the performance of metrics to predict quality in point cloud representations
Point clouds are a promising alternative for immersive representation of visual contents. Recently, an increased interest has been observed in the acquisition, processing and rendering of this modality. Although subjective and objective evaluations are critical in order to assess the visual quality of media content, they still remain open problems for point cloud representation. In this paper we focus our efforts on subjective quality assessment of point cloud geometry, subject to typical types of impairments such as noise corruption and compression-like distortions. In particular, we propose a subjective methodology that is closer to real-life scenarios of point cloud visualization. The performance of the state-of-the-art objective metrics is assessed by considering the subjective scores as the ground truth. Moreover, we investigate the impact of adopting different test methodologies by comparing them. Advantages and drawbacks of every approach are reported, based on statistical analysis. The results and conclusions of this work provide useful insights that could be considered in future experimentation
No-reference Bitstream-layer Model for Perceptual Quality Assessment of V-PCC Encoded Point Clouds
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.No-reference bitstream-layer models for point cloud quality assessment (PCQA) use the information extracted from a bitstream for real-time and nonintrusive quality monitoring. We propose a no-reference bitstream-layer model for the perceptual quality assessment of video-based point cloud compression (V-PCC) encoded point clouds. First, we describe the fundamental relationship between perceptual coding distortion and the texture quantization parameter (TQP) when geometry encoding is lossless. Then, we incorporate the texture complexity (TC) into the proposed model while considering the fact that the perceptual coding distortion of a point cloud depends on the texture characteristics. TC is estimated using TQP and the texture bitrate per pixel (TBPP), both of which are extracted from the compressed bitstream without resorting to complete decoding. Then, we construct a texture distortion assessment model upon TQP and TBPP. By combining this texture distortion model with the geometry quantization parameter (GQP), we obtain an overall no-reference bitstream-layer PCQA model that we call bitstreamPCQ. Experimental results show that the proposed model markedly outperforms existing models in terms of widely used performance criteria, including the Pearson linear correlation coefficient (PLCC), the Spearman rank order correlation coefficient (SRCC) and the root mean square error (RMSE). The dataset developed in this study is publicly available at https://github.com/qdushl/Waterloo-Point-Cloud-Database-3.0
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