Multi-Distance Point Cloud Quality Assessment

Diniz, Rafael; Freitas, Pedro Garcia

doi:10.1109/icip40778.2020.9190956

Cited by 31 publications

(11 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…From the perspective of distortion type, objective PCQA models can be classed as geometry distortion metrics [16], [25], [26], [28]- [30], [33], [39], [40], [79] and geometry-plus-color distortion metrics [12], [21], [23], [28], [29], [32], [35], [37], [38], [44]- [47], [49]- [60]. From the perspective of feature extraction, objective PCQA models can be classed as pointbased models [16], [25], [28]- [30], [33], [35], [37]- [40], [44], [46], [47], [49]- [51], [54]- [57], [59], [60] and projection-based models [12], [21], [23], [32], [45], [52], [53], [58].…”

Section: Objective Quality Assessment Of 3d Point Cloudsmentioning

confidence: 99%

“…Compared with geometry-only PCs, colored PCs have a broad range of applications. Many point-based metrics for colored PCs have emerged recently [6], [28], [29], [35], [37], [38], [42], [44], [46], [47], [49]- [51], [54], [55], [57], [59], [60]. In [28], [29], point-to-point PSNR on the Y component (MPEG PSNR Y ) is used to estimate texture distortion of colored PCs, though such a direct extension of PSNR inevitably inherits the widely-known disadvantages of PSNR.…”

Section: Objective Quality Assessment Of 3d Point Cloudsmentioning

confidence: 99%

“…Geometry-only and color-only approaches are then combined to get a rendering-independent objective PCQA metric. More recently, statistics of a variant of the Local Binary Pattern (LBP) [46], [47], Perceptual Color Distance Patterns (PCDP) [50] and Local Luminance Patterns (LLP) [51] descriptors are introduced to PCQA. In [54], the BitDance metric uses color and geometry texture descriptors.…”

Section: Objective Quality Assessment Of 3d Point Cloudsmentioning

confidence: 99%

“…Recently, substantial effort has been made to develop objective PCQA models [12], [16], [21], [23], [25], [28]- [30], [32], [33], [35], [37]- [40], [44]- [47], [49]- [55], [55]- [60]. Existing models either calculate distortions by directly comparing the 3D structures between the reference and distorted point clouds [16], [25], [28]- [30], [33], [35], [37]- [40], [44], [46], [47], [49]- [51], [54], [55], [55]- [57], [59], [60], or leverage welldeveloped FR image quality assessment (IQA) models after projecting point clouds to multiple 2D images [12], [21], [23], [32], [45], [52], [53], [58]. However, the performances of these models are often degraded by the absence of proper modelings of the HVS [69],…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Perceptual Quality Assessment of 3d Point Clouds

Duanmu

Liu

et al. 2019

2019 IEEE International Conference on Image Processing (ICIP)

View full text Add to dashboard Cite

The real-world applications of 3D point clouds have been growing rapidly in recent years, but not much effective work has been dedicated to perceptual quality assessment of colored 3D point clouds. In this work, we first build a large 3D point cloud database for subjective and objective quality assessment of point clouds. We construct 20 high quality, realistic, and omni-directional point clouds of diverse contents. We then apply downsampling, Gaussian noise, and three types of compression algorithms to create 740 distorted point clouds. We carry out a subjective experiment to evaluate the quality of distorted point clouds. Our statistical analysis finds that existing objective point cloud quality assessment (PCQA) models only achieve limited success in predicting subjective quality ratings. We propose a novel objective PCQA model based on the principle of information content weighted structural similarity. Our experimental results show that the proposed model well correlates with subjective opinions and significantly outperforms the existing PCQA models.

show abstract

Section: Objective Quality Assessment Of 3d Point Cloudsmentioning

confidence: 99%

Section: Objective Quality Assessment Of 3d Point Cloudsmentioning

confidence: 99%

Section: Objective Quality Assessment Of 3d Point Cloudsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Perceptual Quality Assessment of 3d Point Clouds

Duanmu

Liu

et al. 2019

2019 IEEE International Conference on Image Processing (ICIP)

View full text Add to dashboard Cite

show abstract

“…In [27], local binary patterns (LBP) on the luminance channel are applied in local neighborhoods. This work is extended in [28] considering the point-to-plane distance between point clouds, and the point-to-point distance between feature maps. A variant descriptor called local luminance patterns (LLP) is proposed in [29], introducing a voxelization stage.…”

Section: Related Workmentioning

confidence: 99%

PointPCA: Point Cloud Objective Quality Assessment Using PCA-Based Descriptors

Alexiou¹,

Viola²,

César³

2021

Preprint

View full text Add to dashboard Cite

With the increasing popularity of extended reality technology and the adoption of depth-enhanced visual data in information exchange and telecommunication systems, point clouds have emerged as a promising 3D imaging modality. Similarly to other types of content representations, visual quality predictors for point cloud data are vital for a wide range of applications, enabling perceptually optimized solutions from acquisition to rendering. Recent standardization activities on point cloud compression have urged the need for objective quality evaluation methods, driving the research community to the development of relevant algorithms. In this work, we complement existing approaches by proposing a new quality metric that compares local shape and appearance measurements between a reference and a distorted point cloud. To this aim, a large set of geometric and textural descriptors is defined, and the prediction accuracy of corresponding statistical features is evaluated in the context of quality assessment. Different combination strategies are examined, providing insights regarding the effectiveness of different metric designs. The performance of the proposed method is validated against subjectively-annotated datasets, showing better performance against state-of-the-art solutions in the majority of cases. A software implementation of the metric is made available here: https://github.com/cwi-dis/pointpca.

show abstract