EPES: Point Cloud Quality Modeling Using Elastic Potential Energy Similarity

Xu, Yiling; Yang, Qi; Yang, Le; Hwang, Jenq‐Neng

doi:10.1109/tbc.2021.3114510

Cited by 22 publications

(3 citation statements)

References 31 publications

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“…An extension is presented in [34], namely, BitDance, which incorporates bit-based labels from a geometric descriptor that relies on the comparison of neighboring normal vectors. The EPES presented in [35], relies on potential energy; that is, the energy needed to move points of a local neighborhood from an origin to their current geometric and color status. The MPED [36] also utilizes the point potential energy, which quantifies the spatial distribution and color under certain metric space to measure isometrical distortion.…”

Section: Point-based Objective Quality Metricsmentioning

confidence: 99%

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

Alexiou,

Zhou,

Viola

et al. 2024

Preprint

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Point clouds denote a prominent solution for the representation of 3D photo-realistic content in immersive applications. Similarly to other imaging modalities, quality predictions for point cloud contents are vital for a wide range of applications, enabling trade-off optimizations between data quality and data size in every processing step from acquisition to rendering. In this work, we focus on use cases that consider human end-users consuming point cloud contents and, hence, we concentrate on visual quality metrics. In particular, we propose a set of perceptually relevant descriptors based on Principal Component Analysis (PCA) decomposition, which is applied to both geometry and texture data for full-reference point cloud quality assessment. Statistical features are derived from these descriptors to characterize local shape and appearance properties for both a reference and a distorted point cloud. The extracted statistical features are subsequently compared to provide corresponding predictions of visual quality for the distorted point cloud. As part of our method, a learning-based approach is proposed to fuse these individual predictors to a unified perceptual score. We validate the accuracy of the individual predictors, as well as the unified quality scores obtained after regression against subjectively annotated datasets, showing that our metric outperforms state-of-the-art solutions. Insights regarding design decisions are provided through exploratory studies, evaluating the performance of our metric under different parameter configurations, attribute domains, color spaces, and regression models. A software implementation of the proposed metric is made available at the following link: https://github.com/cwi-dis/pointpca_suite.

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Section: Point-based Objective Quality Metricsmentioning

confidence: 99%

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

Alexiou,

Zhou,

Viola

et al. 2024

Preprint

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“…In addition, they combined LBP and LCP in [58]. In the past two years, classical physics has also begun to be used in PCQA [34], [59]. In recent years, Graph Signal Processing (GSP) has been increasingly applied to the field of point clouds [60], [61].…”

Section: ) Feature-based Metricsmentioning

confidence: 99%

Point Cloud Quality Assessment Using Multi-Level Features

Lv,

Su,

Long

et al. 2024

IEEE Access

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Recently, point clouds have emerged as a promising research direction for representing 3D visual data in various immersive applications, including augmented reality, self-driving cars and so on. The research on point cloud quality assessment (PCQA) has received significant attention. Although there are various objective PCQA models, their generalization can not satisfy the requirement of practical applications. Obviously, various factors should be considered comprehensively in the PCQA model. The dependence of data and the pertinence of features limit the improvement of the generalization of the PCQA models. Extracting features from different types of point clouds and research directions is also a challenge. To overcome this limitation, we propose a multi-level features model (MFPCQA) that segregates the basic features from the point cloud data into a pool. Then, the extracted basic features are sorted and combined to generate the advanced features, which are more closely related to quality, obtaining a multi-level feature structure. Finally, an efficient quality regressor maps these advanced features to point cloud quality. It is worth noting that, according to different application scenarios, our proposed MFPCQA has carried out detailed experiments on two conditions of whether it can take advantage of the distortion type. Extensive experiments on several publicly available subjective point cloud quality datasets and different distortion type datas validate that our proposed MFPCQA can compete with state-of-the-art full-reference, reducedreference quality assessment models. The proposed MFPCQA significantly improves the generalization of quality assessment algorithms.INDEX TERMS Point cloud quality assessment, machine learning, multi-level features.

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“…Xu et al [86] present the EPES, a point cloud quality metric based on potential energy. In this method, a number of points are selected, called origins, after applying a high-pass filtering operation in the topology of a point cloud.…”

Section: For Point Cloudsmentioning

confidence: 99%