Visual Quality of Compressed Mesh and Point Cloud Sequences

Cao, Keming; Xu, Yi; Cosman, Pamela C.

doi:10.1109/access.2020.3024633

Cited by 23 publications

(10 citation statements)

References 59 publications

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“…Only a few authors specifically tackled the problem of volumetric video quality assessment. Zerman et al [ 29 ] and Cao et al [ 30 ] conducted subjective quality assessment experiments with volumetric videos, they both compared mesh and point cloud representations concerning their rate-distortion tradeoff. Viola et al [ 6 ] compared how the level of interactions (e.g., 3DoFs vs 6DoFs) influence the perceived quality of volumetric videos.…”

Section: Related Workmentioning

confidence: 99%

Crafting the MPEG metrics for objective and perceptual quality assessment of volumetric videos

Marvie¹,

Nehmé

Graziosi

et al. 2023

Qual User Exp

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Efficient objective and perceptual metrics are valuable tools to evaluate the visual impact of compression artifacts on the visual quality of volumetric videos (VVs). In this paper, we present some of the MPEG group efforts to create, benchmark and calibrate objective quality assessment metrics for volumetric videos represented as textured meshes. We created a challenging dataset of 176 volumetric videos impaired with various distortions and conducted a subjective experiment to gather human opinions (more than 5896 subjective scores were collected). We adapted two state-of-the-art model-based metrics for point cloud evaluation to our context of textured mesh evaluation by selecting efficient sampling methods. We also present a new image-based metric for the evaluation of such VVs whose purpose is to reduce the cumbersome computation times inherent to the point-based metrics due to their use of multiple kd-tree searches. Each metric presented above is calibrated (i.e., selection of best values for parameters such as the number of views or grid sampling density) and evaluated on our new ground-truth subjective dataset. For each metric, the optimal selection and combination of features is determined by logistic regression through cross-validation. This performance analysis, combined with MPEG experts’ requirements, lead to the validation of two selected metrics and recommendations on the features of most importance through learned feature weights.

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Section: Related Workmentioning

confidence: 99%

Crafting the MPEG metrics for objective and perceptual quality assessment of volumetric videos

Marvie¹,

Nehmé

Graziosi

et al. 2023

Qual User Exp

View full text Add to dashboard Cite

show abstract

“…It is important to understand the impact of compression and streaming on the quality of spherical and volumetric content [ 167 ]. In [ 168 ], a spatio-temporal modeling approach is proposed to evaluate the quality of omnidirectional video, while in [ 169 , 170 , 171 ] quality assessment of the compressed volumetric content is studied for two popular representations (meshes and point clouds). The results show that meshes provide the best quality at high bitrates, while point clouds perform better for low bitrate cases.…”

Section: Learning-based Transmissionmentioning

confidence: 99%

Machine Learning for Multimedia Communications

Thomos

Maugey

Toni

2022

Sensors

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Machine learning is revolutionizing the way multimedia information is processed and transmitted to users. After intensive and powerful training, some impressive efficiency/accuracy improvements have been made all over the transmission pipeline. For example, the high model capacity of the learning-based architectures enables us to accurately model the image and video behavior such that tremendous compression gains can be achieved. Similarly, error concealment, streaming strategy or even user perception modeling have widely benefited from the recent learning-oriented developments. However, learning-based algorithms often imply drastic changes to the way data are represented or consumed, meaning that the overall pipeline can be affected even though a subpart of it is optimized. In this paper, we review the recent major advances that have been proposed all across the transmission chain, and we discuss their potential impact and the research challenges that they raise.

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“…In [35], the authors presented subjective quality evaluation of point clouds that were encoded directly using V-PCC, or by encoding their mesh representations (in which case both their atlas images and vertices had to be compressed). They also proposed no-reference objective quality measure, depending on the used bitrate and observers' distance from the screen.…”

Section: Subjective Evaluation Of Point Cloudsmentioning

confidence: 99%

Point Cloud Coding Solutions, Subjective Assessment and Objective Measures: A Case Study

Dumic

Cruz

2020

Symmetry

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This paper presents a summary of recent progress in compression, subjective assessment and objective quality measures of point cloud representations of three dimensional visual information. Different existing point cloud datasets, as well as discusses the protocols that have been proposed to evaluate the subjective quality of point cloud data. Several geometry and attribute point cloud data objective quality measures are also presented and described. A case study on the evaluation of subjective quality of point clouds in two laboratories is presented. Six original point clouds degraded with G-PCC and V-PCC point cloud compression and five degradation levels were subjectively evaluated, showing high inter-laboratory correlation. Furthermore, performance of several geometry-based objective quality measures applied to the same data are described, concluding that the highest correlation with subjective scores is obtained using point-to-plane measures. Finally, several current challenges and future research directions on point clouds compression and quality evaluation are discussed.

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Visual Quality of Compressed Mesh and Point Cloud Sequences

Cited by 23 publications

References 59 publications

Crafting the MPEG metrics for objective and perceptual quality assessment of volumetric videos

Crafting the MPEG metrics for objective and perceptual quality assessment of volumetric videos

Machine Learning for Multimedia Communications

Point Cloud Coding Solutions, Subjective Assessment and Objective Measures: A Case Study

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