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Multi-modal No-Reference Objective Quality Assessment Method for Point Cloud Videos

Published: 17 April 2024 Publication History

Editorial Notes

The authors have requested minor, non-substantive changes to the Version of Record and, in accordance with ACM policies, a Corrected Version of Record was published on July 9, 2024. For reference purposes, the VoR may still be accessed via the Supplemental Material section on this page.

Abstract

This paper proposes a multi-modal reference-free objective quality assessment method for point cloud videos. The method extracts spatial and temporal features of distorted point cloud videos through three network branches, namely point cloud, projection, and video, respectively, and then fuses the spatio-temporal features using a multi-modal attention mechanism to improve the accuracy and generalization ability of point cloud video quality prediction. In this paper, sufficient experiments are conducted on a self-constructed database consisting of 36 distorted point cloud video sequences, and the experimental results show that the multi-modal quality assessment method proposed in this paper outperforms the current state-of-the-art 11 unimodal and bimodal methods, and can further improve the quality assessment performance.

Supplemental Material

PDF File - Version of Record
VoR for "Multi-modal No-Reference Objective Quality Assessment Method for Point Cloud Videos" by Chen et al., Proceedings of the 2023 7th International Conference on Electronic Information Technology and Computer Engineering (EITCE '23).

References

[1]
Marvie J E, Nehmé Y, Graziosi D, Crafting the MPEG metrics for objective and perceptual quality assessment of volumetric videos[J]. Quality and User Experience, 2023, 8(1): 4.
[2]
Alexiou E, Nehmé Y, Zerman E, Subjective and objective quality assessment for volumetric video[M]//Immersive Video Technologies. Academic Press, 2023: 501-552.
[3]
Zerman E, Ozcinar C, Gao P, Textured mesh vs coloured point cloud: A subjective study for volumetric video compression[C]//2020 Twelfth International Conference on Quality of Multimedia Experience (QoMEX). IEEE, 2020: 1-6.
[4]
Viola I, Subramanyam S, Li J, On the impact of VR assessment on the quality of experience of highly realistic digital humans: A volumetric video case study[J]. Quality and User Experience, 2022, 7(1): 3.
[5]
van der Hooft J, Vega M T, Timmerer C, Objective and subjective QoE evaluation for adaptive point cloud streaming[C]//2020 twelfth international conference on quality of multimedia experience (QoMEX). IEEE, 2020: 1-6.
[6]
Ak A, Zerman E, Ling S, The effect of temporal sub-sampling on the accuracy of volumetric video quality assessment[C]//2021 Picture Coding Symposium (PCS). IEEE, 2021: 1-5.
[7]
Van Damme S, Vega M T, De Turck F. A Full-and No-Reference Metrics Accuracy Analysis for Volumetric Media Streaming[C]//2021 13th International Conference on Quality of Multimedia Experience (QoMEX). IEEE, 2021: 225-230.
[8]
Van Damme S, Vega M T, van der Hooft J, Clustering-based psychometric no-reference quality model for point cloud video[C]//2022 IEEE International Conference on Image Processing (ICIP). IEEE, 2022: 1866-1870.
[9]
International Conference on Multimedia & Expo Workshops (ICMEW). IEEE, 2020: 1-6.
[10]
Yang Q, Ma Z, Xu Y, Inferring point cloud quality via graph similarity[J]. IEEE transactions on pattern analysis and machine intelligence, 2020, 44(6): 3015-3029.
[11]
Tao W, Jiang G, Jiang Z, Point cloud projection and multi-scale feature fusion network based blind quality assessment for colored point clouds[C]//Proceedings of the 29th ACM International Conference on Multimedia. 2021: 5266-5272.
[12]
Wang Z, Zhang Y, Yang Q, Point cloud quality assessment using 3D saliency maps[J]. arXiv preprint arXiv:2209.15475, 2022.
[13]
Zhang Z, Sun W, Min X, MM-PCQA: Multi-modal learning for no-reference point cloud quality assessment[J]. arXiv preprint arXiv:2209.00244, 2022.
[14]
Tsai Y H H, Bai S, Liang P P, Multi-modal transformer for unaligned multi-modal language sequences[C]//Proceedings of the conference. Association for Computational Linguistics. Meeting. NIH Public Access, 2019, 2019: 6558.
[15]
Qi C R, Yi L, Su H, Pointnet++: Deep hierarchical feature learning on point sets in a metric space[J]. Advances in neural information processing systems, 2017, 30.
[16]
E. d'Eon, B. Harrison, T. Myers, and P . A. Chou, “8i V oxelized Full Bodies - A Voxelized Point Cloud Dataset,” ISO/IEC JTC1/SC29 Joint WG11/WG1 (MPEG/JPEG) input document WG11M40059/WG1M74006, Geneva, CH, January 2017.
[17]
MPEG 3DG and Requirements, “Complementary PCC Test Material,” ISO/IEC JTC1/SC29 WG11 Doc. N16716, Geneva, CH, January 2017.
[18]
Tian D, Ochimizu H, Feng C, Geometric distortion metrics for point cloud compression[C]//2017 IEEE International Conference on Image Processing (ICIP). IEEE, 2017: 3460-3464.
[19]
Liu Y, Yang Q, Xu Y, Point cloud quality assessment: Dataset construction and learning-based no-reference metric[J]. ACM Transactions on Multimedia Computing, Communications and Applications, 2023, 19(2s): 1-26.
[20]
Mittal A, Soundararajan R, Bovik A C. Making a “completely blind” image quality analyzer[J]. IEEE Signal processing letters, 2012, 20(3): 209-212.
[21]
Yu L, Li J, Pakdaman F, MAMIQA: No-Reference Image Quality Assessment Based on Multiscale Attention Mechanism With Natural Scene Statistics[J]. IEEE Signal Processing Letters, 2023.
[22]
Kotevski Z, Mitrevski P. Experimental comparison of psnr and ssim metrics for video quality estimation[C]//International Conference on ICT Innovations. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009: 357-366.
[23]
Li Z, Bampis C, Novak J, VMAF: The journey continues[J]. Netflix Technology Blog, 2018, 25(1).
[24]
Li D, Jiang T, Jiang M. Quality assessment of in-the-wild videos[C]//Proceedings of the 27th ACM International Conference on Multimedia. 2019: 2351-2359.
[25]
Wu H, Chen C, Hou J, Fast-vqa: Efficient end-to-end video quality assessment with fragment sampling[C]//European Conference on Computer Vision. Cham: Springer Nature Switzerland, 2022: 538-554.

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    EITCE '23: Proceedings of the 2023 7th International Conference on Electronic Information Technology and Computer Engineering
    October 2023
    1809 pages
    ISBN:9798400708305
    DOI:10.1145/3650400
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 17 April 2024

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