research-article

GMS-3DQA: Projection-Based Grid Mini-patch Sampling for 3D Model Quality Assessment

Authors:

Weisi LinAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications and Applications, Volume 20, Issue 6

Article No.: 178, Pages 1 - 19

https://doi.org/10.1145/3643817

Published: 08 March 2024 Publication History

Abstract

Nowadays, most three-dimensional model quality assessment (3DQA) methods have been aimed at improving accuracy. However, little attention has been paid to the computational cost and inference time required for practical applications. Model-based 3DQA methods extract features directly from the 3D models, which are characterized by their high degree of complexity. As a result, many researchers are inclined towards utilizing projection-based 3DQA methods. Nevertheless, previous projection-based 3DQA methods directly extract features from multi-projections to ensure quality prediction accuracy, which calls for more resource consumption and inevitably leads to inefficiency. Thus, in this article, we address this challenge by proposing a no-reference (NR) projection-based Grid Mini-patch Sampling 3D Model Quality Assessment (GMS-3DQA) method. The projection images are rendered from six perpendicular viewpoints of the 3D model to cover sufficient quality information. To reduce redundancy and inference resources, we propose a multi-projection grid mini-patch sampling strategy (MP-GMS), which samples grid mini-patches from the multi-projections and forms the sampled grid mini-patches into one quality mini-patch map (QMM). The Swin-Transformer tiny backbone is then used to extract quality-aware features from the QMMs. The experimental results show that the proposed GMS-3DQA outperforms existing state-of-the-art NR-3DQA methods on the point cloud quality assessment databases for both accuracy and efficiency. The efficiency analysis reveals that the proposed GMS-3DQA requires far less computational resources and inference time than other 3DQA competitors. The code is available at https://github.com/zzc-1998/GMS-3DQA.

References

[1]

Ilyass Abouelaziz, Aladine Chetouani, Mohammed El Hassouni, Longin Jan Latecki, and Hocine Cherifi. 2020. No-reference mesh visual quality assessment via ensemble of convolutional neural networks and compact multi-linear pooling. Pattern Recognition 100 (2020), 107174.

Digital Library

[2]

Ilyass Abouelaziz, Mohammed El Hassouni, and Hocine Cherifi. 2016. No-reference 3D mesh quality assessment based on dihedral angles model and support vector regression. In Image and Signal Processing. 369–377.

[3]

I. Abouelaziz, M. E. Hassouni, and H. Cherifi. 2017. A convolutional neural network framework for blind mesh visual quality assessment. In IEEE International Conference on Image Processing. 755–759.

Digital Library

[4]

Evangelos Alexiou and Touradj Ebrahimi. 2018. Point cloud quality assessment metric based on angular similarity. In IEEE International Conference on Multimedia and Expo. 1–6.

[5]

Evangelos Alexiou and Touradj Ebrahimi. 2020. Towards a point cloud structural similarity metric. In IEEE International Conference on Multimedia & Expo Workshops. 1–6.

[6]

Jochen Antkowiak, Jamal Baina, Noel Chateau, Antonio Claudio, Stephanie Colonnese, and Jorge Caviedes. 2000. Final report from the Video Quality Experts Group on the validation of objective models of video quality assessment March 2000. (2000).

[7]

Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009. ImageNet: A large-scale hierarchical image database. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 248–255.

[8]

Yu Fan, Zicheng Zhang, Wei Sun, Xiongkuo Min, Ning Liu, Quan Zhou, Jun He, Qiyuan Wang, and Guangtao Zhai. 2022. A no-reference quality assessment metric for point cloud based on captured video sequences. In IEEE International Workshop on Multimedia Signal Processing. IEEE, 1–5.

[9]

Yuming Fang, Liping Huang, Jiebin Yan, Xuelin Liu, and Yang Liu. 2022. Perceptual quality assessment of omnidirectional images. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 36. 580–588.

[10]

H. Graf, S. P. Serna, and A. Stork. 2006. Adaptive quality meshing for ”on-the-fly” volumetric mesh manipulations within virtual environments. In IEEE International Symposium on Virtual Environments, Human-Computer Interfaces and Measurement Systems. 178–183.

[11]

D. Graziosi, O. Nakagami, S. Kuma, A. Zaghetto, T. Suzuki, and A. Tabatabai. 2020. An overview of ongoing point cloud compression standardization activities: Video-based (V-PCC) and geometry-based (G-PCC). APSIPA Transactions on Signal and Information Processing 9 (2020).

[12]

Jie Gu, Gaofeng Meng, Cheng Da, Shiming Xiang, and Chunhong Pan. 2019. No-reference image quality assessment with reinforcement recursive list-wise ranking. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 8336–8343.

Digital Library

[13]

Ke Gu, Min Liu, Guangtao Zhai, Xiaokang Yang, and Wenjun Zhang. 2015. Quality assessment considering viewing distance and image resolution. IEEE Transactions on Broadcasting 61, 3 (2015), 520–531.

[14]

Jinjiang Guo, Vincent Vidal, Irene Cheng, Anup Basu, Atilla Baskurt, and Guillaume Lavoue. 2016. Subjective and objective visual quality assessment of textured 3D meshes. ACM Transactions on Applied Perception 14, 2 (2016), 20 pages.

[15]

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 770–778.

[16]

Alireza Javaheri, Catarina Brites, Fernando Pereira, and João Ascenso. 2020. A generalized Hausdorff distance based quality metric for point cloud geometry. In International Conference on Quality of Multimedia Experience. 1–6.

[17]

Diederik P. Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In ICLR.

[18]

Guillaume Lavoué. 2011. A multiscale metric for 3D mesh visual quality assessment. Computer Graphics Forum 30, 5 (2011), 1427–1437.

[19]

Li Li, Zhu Li, Shan Liu, and Houqiang Li. 2020. Occupancy-map-based rate distortion optimization and partition for video-based point cloud compression. IEEE Transactions on Circuits and Systems for Video Technology 31, 1 (2020), 326–338.

Digital Library

[20]

Leida Li, Weisi Lin, Xuesong Wang, Gaobo Yang, Khosro Bahrami, and Alex C. Kot. 2015. No-reference image blur assessment based on discrete orthogonal moments. IEEE Transactions on Cybernetics 46, 1 (2015), 39–50.

[21]

Qi Liu, Honglei Su, Zhengfang Duanmu, Wentao Liu, and Zhou Wang. 2022. Perceptual quality assessment of colored 3D point clouds. IEEE Transactions on Visualization and Computer Graphics (2022).

[22]

Qi Liu, Hui Yuan, Raouf Hamzaoui, Honglei Su, Junhui Hou, and Huan Yang. 2021. Reduced reference perceptual quality model with application to rate control for video-based point cloud compression. IEEE Transactions on Image Processing 30 (2021), 6623–6636.

Digital Library

[23]

Qi Liu, Hui Yuan, Junhui Hou, Raouf Hamzaoui, and Honglei Su. 2020. Model-based joint bit allocation between geometry and color for video-based 3D point cloud compression. IEEE Transactions on Multimedia 23 (2020), 3278–3291.

[24]

Qi Liu, Hui Yuan, Honglei Su, Hao Liu, Yu Wang, Huan Yang, and Junhui Hou. 2021. PQA-Net: Deep no reference point cloud quality assessment via multi-view projection. IEEE Transactions on Circuits and Systems for Video Technology 31, 12 (2021), 4645–4660.

[25]

Yutao Liu, Ke Gu, Xiu Li, and Yongbing Zhang. 2020. Blind image quality assessment by natural scene statistics and perceptual characteristics. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 16, 3 (2020), 1–91.

Digital Library

[26]

Yipeng Liu, Qi Yang, Yiling Xu, and Le Yang. 2022. Point cloud quality assessment: Dataset construction and learning-based no-reference metric. ACM Transactions on Multimedia Computing, Communications, and Applications (2022).

[27]

Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. 2021. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 10012–10022.

[28]

Rufael Mekuria, Kees Blom, and Pablo Cesar. 2016. Design, implementation, and evaluation of a point cloud codec for tele-immersive video. IEEE Transactions on Circuits and Systems for Video Technology 27, 4 (2016), 828–842.

Digital Library

[29]

R. Mekuria, Z. Li, C. Tulvan, and P. Chou. 2016. Evaluation criteria for point cloud compression. ISO/IEC MPEG16332 (2016).

[30]

Gabriel Meynet, Yana Nehmé, Julie Digne, and Guillaume Lavoué. 2020. PCQM: A full-reference quality metric for colored 3D point clouds. In International Conference on Quality of Multimedia Experience. 1–6.

[31]

Anish Mittal, Anush Krishna Moorthy, and Alan Conrad Bovik. 2012. No-reference image quality assessment in the spatial domain. IEEE Transactions on Image Processing 21, 12 (2012), 4695–4708.

Digital Library

[32]

Anish Mittal, Rajiv Soundararajan, and Alan C. Bovik. 2012. Making a “completely blind” image quality analyzer. IEEE Signal Processing Letters 20, 3 (2012), 209–212.

[33]

Yana Nehmé, Florent Dupont, Jean-Philippe Farrugia, Patrick Le Callet, and Guillaume Lavoué. 2022. Textured mesh quality assessment: Large-scale dataset and deep learning-based quality metric. arXiv preprint arXiv:2202.02397 (2022).

[34]

Y. Nehmé, F. Dupont, J. P. Farrugia, P. Le Callet, and G. Lavoué. 2021. Visual quality of 3D meshes with diffuse colors in virtual reality: Subjective and objective evaluation. IEEE Transactions on Visualization and Computer Graphics 27, 3 (2021), 2202–2219.

[35]

Sebastian Schwarz, Marius Preda, Vittorio Baroncini, Madhukar Budagavi, Pablo Cesar, Philip A. Chou, Robert A. Cohen, Maja Krivokuća, Sébastien Lasserre, Zhu Li, et al. 2018. Emerging MPEG standards for point cloud compression. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 9, 1 (2018), 133–148.

[36]

Ziyu Shan, Qi Yang, Rui Ye, Yujie Zhang, Yiling Xu, Xiaozhong Xu, and Shan Liu. 2023. GPA-Net: No-reference point cloud quality assessment with multi-task graph convolutional network. IEEE Transactions on Visualization and Computer Graphics (2023).

[37]

H. R. Sheikh, M. F. Sabir, and A. C. Bovik. 2006. A statistical evaluation of recent full reference image quality assessment algorithms. IEEE Transactions on Image Processing 15, 11 (2006), 3440–3451.

Digital Library

[38]

Wei Sun, Xiongkuo Min, Wei Lu, and Guangtao Zhai. 2022. A deep learning based no-reference quality assessment model for UGC videos. In ACM International Conference on Multimedia. 856–865.

Digital Library

[39]

Dihong Tian and Ghassan AlRegib. 2008. BaTex3: Bit allocation for progressive transmission of textured 3-D models. IEEE Transactions on Circuits and Systems for Video Technology 18, 1 (2008), 23–35.

Digital Library

[40]

Dong Tian, Hideaki Ochimizu, Chen Feng, Robert Cohen, and Anthony Vetro. 2017. Geometric distortion metrics for point cloud compression. In IEEE International Conference on Image Processing. 3460–3464.

Digital Library

[41]

Eric M. Torlig, Evangelos Alexiou, Tiago A. Fonseca, Ricardo L. de Queiroz, and Touradj Ebrahimi. 2018. A novel methodology for quality assessment of voxelized point clouds. In Applications of Digital Image Processing XLI, Vol. 10752. 174–190.

[42]

Renwei Tu, Gangyi Jiang, Mei Yu, Ting Luo, Zongju Peng, and Fen Chen. 2022. V-PCC projection based blind point cloud quality assessment for compression distortion. IEEE Transactions on Emerging Topics in Computational Intelligence (2022).

[43]

Libor Váša and Jan Rus. 2012. Dihedral angle mesh error: A fast perception correlated distortion measure for fixed connectivity triangle meshes. Computer Graphics Forum 31, 5 (2012), 1715–1724.

Digital Library

[44]

Kai Wang, Fakhri Torkhani, and Annick Montanvert. 2012. A fast roughness-based approach to the assessment of 3D mesh visual quality. Computers and Graphics 36, 7 (2012), 808–818.

Digital Library

[45]

Zhou Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli. 2004. Image quality assessment: from error visibility to structural similarity. IEEE TIP 13, 4 (2004), 600–612.

Digital Library

[46]

Shaoguo Wen and Junle Wang. 2021. A strong baseline for image and video quality assessment. arXiv preprint arXiv:2111.07104 (2021).

[47]

Sanghyun Woo, Shoubhik Debnath, Ronghang Hu, Xinlei Chen, Zhuang Liu, In So Kweon, and Saining Xie. 2023. ConvNeXt V2: Co-designing and scaling ConvNets with masked autoencoders. arXiv preprint arXiv:2301.00808 (2023).

[48]

Haoning Wu, Chaofeng Chen, Jingwen Hou, Liang Liao, Annan Wang, Wenxiu Sun, Qiong Yan, and Weisi Lin. 2022. FAST-VQA: Efficient end-to-end video quality assessment with fragment sampling. In European Conference on Computer Vision. 538–554.

Digital Library

[49]

Haoning Wu, Chaofeng Chen, Liang Liao, Jingwen Hou, Wenxiu Sun, Qiong Yan, Jinwei Gu, and Weisi Lin. 2022. Neighbourhood representative sampling for efficient end-to-end video quality assessment. arXiv preprint arXiv:2210.05357 (2022).

[50]

Haoning Wu, Liang Liao, Chaofeng Chen, Jingwen Hou, Annan Wang, Wenxiu Sun, Qiong Yan, and Weisi Lin. 2022. Disentangling aesthetic and technical effects for video quality assessment of user generated content. arXiv preprint arXiv:2211.04894 (2022).

[51]

Wuyuan Xie, Kaimin Wang, Yakun Ju, and Miaohui Wang. 2023. pmBQA: Projection-based blind point cloud quality assessment via multimodal learning. In Proceedings of the 31st ACM International Conference on Multimedia. 3250–3258.

Digital Library

[52]

Qi Yang, Hao Chen, Zhan Ma, Yiling Xu, Rongjun Tang, and Jun Sun. 2020. Predicting the perceptual quality of point cloud: A 3D-to-2D projection-based exploration. IEEE Transactions on Multimedia (2020).

[53]

Qi Yang, Yipeng Liu, Siheng Chen, Yiling Xu, and Jun Sun. 2022. No-reference point cloud quality assessment via domain adaptation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 21179–21188.

[54]

Qi Yang, Zhan Ma, Yiling Xu, Zhu Li, and Jun Sun. 2020. Inferring point cloud quality via graph similarity. IEEE Transactions on Pattern Analysis and Machine Intelligence (2020).

[55]

Qi Yang, Zhan Ma, Yiling Xu, Le Yang, Wenjun Zhang, and Jun Sun. 2019. Modeling the screen content image quality via multiscale edge attention similarity. IEEE Transactions on Broadcasting 66, 2 (2019), 310–321.

[56]

Guangtao Zhai, Wei Sun, Xiongkuo Min, and Jiantao Zhou. 2021. Perceptual quality assessment of low-light image enhancement. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 17, 4 (2021), 1–24.

Digital Library

[57]

Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, and Oliver Wang. 2018. The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 586–595.

[58]

Xiaoyu Zhang, Wei Gao, Ge Li, Qiuping Jiang, and Runmin Cong. 2023. Image quality assessment–driven reinforcement learning for mixed distorted image restoration. ACM Transactions on Multimedia Computing, Communications and Applications 19, 1s (2023), 1–23.

Digital Library

[59]

Zicheng Zhang, Wei Sun, Xiongkuo Min, Tao Wang, Wei Lu, and Guangtao Zhai. 2022. No-reference quality assessment for 3D colored point cloud and mesh models. IEEE Transactions on Circuits and Systems for Video Technology (2022).

Digital Library

[60]

Zicheng Zhang, Wei Sun, Xiongkuo Min, Tao Wang, Wei Lu, Wenhan Zhu, and Guangtao Zhai. 2021. A no-reference visual quality metric for 3D color meshes. In 2021 IEEE International Conference on Multimedia & Expo Workshops (ICMEW). IEEE, 1–6.

[61]

Zicheng Zhang, Wei Sun, Xiongkuo Min, Wei Wu, Ying Chen, and Guangtao Zhai. 2023. Evaluating point cloud from moving camera videos: A no-reference metric. IEEE Transactions on Multimedia (2023).

[62]

Zicheng Zhang, Wei Sun, Xiongkuo Min, Quan Zhou, Jun He, Qiyuan Wang, and Guangtao Zhai. 2023. MM-PCQA: Multi-modal learning for no-reference point cloud quality assessment. IJCAI (2023).

[63]

Zicheng Zhang, Wei Sun, Xiongkuo Min, Wenhan Zhu, Tao Wang, Wei Lu, and Guangtao Zhai. 2021. A no-reference evaluation metric for low-light image enhancement. In 2021 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 1–6.

[64]

Zicheng Zhang, Wei Sun, Yingjie Zhou, Jun Jia, Zhichao Zhang, Jing Liu, Xiongkuo Min, and Guangtao Zhai. 2023. Subjective and objective quality assessment for in-the-wild computer graphics images. ACM Transactions on Multimedia Computing, Communications and Applications 20, 4 (2023), 1–22.

Digital Library

[65]

Zicheng Zhang, Wei Sun, Yingjie Zhou, Haoning Wu, Chunyi Li, Xiongkuo Min, Xiaohong Liu, Guangtao Zhai, and Weisi Lin. 2023. Advancing zero-shot digital human quality assessment through text-prompted evaluation. arXiv preprint arXiv:2307.02808 (2023).

[66]

Zicheng Zhang, Wei Wu, Wei Sun, Danyang Tu, Wei Lu, Xiongkuo Min, Ying Chen, and Guangtao Zhai. 2023. MD-VQA: Multi-dimensional quality assessment for UGC live videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 1746–1755.

[67]

Zicheng Zhang, Yingjie Zhou, Wei Sun, Xiongkuo Min, Yuzhe Wu, and Guangtao Zhai. 2023. Perceptual quality assessment for digital human heads. In ICASSP 2023–2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 1–5.

[68]

Zicheng Zhang, Yingjie Zhou, Long Teng, Wei Sun, Chunyi Li, Xiongkuo Min, Xiao-Ping Zhang, and Guangtao Zhai. 2024. Quality-of-experience evaluation for digital twins in 6G network environments. IEEE Transactions on Broadcasting (2024).

[69]

Qian-Yi Zhou, Jaesik Park, and Vladlen Koltun. 2018. Open3D: A modern library for 3D data processing. arXiv preprint arXiv:1801.09847 (2018).

[70]

Wei Zhou, Qi Yang, Qiuping Jiang, Guangtao Zhai, and Weisi Lin. 2022. Blind quality assessment of 3D dense point clouds with structure guided resampling. arXiv preprint arXiv:2208.14603 (2022).

[71]

Wei Zhou, Guanghui Yue, Ruizeng Zhang, Yipeng Qin, and Hantao Liu. 2023. Reduced-reference quality assessment of point clouds via content-oriented saliency projection. IEEE Signal Processing Letters 30 (2023), 354–358.

Cited By

Tious AVigier TRicordel V(2024)New challenges in point cloud visual quality assessment: a systematic reviewFrontiers in Signal Processing10.3389/frsip.2024.14200604Online publication date: 1-Nov-2024
https://doi.org/10.3389/frsip.2024.1420060
Wang JGao WLi G(2024)Zoom to Perceive Better: No-Reference Point Cloud Quality Assessment via Exploring Effective Multiscale FeatureIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2024.336236934:7(6334-6346)Online publication date: Jul-2024
https://doi.org/10.1109/TCSVT.2024.3362369
Chai XShao FMu BChen HJiang QHo Y(2024)Plain-PCQA: No-Reference Point Cloud Quality Assessment by Analysis of Plain Visual and Geometrical ComponentsIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2024.335018034:7(6207-6223)Online publication date: Jul-2024
https://doi.org/10.1109/TCSVT.2024.3350180
Show More Cited By

Index Terms

GMS-3DQA: Projection-Based Grid Mini-patch Sampling for 3D Model Quality Assessment
1. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis
2. Human-centered computing
  1. Visualization
    1. Visualization design and evaluation methods

Recommendations

Elementary Projection Drawing: Theory and Practice. for Preparatory and Higher Scientific Schools, Industrial and Normal Classes; and the ... Draftsmen, and Artisans. in Six Divisions
Auxiliary Information Guided Self-attention for Image Quality Assessment
Image quality assessment (IQA) is an important problem in computer vision with many applications. We propose a transformer-based multi-task learning framework for the IQA task. Two subtasks: constructing an auxiliary information error map and completing ...
Neural network solution for a real-time no-reference video quality assessment of H.264/AVC video bitstreams
Abstract
The ever-growing video streaming services require accurate quality assessment with often no reference to the original media. One primary challenge in developing no-reference (NR) video quality metrics is achieving real-timeliness while retaining ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 20, Issue 6

June 2024

715 pages

EISSN:1551-6865

DOI:10.1145/3613638

Editor:
Abdulmotaleb El Saddik
Mohamed Bin Zayed University of Artificial Intelligence, UAE and University of Ottawa, Canada

Issue’s Table of Contents

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].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 March 2024

Online AM: 01 February 2024

Accepted: 28 January 2024

Revised: 30 November 2023

Received: 20 August 2023

Published in TOMM Volume 20, Issue 6

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

NSFC
Fundamental Research Funds for the Central Universities, National Key R&D Program of China
Shanghai Municipal Science and Technology Major Project

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

10
Total Citations
View Citations
421
Total Downloads

Downloads (Last 12 months)421
Downloads (Last 6 weeks)53

Reflects downloads up to 17 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Tious AVigier TRicordel V(2024)New challenges in point cloud visual quality assessment: a systematic reviewFrontiers in Signal Processing10.3389/frsip.2024.14200604Online publication date: 1-Nov-2024
https://doi.org/10.3389/frsip.2024.1420060
Wang JGao WLi G(2024)Zoom to Perceive Better: No-Reference Point Cloud Quality Assessment via Exploring Effective Multiscale FeatureIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2024.336236934:7(6334-6346)Online publication date: Jul-2024
https://doi.org/10.1109/TCSVT.2024.3362369
Chai XShao FMu BChen HJiang QHo Y(2024)Plain-PCQA: No-Reference Point Cloud Quality Assessment by Analysis of Plain Visual and Geometrical ComponentsIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2024.335018034:7(6207-6223)Online publication date: Jul-2024
https://doi.org/10.1109/TCSVT.2024.3350180
Li CZhang ZWu HZhang KBai LLiu XZhai GLin W(2024)PAPS-OVQA: Projection-Aware Patch Sampling for Omnidirectional Video Quality Assessment2024 IEEE International Symposium on Circuits and Systems (ISCAS)10.1109/ISCAS58744.2024.10558283(1-5)Online publication date: 19-May-2024
https://doi.org/10.1109/ISCAS58744.2024.10558283
Zhang ZFan YSun WMin XLiu XLi CWu HLin WLiu NZhai G(2024)Optimizing Projection-Based Point Cloud Quality Assessment with Human Preferred Viewpoints Selection2024 IEEE International Conference on Multimedia and Expo (ICME)10.1109/ICME57554.2024.10688055(1-6)Online publication date: 15-Jul-2024
https://doi.org/10.1109/ICME57554.2024.10688055
Li CWu HZhang ZHao HZhang KBai LLiu XMin XLin WZhai G(2024)Q-Refine: A Perceptual Quality Refiner for AI-Generated Image2024 IEEE International Conference on Multimedia and Expo (ICME)10.1109/ICME57554.2024.10687390(1-6)Online publication date: 15-Jul-2024
https://doi.org/10.1109/ICME57554.2024.10687390
Li CKou TGao YCao YSun WZhang ZZhou YZhang ZZhang WWu HLiu XMin XZhai G(2024)AIGIQA-20K: A Large Database for AI-Generated Image Quality Assessment2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW63382.2024.00636(6327-6336)Online publication date: 17-Jun-2024
https://doi.org/10.1109/CVPRW63382.2024.00636
Chen ZXiao SWang YWang YCai H(2024)GLDBF: Global and local dual-branch fusion network for no-reference point cloud quality assessmentDisplays10.1016/j.displa.2024.10288285(102882)Online publication date: Dec-2024
https://doi.org/10.1016/j.displa.2024.102882
Li ZTian CYuan HLu XMalekmohamadi H(2024)3D-MSFC: A 3D multi-scale features compression method for object detectionDisplays10.1016/j.displa.2024.10288085(102880)Online publication date: Dec-2024
https://doi.org/10.1016/j.displa.2024.102880
Zhang ZZhou YSun WMin XZhai G(2023)Geometry-Aware Video Quality Assessment for Dynamic Digital Human2023 IEEE International Conference on Image Processing (ICIP)10.1109/ICIP49359.2023.10222061(1365-1369)Online publication date: 8-Oct-2023
https://doi.org/10.1109/ICIP49359.2023.10222061

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Full Text

View this article in Full Text.

Media

Figures

Other

Tables

View full text|Download PDF

View Issue’s Table of Contents