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Block size selection in rate-constrained geometry based point cloud compression

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Abstract

In geometry-based point cloud compression, the geometry information is typically compressed using octree coding. In octree coding, the size of the blocks in the voxelized point clouds, i.e., the number of voxels contained in a block, determines whether the geometry coding is lossless or lossy, and the degree of geometry compression in lossy coding. Therefore, selecting an appropriate block size for octree coding is crucial for compression quality of voxelized point clouds. In this paper, we propose an optimal block size selection scheme for geometry based point cloud compression with a given bit rate constraint. Firstly, we analyze the gradients of the overall quality of the point clouds with color coding bit rate and geometry coding bit rate in lossy geometry coding. Then, we propose an octree level selection approach that can output the optimal octree level for point cloud compression under a target bit rate. In this approach, we consider the difference between the impacts of lossy geometry coding and lossless geometry coding on the overall quality of the point clouds. Experimental results demonstrate that, using the level selected by the proposed algorithm for geometry coding can yield best coding results in terms of the average quality of the images rendered from decoded point clouds.

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References

  1. Alexiou E, Ebrahimi T (2018) Point cloud quality assessment metric based on angular similarity. 2018 IEEE International Conference on Multimedia and Expo (ICME), 1–6

  2. BT.709 (2015) Parameter values for the hdtv standards for production and international programme exchange

  3. Cao C, Preda M, Zaharia (2019) T. 3d point cloud compression: A survey. In The 24th International Conference on 3D Web Technology (New York, NY, USA), Web3D ’19, Association for Computing Machinery, p. 1–9

  4. Chou PA, Koroteev M, Krivokuća M (2019) A volumetric approach to point cloud compression, part i Attribute compression. IEEE Trans Image Process

  5. de Oliveira Rente P, Brites C, Ascenso J, Pereira F (2018) Graph-based static 3d point clouds geometry coding. IEEE Trans Multimedia 21(2):284–299

    Article  Google Scholar 

  6. De Queiroz RL, Chou PA (2016) Compression of 3d point clouds using a region-adaptive hierarchical transform. IEEE Trans Image Process 25(8):3947–3956

    Article  MathSciNet  Google Scholar 

  7. de Queiroz RL, Chou PA (2017) Motion-compensated compression of dynamic voxelized point clouds. IEEE Trans Image Process 26(8):3886–3895

    Article  MathSciNet  Google Scholar 

  8. d’Eon E, Harrison B, Myers T, Chou PA (2017) 8i voxelized full bodies-a voxelized point cloud dataset. ISO/IEC JTC1/SC29 Joint WG11/WG1 (MPEG/JPEG) input document WG11M40059/WG1M74006

  9. Fiengo A, Chierchia G, Cagnazzo M, Pesquet-Popescu B (2016) Rate allocation in predictive video coding using a convex optimization framework. IEEE Trans Image Process 26(1):479–489

    Article  MathSciNet  Google Scholar 

  10. Filali A, Ricordel V, Normand N, Hamidouche W (2019) Rate-distortion optimized tree-structured point-lattice vector quantization for compression of 3d point clouds geometry. In 2019 IEEE International Conference on Image Processing (ICIP), IEEE, pp. 1099–1103

  11. Gao P, Smolic A (2019) Occlusion-aware depth map coding optimization using allowable depth map distortions. IEEE Trans Image Process 28(11):5266–5280

    Article  MathSciNet  Google Scholar 

  12. Guo J, Vidal V, Cheng I, Basu A, Baskurt A, Lavoue G (2016) Subjective and objective visual quality assessment of textured 3d meshes. ACM Trans Appl Percept 14:2

    Google Scholar 

  13. Hosseini M, Timmerer C (2018) Dynamic adaptive point cloud streaming. In Proceedings of the 23rd Packet Video Workshop (New York, NY, USA), PV ’18, Association for Computing Machinery, p. 25–30

  14. Huang Y, Peng J, Kuo C-CJ, Gopi M (2008) A generic scheme for progressive point cloud coding. IEEE Trans Vis Comput Graph 14(2):440–453

    Article  Google Scholar 

  15. Perry S (2020) JPEG pleno point cloud coding common test conditions v3.1 (2020) Document n86044, Sydney, Australia

  16. Loop C, Cai Q, Orts Escolano S, Chou PA (2006) Microsoft voxelized upper bodies-a voxelized point cloud dataset (2006) Doc. m38673, Geneva, Switzerland

  17. Tian D, Ochimizu H, Feng C, Cohen R, Vetro A (2017) Evaluation metrics for point cloud compression (2017) Doc. m39966, Geneva

  18. Mammou K (2017) PCC test model category 2 v0. (2017) Doc. n17248, Macau, China

  19. Mammou K (2017) PCC test model category 3 v0. (2017) Doc. n17249, Macau, China

  20. Chou PA, Nakagami O, Jang ES (2017) Point cloud compression test model for category 1 v0. (2017) Doc. n17223, Macau, China

  21. Mammou K, Chou PA, Flynn D, Krivokuća M, Nakagami O, Sugio T (2019) G-PCC codec description v2. (2019) Doc. n18189, Marrakech, MA

  22. MPEG 3DG and Requirements (2017) Call for proposals for point cloud compression v2 Doc. n16763, Hobart, Australia

  23. Jang ES, Preda M, Mammou K, Tourapis AM, Kim J, Graziosi DB, Rhyu S, Budagavi M (2019) Video-based point-cloud-compression standard in mpeg: from evidence collection to committee draft [standards in a nutshell]. IEEE Signal Process Mag 36(3):118–123

    Article  Google Scholar 

  24. Kammerl J, Blodow N, Rusu RB, Gedikli S, Beetz M, Steinbach E (2012) Real-time compression of point cloud streams. In 2012 IEEE International Conference on Robotics and Automation, IEEE pp. 778–785

  25. Krivokuca M, Chou PA, Koroteev M (2020) A volumetric approach to point cloud compression part ii: Geometry compression. IEEE Trans Image Process 29:2217–2229

    Article  Google Scholar 

  26. Li L, Li Z, Zakharchenko V, Chen J, Li H (2019) Advanced 3d motion prediction for video-based dynamic point cloud compression. IEEE Trans Image Process 29:289–302

    Article  MathSciNet  Google Scholar 

  27. Liu Q, Yuan H, Hamzaoui R, Su H (2020) Coarse to fine rate control for region-based 3d point cloud compression. In 2020 IEEE International Conference on Multimedia Expo Workshops (ICMEW), pp. 1–6

  28. Ma S, Wen G, Yan L (2005) Rate-distortion analysis for h 264/avc video coding and its application to rate control. IEEE Trans Circuits Syst Video Technol 15(12):1533–1544

    Article  Google Scholar 

  29. Mekuria R, Blom K, Cesar P (2016) Design, implementation, and evaluation of a point cloud codec for tele-immersive video. IEEE Trans Circuits Syst Video Technol 27(4):828–842

    Article  Google Scholar 

  30. Meynet G, Nehme Y, Digne J, Lavoue G (2020) Pcqm: A full-reference quality metric for colored 3d point clouds. In 12th International Conference on Quality of Multimedia Experience (QoMEX 2020) IEEE, p. 1–6

  31. MPEG (2019) Geometry based point cloud compression (G-PCC) test model

  32. Orts-Escolano S, Rhemann C, Fanello S, Chang W, Kowdle A, Degtyarev Y, Kim D, Davidson PL, Khamis S, Dou M, Tankovich V, Loop C, Cai Q, Chou PA, Mennicken S, Valentin J, Pradeep V, Wang S, Kang SB, Kohli P, Lutchyn Y, Keskin C, Izadi S (2016) Holoportation: Virtual 3d teleportation in real-time. In Proceedings of the 29th Annual Symposium on User Interface Software and Technology (New York, NY, USA), UIST ’16, Association for Computing Machinery, p. 741–754

  33. Pagés R, Amplianitis K, Monaghan D, Ondřej J, Smolić A (2018) Affordable content creation for free-viewpoint video and vr/ar applications. J Vis Commun Image Represent 53:192–201

    Article  Google Scholar 

  34. Qian F, Han B, Pair J, Gopalakrishnan V (2019) Toward practical volumetric video streaming on commodity smartphones. In Proceedings of the 20th International Workshop on Mobile Computing Systems and Applications (New York, NY, USA), HotMobile ’19, Association for Computing Machinery, p. 135–140

  35. Quach M, Valenzise G, Dufaux F (2019) Learning convolutional transforms for lossy point cloud geometry compression. In 2019 IEEE International Conference on Image Processing (ICIP) IEEE pp. 4320–4324

  36. Rusu RB, Cousins S (2011) 3D is here: Point Cloud Library (PCL). In IEEE International Conference on Robotics and Automation (ICRA) (Shanghai, China)

  37. Schnabel R, Klein R (2006) Octree-based point-cloud compression. In Proceedings of Eurographics Symposium Point-Based Graphics (Boston, USA) pp. 111–120

  38. Schneider PJ, Eberly DH (2003) Geometric tools for computer graphics. Morgan Kaufmann Publishers, San Francisco, CA

    Google Scholar 

  39. Schwarz S, Preda M, Baroncini V, Budagavi M, Cesar P, Chou PA, Cohen RA, Krivokuća M, Lasserre S, Li Z et al (2018) Emerging mpeg standards for point cloud compression. IEEE J Emerging Sel Top Circuits Syst 9(1):133–148

    Article  Google Scholar 

  40. Slater M, Sanchez-Vives MV (2016) Enhancing our lives with immersive virtual reality. Frontiers in Robotics and AI 3:74

    Article  Google Scholar 

  41. Song H, Feng H-Y (2008) A global clustering approach to point cloud simplification with a specified data reduction ratio. Comput Aided Des 40(3):281–292

    Article  Google Scholar 

  42. Subramanyam S, Li J, Viola I, Cesar P (2020) Comparing the quality of highly realistic digital humans in 3dof and 6dof: A volumetric video case study. 2020 IEEE Conference on Virtual Reality and 3D User Interfaces (VR), 1–9

  43. Sullivan GJ, Ohm J-R, Han W-J, Wiegand T (2012) Overview of the high efficiency video coding (hevc) standard. IEEE Trans Circuits Syst Video Technol 22(12):1649–1668

    Article  Google Scholar 

  44. Sun X, Ma H, Sun Y, Liu M (2019) A novel point cloud compression algorithm based on clustering. IEEE Robot Autom Lett 4(2):2132–2139

    Article  Google Scholar 

  45. Thanou D, Chou PA, Frossard P (2016) Graph-based compression of dynamic 3d point cloud sequences. IEEE Trans Image Process 25(4):1765–1778

    Article  MathSciNet  Google Scholar 

  46. Torlig EM, Alexiou E, Fonseca TA, de Queiroz RL, Ebrahimi T (2018) A novel methodology for quality assessment of voxelized point clouds. In Applications of Digital Image Processing XLI, vol. 10752, International Society for Optics and Photonics, p. 107520I

  47. van der Hooft J, Wauters T, De Turck F, Timmerer C, Hellwagner H (2019) Towards 6dof http adaptive streaming through point cloud compression. In Proceedings of the 27th ACM International Conference on Multimedia (New York, NY, USA), MM ’19, Association for Computing Machinery, p. 2405–C2413

  48. Zerman E, Gao P, Ozcinar C, Smolic A (2019) Subjective and objective quality assessment for volumetric video compression. Electron Imaging 2019(10):323–1

    Article  Google Scholar 

  49. Zerman E, Ozcinar C, Gao P, Smolic A (2020) Textured mesh vs coloured point cloud: A subjective study for volumetric video compression. In 2020 Twelfth International Conference on Quality of Multimedia Experience (QoMEX) IEEE pp. 1–6

  50. Zhang C, Florencio D, Loop C (2014) Point cloud attribute compression with graph transform. In 2014 IEEE International Conference on Image Processing (ICIP) IEEE, pp. 2066–2070

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Acknowledgements

The authors would like to thank Professor Aljosa Smolic from Trinity College Dublin, Ireland, for the insightful advice and fruitful discussion on the design of the proposed algorithm in this paper. This work is supported in part by Aeronautical Science Foundation of China under Grant 201951052001, the Natural Science Foundation of Jiangsu Province under Grant BK20170806, and the Natural Science Foundation of China under Grant 61701227.

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  1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Pan Gao & Mingqiang Wei

  2. Science and Technology on Electro-optic Control Laboratory, Luoyang, China

    Pan Gao

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  1. Pan Gao
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  2. Mingqiang Wei
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Correspondence to Pan Gao.

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Gao, P., Wei, M. Block size selection in rate-constrained geometry based point cloud compression. Multimed Tools Appl 81, 2557–2575 (2022). https://doi.org/10.1007/s11042-021-11672-8

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