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Optimized wavelet-based texture representation and streaming for GPU texture mapping

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Abstract

Because of the ever increasing resolution of consumer displays, high quality real-time 3D rendering applications require large amounts of 2D texture data, which in turn require large amounts of storage, memory and bandwidth. One of the major advantages of using compressed texture data is the significant decrease in bandwidth requirements, decreasing texture loading times. Additionally, instead of preloading all the potentially required information, texture data can be streamed progressively at run-time, which is a very common scenario in web-based applications, games and large virtual environments. This paper proposes a new texture streaming system, utilizing both pre-computed and run-time scene analysis, camera prediction and wavelet-based texture compression to provide maximal visual quality within bandwidth and real-time constraints. Texture decoding is done on the GPU, saving both on streaming bandwidth and GPU memory. The pre-computed scene analysis system can perform the run-time analysis at virtually no performance cost in multi-threaded systems. The proposed solution can easily be plugged into existing classical texture mapping solutions, as it features drop-in replacement shaders and re-uses existing render facilities. The wavelet-based streaming system results in PSNR improvements of up to 2 dB when compared to a DXT1-based streaming system.

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References

  1. Andries B, Lemeire J, Munteanu A (2014) Optimized quantization of wavelet subbands for high quality real-time texture compression. In: IEEE International conference on image processing 2014. Paris

  2. Andries B, Lemeire J, Munteanu A (2016) Scalable texture compression using the wavelet transform. Vis Comput 1–19

  3. Barrett S (2008) Sparse virtual textures. In: Game developer conference. San Francisco

  4. Bjontegaard G (2001) Calcuation of average PSNR differences between RD-curves. Doc. VCEG-M33 ITU-T Q6/16. Austin

  5. Cline D, Egbert PK (1998) Interactive display of very large textures Proceedings of the conference on visualization ’98, VIS ’98. http://dl.acm.org/citation.cfm?id=288216.288306. IEEE Computer Society Press, Los Alamitos, pp 343–350

  6. Cohen-Or D, Rich E, Lerner U, Shenkar V (1996) A real-time photo-realistic visual flythrough. IEEE Trans Vis Comput Graph 2(3):255–265

    Article  Google Scholar 

  7. Delp E, Mitchell O (1979) Image compression using block truncation coding. IEEE Trans Commun 27(9):1335–1342

    Article  Google Scholar 

  8. Dick C, Krüger J, Westermann R (2009) GPU ray-casting for scalable terrain rendering. In: Proceedings of EUROGRAPHICS, vol 50. Citeseer

  9. Dumont R, Pellacini F, Ferwerda JA (2003) Perceptually-driven decision theory for interactive realistic rendering. ACM Trans Graph 22(2):152–181. doi:10.1145/636886.636888

    Article  Google Scholar 

  10. Durand F, Drettakis G, Thollot J, Puech C (2000) Conservative visibility preprocessing using extended projections. In: Proceedings of the 27th annual conference on computer graphics and interactive techniques. ACM Press/Addison-Wesley Publishing Co, pp 239–248

  11. Fenney S (2003) Texture compression using low-frequency signal modulation Proceedings of the ACM SIGGRAPH/EUROGRAPHICS conference on graphics hardware, HWWS ’03. Eurographics Association, Aire-la-Ville, pp 84–91

  12. Hollemeersch CF, Pieters B, Lambert P, Van de Walle R (2012) A new approach to combine texture compression and filtering. Vis Comput 28(4):371–385

  13. Hosseini M, Ahmed DT, Shirmohammadi S (2012) Adaptive 3D texture streaming in M3G-based mobile games. In: Proceedings of the 3rd multimedia systems conference. ACM, pp 143–148

  14. Iourcha KI, Nayak KS, Hong Z (2003) Fixed-rate block-based image compression with inferred pixel values. US Patent 6,658,146

  15. Kilgard MJ, Brown P, Zhang Y, Barsi A (2009) LATC OpenGL extension. https://www.opengl.org/registry/specs/EXT/texture/compression/latc.txt. Accessed 20 Jan 2016

  16. Luebke DP (2003) Level of detail for 3D graphics. Morgan Kaufmann

  17. Mallat SG (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 11(7):674–693

    Article  MATH  Google Scholar 

  18. Mavridis P, Papaioannou G (2012) Texture compression using wavelet decomposition. In: Computer graphics forum, vol 31. Wiley Online Library, pp 2107–2116

  19. Mittring M et al (2008) Advanced virtual texture topics. In: ACM SIGGRAPH 2008 games. ACM, pp 23–51

  20. Ngoc NP, Raemdonck Wv, Lafruit G, Deconinck G, Lauwereins R (2002) A QoS framework for interactive 3d applications

  21. Nystad J, Lassen A, Pomianowski A, Ellis S, Olson T (2012) Adaptive Scalable Texture Compression. In: Dachsbacher C, Munkberg J, Pantaleoni J (eds) High performance graphics. Eurographics Association, pp 105–114. http://dblp.uni-trier.de/db/conf/egh/hpg2012.html; doi:10.2312/EGGH/HPG12/105-114; http://www.bibsonomy.org/bibtex/202cb5a1d8c0a4f7deba6d82d0628abee/dblp

  22. Park J, Lee H (2016) A hierarchical framework for large 3D mesh streaming on mobile systems. Multimed Tools Appl 75(4):1983–2004. doi:10.1007/s11042-014-2383-6

    Article  Google Scholar 

  23. Sellers G, Boudier P, Obert J, Bolz J, Brown P (2013) Sparse texture OpenGL extension. https://www.opengl.org/registry/specs/ARB/sparse_texture.txt. Accessed 10 Feb 2016

  24. Sun Y, Dong Y, Tang Z (2015) Internet-based interactive visualization method of 3D lunar model with texture. Multimed Tools Appl 74(15):5449–5462. doi:10.1007/s11042-014-1863-z

    Article  Google Scholar 

  25. Treib M, Reichl F, Auer S, Westermann R (2012) Interactive editing of gigasample terrain fields. In: Computer graphics forum, vol 31. Wiley Online Library, pp 383–392

  26. Williams L (1983) Pyramidal parametrics. In: ACM Siggraph computer graphics, vol 17. ACM, pp 1–11

  27. Yu Y (1999) Efficient visibility processing for projective texture mapping. Comput Graph 23(2):245–253

    Article  MathSciNet  Google Scholar 

  28. Zorrilla M, Martin A, Sanchez JR, Tamayo I, Olaizola IG (2014) HTML5-based system for interoperable 3D digital home applications. Multimed Tools Appl 71(2):533–553. doi:10.1007/s11042-013-1516-7

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electronics and Informatics, Vrije Universiteit Brussel (VUB), Pleinlaan 2, 1050, Brussels, Belgium

    Bob Andries, Jan Lemeire & Adrian Munteanu

  2. Department of Industrial Sciences, Vrije Universiteit Brussel (VUB), Pleinlaan 2, 1050, Brussels, Belgium

    Jan Lemeire

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  1. Bob Andries
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  2. Jan Lemeire
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  3. Adrian Munteanu
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Correspondence to Bob Andries.

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Andries, B., Lemeire, J. & Munteanu, A. Optimized wavelet-based texture representation and streaming for GPU texture mapping. Multimed Tools Appl 77, 2873–2899 (2018). https://doi.org/10.1007/s11042-017-4433-3

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  • DOI: https://doi.org/10.1007/s11042-017-4433-3

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