“High dynamic range texture compression for graphics hardware” by Munkberg, Clarberg, Hasselgren and Akenine-Moeller

  • ©Jacob Munkberg, Petrik Clarberg, Jon Hasselgren, and Tomas Akenine-Moeller

Conference:


Type:


Title:

    High dynamic range texture compression for graphics hardware

Presenter(s)/Author(s):



Abstract:


    In this paper, we break new ground by presenting algorithms for fixed-rate compression of high dynamic range textures at low bit rates. First, the S3TC low dynamic range texture compression scheme is extended in order to enable compression of HDR data. Second, we introduce a novel robust algorithm that offers superior image quality. Our algorithm can be efficiently implemented in hardware, and supports textures with a dynamic range of over 109:1. At a fixed rate of 8 bits per pixel, we obtain results virtually indistinguishable from uncompressed HDR textures at 48 bits per pixel. Our research can have a big impact on graphics hardware and real-time rendering, since HDR texturing suddenly becomes affordable.

References:


    1. Beers, A., Agrawala, M., and Chadda, N. 1996. Rendering from Compressed Textures. In Proceedings of ACM SIGGRAPH 96, 373–378. Google ScholarDigital Library
    2. Bogart, R., Kainz, F., and Hess, D. 2003. OpenEXR Image File Format. In ACM SIGGRAPH Sketches & Applications.Google Scholar
    3. Chalmers, A., McNamara, A., Daly, S., Myszkowski, K., and Troscianko, T. 2000. Image Quality Metrics. In ACM SIGGRAPH Course Notes.Google Scholar
    4. Cohen, J., Tchou, C., Hawkins, T., and Debevec, P. 2001. Real-Time High Dynamic Range Texture Mapping. In Eurographics Workshop on Rendering, 313–320. Google ScholarDigital Library
    5. Daly, S. 1993. The Visible Differences Predictor: An Algorithm for the Assessment of Image Fidelity. In Digital Images and Human Vision. MIT Press, 179–206. Google ScholarDigital Library
    6. Debevec, P. E., and Malik, J. 1997. Recovering High Dynamic Range Radiance Maps from Photographs. In Proceedings of ACM SIGGRAPH 97, 369–378. Google ScholarDigital Library
    7. Debevec, P. E. 1998. Rendering Synthetic Objects into Real Scenes: Bridging Traditional and Image-Based Graphics with Global Illumination and High Dynamic Range Photography. In Proceedings of ACM SIGGRAPH 98, 189–198. Google ScholarDigital Library
    8. Dryden, I., and Mardia, K. 1998. Statistical Shape Analysis. Wiley.Google Scholar
    9. Fenney, S. 2003. Texture Compression using Low-Frequency Signal Modulation. In Graphics Hardware, 84–91. Google ScholarDigital Library
    10. Iourcha, K., Nayak, K., and Hong, Z., 1999. System and Method for Fixed-Rate Block-Based Image Compression with Inferred Pixel Values. US Patent 5,956,431.Google Scholar
    11. Knittel, G., Schilling, A. G., Kugler, A., and Strasser, W. 1996. Hardware for Superior Texture Performance. Computers & Graphics, 20, 4, 475–481.Google ScholarCross Ref
    12. Li, Y., Sharan, L., and Adelson, E. H. 2005. Compressing and Companding High Dynamic Range Images with Subband Architectures. ACM Transactions on Graphics, 24, 3, 836–844. Google ScholarDigital Library
    13. Mantiuk, R., Krawczyk, G., Myszkowski, K., and Seidel, H.-P. 2004. Perception-Motivated High Dynamic Range Video Encoding. ACM Transactions on Graphics, 23, 3, 733–741. Google ScholarDigital Library
    14. Mantiuk, R., Daly, S., Myszkowski, K., and Seidel, H.-P. 2005. Predicting Visible Differences in High Dynamic Range Images — Model and its Calibration. In Human Vision and Electronic Imaging X, 204–214.Google Scholar
    15. Owens, J. D. 2005. Streaming Architectures and Technology Trends. In GPU Gems 2. Addison-Wesley, 457–470.Google Scholar
    16. Pereberin, A. 1999. Hierarchical Approach for Texture Compression. In Proceedings of GraphiCon ’99, 195–199.Google Scholar
    17. Poynton, C. 2003. Digital Video and HDTV Algorithms and Interfaces. Morgan Kaufmann Publishers. Google ScholarDigital Library
    18. Reinhard, E., Ward, G., Pattanaik, S., and Debevec, P. 2005. High Dynamic Range Imaging: Acquisition, Display and Image-Based Lighting. Morgan Kaufmann Publishers. Google ScholarDigital Library
    19. Sangwine, S. J., and Horne, R. E. N., Eds. 1998. The Colour Image Processing Handbook. Chapman and Hill. Google ScholarDigital Library
    20. Seetzen, H., Whitehead, L. A., and Ward, G. 2003. A High Dynamic Range Display Using Low and High Resolution Modulators. Society for Information Display Internatiational Symposium Digest of Technical Papers, 1450–1453.Google Scholar
    21. Seetzen, H., Heidrich, W., Stuerzlinger, W., Ward, G., Whitehead, L., Trentacoste, M., Ghosh, A., and Vorozcovs, A. 2004. High Dynamic Range Display Systems. ACM Transactions on Graphics 23, 3, 760–768. Google ScholarDigital Library
    22. Ström, J., and Akenine-Möller, T. 2005. iPACKMAN: High-Quality, Low-Complexity Texture Compression for Mobile Phones. In Graphics Hardware, 63–70. Google ScholarDigital Library
    23. Torborg, J., and Kajiya, J. 1996. Talisman: Commodity Realtime 3D Graphics for the PC. In Proceedings of SIGGRAPH, 353–364. Google ScholarDigital Library
    24. Ward, G., and Simmons, M. 2004. Subband Encoding of High Dynamic Range Imagery. In Proceedings of APGV ’04, 83–90. Google ScholarDigital Library
    25. Ward, G. 1991. Real Pixels. In Graphics Gems II. Academic Press, 80–83.Google Scholar
    26. Ward, G. J. 1994. The RADIANCE Lighting Simulation and Rendering System. In Proceedings of ACM SIGGRAPH 94, 459–472. Google ScholarDigital Library
    27. Ward, G. L. 1998. LogLuv Encoding for Full Gamut High Dynamic Range Images. Journal of Graphics Tools, 3, 1, 15–31. Google ScholarDigital Library
    28. Ward, G., 2005. High Dynamic Range Image Encodings, http://www.anyhere.com/.Google Scholar
    29. Xu, R., Pattanaik, S. N., and Hughes, C. E. 2005. High-Dynamic-Range Still-Image Encoding in JPEG 2000. IEEE Computer Graphics and Applications, 25, 6, 57–64. Google ScholarDigital Library


ACM Digital Library Publication:



Overview Page: