“High dynamic range texture compression” by Roimela, Aarnio and Itäranta
Conference:
Type(s):
Title:
- High dynamic range texture compression
Presenter(s)/Author(s):
Abstract:
We present a novel compression scheme for high dynamic range textures, targeted for hardware implementation. Our method encodes images at a constant 8 bits per pixel, for a compression ratio of 6:1. We demonstrate that our method achieves good visual fidelity, surpassing DXTC texture compression of RGBE data which is the most practical method on existing graphics hardware. The decoding logic for our method is simple enough to be implemented as part of the texture fetch unit in graphics hardware.
References:
1. Beers, A. C., Agrawala, M., and Chaddha, N. 1996. Rendering from compressed textures. In SIGGRAPH ’96: Proceedings of the 23rd annual conference on Computer graphics and interactive techniques, ACM Press, New York, NY, USA, 373–378. Google ScholarDigital Library
2. Blinn, J. F. 1997. Floating-point tricks. IEEE Computer Graphics and Applications 17, 4, 80–84. Google ScholarDigital Library
3. Fenney, S. 2003. Texture compression using low-frequency signal modulation. In HWWS ’03: Proceedings of the ACM SIGGRAPH/EUROGRAPHICS conference on Graphics hardware, Eurographics Association, Aire-la-Ville, Switzerland, Switzerland, 84–91. Google ScholarDigital Library
4. 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
5. Knittel, G., Schilling, A. G., Kugler, A., and Strasser, W. 1996. Hardware for superior texture performance. Computers & Graphics 20, 4 (July), 475–481.Google ScholarCross Ref
6. Mantiuk, R., Krawczyk, G., Myszkowski, K., and Seidel, H.-P. 2004. Perception-motivated high dynamic range video encoding. ACM Trans. Graph. 23, 3, 733–741. Google ScholarDigital Library
7. 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, IST/SPIE’s 17th Annual Symposium on Electronic Imaging (2005), vol. 5666, 204–214.Google Scholar
8. Persson, E. 2005. HDR texturing. Radeon SDK, October 2005, ATI Technologies, Inc.Google Scholar
9. Reinhard, E., Ward, G., Pattanaik, S., and Debevec, P. 2006. High Dynamic Range Imaging. Morgan Kaufmann Publishers.Google Scholar
10. Ström, J., and Akenine-Möller, T. 2005. iPACKMAN: high-quality, low-complexity texture compression for mobile phones. In HWWS ’05: Proceedings of the ACM SIGGRAPH/EUROGRAPHICS conference on Graphics hardware, ACM Press, New York, NY, USA, 63–70. Google ScholarDigital Library
11. Ward Larson, G. 1998. The LogLuv encoding for full gamut, high dynamic range images. Journal of Graphics Tools 3, 1, 15–31. Google ScholarDigital Library
12. Ward, G. 1992. Real pixels. In Graphics Gems II, J. Arvo, Ed.Google Scholar
13. Ward, G., 2005. High dynamic range image encodings. http://www.anyhere.com/gward/hdrenc/hdr_encodings.html. (Dec. 2005).Google Scholar
14. Ward, G. 2005. JPEG-HDR: A backwards-compatible, high dynamic range extension to JPEG. In Proceedings of the Thirteenth Color Imaging Conference. Google ScholarDigital Library
15. 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
