Josiah Manson
Abstract
We present a method to create high-quality sampling filters by combining a prescribed number of texels from several resolutions in a mipmap. Our technique provides fine control over the number of texels we read per texture sample so that we can scale quality to match a memory bandwidth budget. Our method also has a fixed cost regardless of the filter we approximate, which makes it feasible to approximate higher-quality filters such as a Lánczos 2 filter in real-time rendering. To find the best set of texels to represent a given sampling filter and what weights to assign those texels, we perform a cardinality-constrained least-squares optimization of the most likely candidate solutions and encode the results of the optimization in a small table that is easily stored on the GPU. We present results that show we accurately reproduce filters using few texel reads and that both quality and speed scale smoothly with available bandwidth. When using four or more texels per sample, our image quality exceeds that of trilinear interpolation.
Supplemental Material
- Burt, P., and Adelson, E. 1983. The laplacian pyramid as a compact image code. IEEE Transactions on Communications 31, 4, 532--540.Google Scholar
- Burt, P. 1981. Fast filter transform for image processing. Computer Graphics and Image Processing 16, 1, 20--51.Google ScholarCross Ref
- Cant, R., and Shrubsole, P. 1997. Texture potential mapping: A way to provide antialiased texture without blurring. In Visualization and Modelling, 223--240.Google Scholar
- Cant, R., and Shrubsole, P. 2000. Texture potential mip mapping, a new high-quality texture antialiasing algorithm. ACM Transactions on Graphics 19, 3, 164--184. Google ScholarDigital Library
- Chen, B., Dachille, F., and Kaufman, A. E. 2004. Footprint area sampled texturing. IEEE Transactions on Visualization and Computer Graphics 10, 2, 230--240. Google ScholarDigital Library
- Crow, F. C. 1984. Summed-area tables for texture mapping. In SIGGRAPH, 207--212. Google ScholarDigital Library
- Duchon, C. 1979. Lanczos filtering in one and two dimensions. Journal of Applied Meteorology 18, 8, 1016--1022.Google ScholarCross Ref
- Fournier, A., Fiume, E., and Building, S. F. 1988. Constant-time filtering with space-variant kernels. In SIGGRAPH, 229--238. Google ScholarDigital Library
- Glassner, A. 1986. Adaptive precision in texture mapping. In SIGGRAPH, 297--306. Google ScholarDigital Library
- Gotsman, C. 1994. Constant-time filtering by singular value decomposition. Computer Graphics Forum 13, 2, 153--163.Google ScholarCross Ref
- Greene, N., and Heckbert, P. 1986. Creating raster omnimax images from multiple perspective views using the elliptical weighted average filter. IEEE Computer Graphics and Applications 6, 6, 21--27. Google ScholarDigital Library
- Heckbert, P. 1989. Fundamentals of Texture Mapping and Image Warping. Master's thesis, University of California, Berkeley.Google Scholar
- Hüttner, T., and Strasser, W. 1999. Fast footprint mipmapping. In Proceedings of the SIGGRAPH/EUROGRAPHICS workshop on graphics hardware, 35--44. Google ScholarDigital Library
- Igehy, H., Eldridge, M., and Proudfoot, K. 1998. Prefetching in a texture cache architecture. In Proceedings of the ACM SIGGRAPH/EUROGRAPHICS workshop on Graphics Hardware, 133--143. Google ScholarDigital Library
- Mallat, S. 1989. A theory for multiresolution signal decomposition: the wavelet representation. Pattern Analysis and Machine Intelligence, IEEE Transactions on 11, 7, 674--693. Google ScholarDigital Library
- Mavridis, P., and Papaioannou, G. 2011. High quality elliptical texture filtering on gpu. In Symposium on Interactive 3D Graphics and Games, 23--30. Google ScholarDigital Library
- McCormack, J., Perry, R. N., Farkas, K. I., and Jouppi, N. P. 1999. Feline: Fast elliptical lines for anisotropic texture mapping. In SIGGRAPH, 243--250. Google ScholarDigital Library
- Mitchell, D. P., and Netravali, A. N. 1988. Reconstruction filters in computer-graphics. ACM Computer Graphics 22, 221--228. Google ScholarDigital Library
- Schilling, A., Knittel, G., and Strasser, W. 1996. Texram: a smart memory for texturing. Computer Graphics and Applications, IEEE 16, 3, 32--41. Google ScholarDigital Library
- Shannon, C. 1949. Communication in the presence of noise. Proceedings of the IRE 37, 1, 10--21.Google Scholar
- Welch, W. J. 1982. Algorithmic complexity: three NP-hard problems in computational statistics. Journal of Statistical Computation and Simulation 15, 1, 17--25.Google ScholarCross Ref
- Williams, L. 1983. Pyramidal parametrics. In SIGGRAPH, 1--11. Google ScholarDigital Library
- Zhouchen Lin, L. W., and Wan, L. 2006. First order approximation for texture filtering. In Pacific Graphics Poster.Google Scholar
Index Terms
Cardinality-constrained texture filtering
Recommendations
Bilateral texture filtering
This paper presents a novel structure-preserving image decomposition operator called bilateral texture filter. As a simple modification of the original bilateral filter [Tomasi and Manduchi 1998], it performs local patch-based analysis of texture ...
Multi-structure local binary patterns for texture classification
Recently, the local binary patterns (LBP) have been widely used in the texture classification. The LBP methods obtain the binary pattern by comparing the gray scales of pixels on a small circular region with the gray scale of their central pixel. The ...
Filtering of Images by Kalman Filter Using VHDL
ICETET '10: Proceedings of the 2010 3rd International Conference on Emerging Trends in Engineering and TechnologyA Kalman filter is an optimal estimator that infers parameters of interest from indirect inaccurate and uncertain observations. It is recursive so that new measurement can be processed as they arrive. In this paper first we take the input image then add ...
Comments