IEEE Computer Graphics and Applications
IEEE Computer Graphics and Applications
Computer arithmetic algorithms
Computer arithmetic algorithms
A new simple and efficient antialiasing with subpixel masks
Proceedings of the 18th annual conference on Computer graphics and interactive techniques
Evaluation of high performance multicache parallel texture mapping
ICS '98 Proceedings of the 12th international conference on Supercomputing
Unsolved problems and opportunities for high-quality, high-performance 3D graphics on a PC platform
HWWS '98 Proceedings of the ACM SIGGRAPH/EUROGRAPHICS workshop on Graphics hardware
Prefetching in a texture cache architecture
HWWS '98 Proceedings of the ACM SIGGRAPH/EUROGRAPHICS workshop on Graphics hardware
Feline: fast elliptical lines for anisotropic texture mapping
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
HWWS '99 Proceedings of the ACM SIGGRAPH/EUROGRAPHICS workshop on Graphics hardware
Texture potential MIP mapping, a new high-quality texture antialiasing algorithm
ACM Transactions on Graphics (TOG)
Texram: A Smart Memory for Texturing
IEEE Computer Graphics and Applications
SIGGRAPH '83 Proceedings of the 10th annual conference on Computer graphics and interactive techniques
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Texture mapping is one of the techniques that express realism in three-dimensional (3-D) graphics. To produce high-quality images, various anisotropic filtering methods have been proposed for texture mapping. These methods require more texels than isotropic (trilinear) filtering method. In spite of increases to texture memory bandwidth, however, texture memory bandwidth is still a bottleneck of texture-filtering hardware. Consequently, an exact filtering method is required for good-quality images in a limited texture memory bandwidth. In this paper, we propose anisotropic texture filtering based on edge functions. Our method proposes an exact footprint-shape approximation with edge functions for generating weights. For real-time filtering, the weight plays a key role in effective filtering of the restricted texels loaded from memory. The normalized value of the edge function gives the distance relative to the contribution of texels to a final intensity. Calculating a Gaussian filter using this normalized value, generates a good weight. The quality of rendered images is superior to other anisotropic filtering methods with the same restricted number of texels. For images of the same quality, our method requires less than half the texels of other methods. Consequently, the improvement in performance is more than twice that of other methods. With low hardware overheads, our method can be implemented at a reasonable cost. In practice, the algorithm is demonstrated through VLSI implementation. The hardware, which is described by verilog and synthesized with a 0.35-µm 3.3-V standard cell library, is operated at 100 MHz and it generates 100 M texture-filtered RGB pixel-color values per second.