Fast Texture Synthesis Treestructure Vector Quantization：快速纹理合成树结构矢量量化.ppt

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2017-12-23 发布于重庆
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Fast Texture Synthesis Treestructure Vector Quantization：快速纹理合成树结构矢量量化.ppt

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Fast Texture Synthesis Treestructure Vector Quantization：快速纹理合成树结构矢量量化

Fast Texture Synthesis Tree-structure Vector Quantization Li-Yi Wei Marc LevoyStanford University Outline Introduction Algorithm TSVQ Acceleration Applications Introdction How texture differ from images? Gaussian pyramid Introdcution In this paper, we present a very simple algorithm that can efficiently synthesize a wide variety of textures. The inputs consist of an example texture patch and a random noise image with size specified by user. The algorithm modifies this random noise to make it look like the given example. New textures can be generated with little computation time, and their tileability is guaranteed. Algorithm Single resolution synthesis Neighborhood Multiresolution synthesis Edge handing initialization Single resolution synthesis The algorithm starts with an input texture sample Ia and a white random noise Is. We force the random noise Is to look like Ia by transforming Is pixel by pixel in a raster scan ordering. To determine the pixel value p at Is, its spatial neighborhood N(p) is compare against all possible neighborhoods N(pi) from Ia. The input pixel pi with the most similar N(pi) is assigned to p. We use a simple L2 norm (sum of squared difference) to measure the similarity between the neighborhoods. Single resolution synthesis Neighborhood Because the set of local neighborhoods N(pi) is used as the primary model for textures, the quality of the synthesized results will depend on its size and shape. The shape of the neighborhood will directly determine the quality of Is. Neighborhood Neighborhood Multiresolution synthesis For textures containing large scale structures we have to use large neighborhoods, and large neighborhoods demand more computation. This problem can be solved by using a multiresloution image pyramid. Multiresolution synthesis Two Gaussian pyramids, Ga and Gs, are first built from Ia and Is, respectively. The algorithm then transfroms Gs from lower to higher resolutions, such that each higher resolution level is construct