TexNN: Fast Texture Encoding Using Neural Networks
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Date
2019
Authors
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Publisher
© 2019 The Eurographics Association and John Wiley & Sons Ltd.
Abstract
We present a novel deep learning‐based method for fast encoding of textures into current texture compression formats. Our approach uses state‐of‐the‐art neural network methods to compute the appropriate encoding configurations for fast compression. A key bottleneck in the current encoding algorithms is the search step, and we reduce that computation to a classification problem. We use a trained neural network approximation to quickly compute the encoding configuration for a given texture. We have evaluated our approach for compressing the textures for the widely used adaptive scalable texture compression format and evaluate the performance for different block sizes corresponding to 4 × 4, 6 × 6 and 8 × 8. Overall, our method (TexNN) speeds up the encoding computation up to an order of magnitude compared to prior compression algorithms with very little or no loss in the visual quality.We present a novel deep learning‐based method for fast encoding of textures into current texture compression formats. Our approach uses state‐of‐the‐art neural network methods to compute the appropriate encoding configurations for fast compression. A key bottleneck in the current encoding algorithms is the search step, and we reduce that computation to a classification problem. We use a trained neural network approximation to quickly compute the encoding configuration for a given texture.We have evaluated our approach for compressing the textures for the widely used adaptive scalable texture compression format and evaluate the performance for different block sizes corresponding to 4 × 4, 6 × 6 and 8 × 8.
Description
@article{10.1111:cgf.13534,
journal = {Computer Graphics Forum},
title = {{TexNN: Fast Texture Encoding Using Neural Networks}},
author = {Pratapa, S. and Olson, T. and Chalfin, A. and Manocha, D.},
year = {2019},
publisher = {© 2019 The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {10.1111/cgf.13534}
}