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dc.contributor.authorPratapa, S.en_US
dc.contributor.authorOlson, T.en_US
dc.contributor.authorChalfin, A.en_US
dc.contributor.authorManocha, D.en_US
dc.contributor.editorChen, Min and Benes, Bedrichen_US
dc.date.accessioned2019-03-17T09:56:56Z
dc.date.available2019-03-17T09:56:56Z
dc.date.issued2019
dc.identifier.issn1467-8659
dc.identifier.urihttps://doi.org/10.1111/cgf.13534
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf13534
dc.description.abstractWe 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.en_US
dc.publisher© 2019 The Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectdata compression
dc.subjectmodelling
dc.subjectimage compression
dc.subjectimage and video processing
dc.subjectGPUs and their application for general‐purpose computing
dc.subjecthardware
dc.subjectCCS Concepts •Computing methodologies→ Neural networks; Image compression; Supervised learning; Graphics file formats
dc.titleTexNN: Fast Texture Encoding Using Neural Networksen_US
dc.description.seriesinformationComputer Graphics Forum
dc.description.sectionheadersArticles
dc.description.volume38
dc.description.number1
dc.identifier.doi10.1111/cgf.13534
dc.identifier.pages328-339


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