TexNN: Fast Texture Encoding Using Neural Networks
| dc.contributor.author | Pratapa, S. | en_US |
| dc.contributor.author | Olson, T. | en_US |
| dc.contributor.author | Chalfin, A. | en_US |
| dc.contributor.author | Manocha, D. | en_US |
| dc.contributor.editor | Chen, Min and Benes, Bedrich | en_US |
| dc.date.accessioned | 2019-03-17T09:56:56Z | |
| dc.date.available | 2019-03-17T09:56:56Z | |
| dc.date.issued | 2019 | |
| dc.description.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. | en_US |
| dc.description.number | 1 | |
| dc.description.sectionheaders | Articles | |
| dc.description.seriesinformation | Computer Graphics Forum | |
| dc.description.volume | 38 | |
| dc.identifier.doi | 10.1111/cgf.13534 | |
| dc.identifier.issn | 1467-8659 | |
| dc.identifier.pages | 328-339 | |
| dc.identifier.uri | https://doi.org/10.1111/cgf.13534 | |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10.1111/cgf13534 | |
| dc.publisher | © 2019 The Eurographics Association and John Wiley & Sons Ltd. | en_US |
| dc.subject | data compression | |
| dc.subject | modelling | |
| dc.subject | image compression | |
| dc.subject | image and video processing | |
| dc.subject | GPUs and their application for general‐purpose computing | |
| dc.subject | hardware | |
| dc.subject | CCS Concepts •Computing methodologies→ Neural networks; Image compression; Supervised learning; Graphics file formats | |
| dc.title | TexNN: Fast Texture Encoding Using Neural Networks | en_US |