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Random-Access Neural Compression of Material Textures

Vaidyanathan, Karthik ; Salvi, Marco ; Wronski, Bartlomiej ; Akenine-Möller, Tomas LU ; Ebelin, Pontus LU orcid and Lefohn, Aaron (2023) In ACM Transactions on Graphics p.1-25
Abstract
The continuous advancement of photorealism in rendering is accompanied
by a growth in texture data and, consequently, increasing storage and memory demands. To address this issue, we propose a novel neural compression
technique specifically designed for material textures. We unlock two more
levels of detail, i.e., 16× more texels, using low bitrate compression, with
image quality that is better than advanced image compression techniques,
such as AVIF and JPEG XL.
At the same time, our method allows on-demand, real-time decompression
with random access similar to block texture compression on GPUs, enabling
compression on disk and memory. The key idea behind our approach is
compressing multiple material... (More)
The continuous advancement of photorealism in rendering is accompanied
by a growth in texture data and, consequently, increasing storage and memory demands. To address this issue, we propose a novel neural compression
technique specifically designed for material textures. We unlock two more
levels of detail, i.e., 16× more texels, using low bitrate compression, with
image quality that is better than advanced image compression techniques,
such as AVIF and JPEG XL.
At the same time, our method allows on-demand, real-time decompression
with random access similar to block texture compression on GPUs, enabling
compression on disk and memory. The key idea behind our approach is
compressing multiple material textures and their mipmap chains together,
and using a small neural network, that is optimized for each material, to
decompress them. Finally, we use a custom training implementation to
achieve practical compression speeds, whose performance surpasses that of
general frameworks, like PyTorch, by an order of magnitude (Less)
Please use this url to cite or link to this publication:
author
; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
ACM Transactions on Graphics
article number
88
pages
25 pages
publisher
Association for Computing Machinery (ACM)
external identifiers
  • scopus:85166620426
ISSN
0730-0301
project
Evaluating and Improving Rendered Visual Experiences
WASP: Wallenberg AI, Autonomous Systems and Software Program at Lund University
language
English
LU publication?
yes
id
33eb6ce9-340b-4a31-a9d5-fb73baa016b7
alternative location
https://dl.acm.org/doi/pdf/10.1145/3592407
date added to LUP
2023-05-15 08:38:51
date last changed
2024-03-04 15:29:39
@article{33eb6ce9-340b-4a31-a9d5-fb73baa016b7,
  abstract     = {{The continuous advancement of photorealism in rendering is accompanied<br/>by a growth in texture data and, consequently, increasing storage and memory demands. To address this issue, we propose a novel neural compression<br/>technique specifically designed for material textures. We unlock two more<br/>levels of detail, i.e., 16× more texels, using low bitrate compression, with<br/>image quality that is better than advanced image compression techniques,<br/>such as AVIF and JPEG XL.<br/>At the same time, our method allows on-demand, real-time decompression<br/>with random access similar to block texture compression on GPUs, enabling<br/>compression on disk and memory. The key idea behind our approach is<br/>compressing multiple material textures and their mipmap chains together,<br/>and using a small neural network, that is optimized for each material, to<br/>decompress them. Finally, we use a custom training implementation to<br/>achieve practical compression speeds, whose performance surpasses that of<br/>general frameworks, like PyTorch, by an order of magnitude}},
  author       = {{Vaidyanathan, Karthik and Salvi, Marco and Wronski, Bartlomiej and Akenine-Möller, Tomas and Ebelin, Pontus and Lefohn, Aaron}},
  issn         = {{0730-0301}},
  language     = {{eng}},
  month        = {{07}},
  pages        = {{1--25}},
  publisher    = {{Association for Computing Machinery (ACM)}},
  series       = {{ACM Transactions on Graphics}},
  title        = {{Random-Access Neural Compression of Material Textures}},
  url          = {{https://dl.acm.org/doi/pdf/10.1145/3592407}},
  year         = {{2023}},
}