Neural Texture Block Compression
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Date
2024
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Block compression is a widely used technique to compress textures in real-time graphics applications, offering a reduction in storage size. However, their storage efficiency is constrained by the fixed compression ratio, which substantially increases storage size when hundreds of high-quality textures are required. In this paper, we propose a novel block texture compression method with neural networks, Neural Texture Block Compression (NTBC). NTBC learns the mapping from uncompressed textures to block-compressed textures, which allows for significantly reduced storage costs without any change in the shaders. Our experiments show that NTBC can achieve reasonable-quality results with up to about 45% less storage footprint, preserving real-time performance with a modest computational overhead at the texture loading phase in the graphics pipeline.
Description
CCS Concepts: Computing methodologies → Image compression; Texturing; Image representations
@inproceedings{10.2312:mam.20241178,
booktitle = {Workshop on Material Appearance ModelingJoint MAM - MANER Conference - Material Appearance Network for Education and Research},
editor = {Hardeberg, Jon Yngve and Rushmeier, Holly},
title = {{Neural Texture Block Compression}},
author = {Fujieda, Shin and Harada, Takahiro},
year = {2024},
publisher = {The Eurographics Association},
ISSN = {2309-5059},
ISBN = {978-3-03868-264-6},
DOI = {10.2312/mam.20241178}
}
@inproceedings{10.2312:mam.20241178,
booktitle = {Workshop on Material Appearance ModelingJoint MAM - MANER Conference - Material Appearance Network for Education and Research},
editor = {Hardeberg, Jon Yngve and Rushmeier, Holly},
title = {{Neural Texture Block Compression}},
author = {Fujieda, Shin and Harada, Takahiro},
year = {2024},
publisher = {The Eurographics Association},
ISSN = {2309-5059},
ISBN = {978-3-03868-264-6},
DOI = {10.2312/mam.20241178}
}