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Neural Texture Block Compression
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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 70% less storage footprint, preserving real-time performance with a modest computational overhead at the texture loading phase in the graphics pipeline.
Forward citations
Cited by 2 Pith papers
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GATE: Geometry-Aware Trained Encoding
A geometry-aware encoding stores trainable feature vectors on triangle surfaces and outperforms hash grids in speed and often quality for neural ambient occlusion and radiance caching.
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Hardware Accelerated Neural Block Texture Compression with Cooperative Vectors
Neural texture sets can be stored as low-range BC1 blocks and decoded with cooperative-vector hardware, giving comparable quality at up to half the memory of prior BC6-based methods.
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