  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.
