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.

翻译：块压缩是实时图形应用中广泛使用的纹理压缩技术，能够有效减少存储空间。然而，其存储效率受限于固定压缩比，当需要数百个高质量纹理时，存储空间会显著增加。本文提出一种基于神经网络的新型块纹理压缩方法——神经纹理块压缩（NTBC）。NTBC学习从非压缩纹理到块压缩纹理的映射关系，可在不修改着色器的前提下大幅降低存储开销。实验表明，NTBC能以约70%的存储空间降幅获得质量合理的结果，在图形管线的纹理加载阶段仅引入适度计算开销，同时保持实时性能。