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., 16x 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.

翻译：渲染中真实感的持续进步伴随着纹理数据的增长，进而对存储和内存提出了更高需求。为解决这一问题，我们提出了一种专为材质纹理设计的新型神经压缩技术。通过低比特率压缩，我们解锁了更多细节层级（即16倍纹素量），在图像质量上优于AVIF和JPEG XL等先进图像压缩技术。同时，我们的方法支持按需实时解压缩，并具备类似于GPU上块纹理压缩的随机访问能力，从而实现了磁盘和内存的压缩。该方法的核心思想是：将多种材质纹理及其mipmap链联合压缩，并利用为每种材质优化的轻量级神经网络进行解压缩。最后，我们采用定制化训练方案实现实用级压缩速度，其性能较PyTorch等通用框架提升一个数量级。