NVIDIA Neural Compression Technique Unlocks 16x Texture Detail

wccftech.com:

NVIDIA has always been at the forefront of AI research, such as when it created Deep Learning Super Sampling (DLSS). However, image reconstruction and upscaling are only one of many research fields where neural graphics techniques are applicable.

At the upcoming SIGGRAPH 2023, which will take place between August 6th and 10th in Los Angeles, NVIDIA will present 20 papers on generative AI and neural graphics. For those undaunted by intriguing yet very technical reads, all of the publications are listed on this page.

In this article, I'll go through some of the most interesting techniques outlined in the new NVIDIA papers for game developers. By far the most readily applicable is the neural compression technique for material textures described in Random-Access Neural Compression of Material Textures (Karthik Vaidyanathan, Marco Salvi, Bartlomiej Wronski, Tomas Akenine‑Möller, Pontus Ebelin, Aaron Lefohn).

The team of NVIDIA engineers posited the need to reduce texture storage requirements at a time when assets are of extremely high quality but also ask for increasingly large amounts of disk space. To achieve this goal, they've combined GPU textures compression with neural compression techniques.

Using this approach we enable low-bitrate compression, unlocking two additional levels of detail (or 16× more texels) with similar storage requirements as commonly used texture compression techniques. In practical terms, this allows a viewer to get very close to an object before losing significant texture detail. Our main contributions are: • A novel approach to texture compression that exploits redundancies spatially, across mipmap levels, and across different material channels. By optimizing for reduced distortion at a low bitrate,