Compression of Spectral Images using Spectral JPEG XL
Alban Fichet, Christoph Peters.
2025–03 in Journal of Computer Graphics Techniques (JCGT) 14, 1.
Official version
Abstract
The advantages of spectral rendering are increasingly well known, and corresponding rendering algorithms have matured. In this context, spectral images are used as input (e.g., reflectance and emission textures) and output of a renderer. Their large memory footprint is one of the big remaining issues with spectral rendering. Our method applies a cosine transform in the wavelength domain. We then reduce the dynamic range of higher-frequency Fourier coefficients by dividing them by the mean brightness, i.e., the Fourier coefficient for frequency zero. Then we store all coefficient images using JPEG XL. The mean brightness is perceptually most important and we store it with high quality. At higher frequencies, we use higher compression ratios and optionally lower resolutions. Our format supports the full feature set of spectral OpenEXR, but compared to this lossless compression, we achieve file sizes that are 10 to 60 times smaller than their ZIP compressed counterparts.
Keywords: spectral images, spectral rendering, lossy image compression, JPEG XL, hyperspectral images, multispectral images, cosine transform, Fourier coefficients, codec, decompression
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