Importantly, it then applies a weighting step, dividing higher-frequency spectral coefficients by the overall brightness (the DC component), allowing less important data to be compressed more aggressively. That is then fed into the codec, and rather than inventing a completely new file type, the method uses the compression engine and features of the standardized JPEG XL image format to store the specially prepared spectral data.

Making spectral images easier to work with

According to the researchers, the massive file sizes of spectral images have reportedly been a real barrier to adoption in industries that would benefit from their accuracy. Smaller files mean faster transfer times, reduced storage costs, and the ability to work with these images more interactively without specialized hardware.

The results reported by the researchers seem impressive—with their technique, spectral image files shrink by 10 to 60 times compared to standard OpenEXR lossless compression, bringing them down to sizes comparable to regular high-quality photos. They also preserve key OpenEXR features like metadata and high dynamic range support.

While some information is sacrificed in the compression process—making this a “lossy” format—the researchers designed it to discard the least noticeable details first, focusing compression artifacts in the less important high-frequency spectral details to preserve important visual information.

Of course, there are some limitations. Translating these research results into widespread practical use hinges on the continued development and refinement of the software tools that handle JPEG XL encoding and decoding. Like many cutting-edge formats, the initial software implementations may need further development to fully unlock every feature. It’s a work in progress.

And while Spectral JPEG XL dramatically reduces file sizes, its lossy approach may pose drawbacks for some scientific applications. Some researchers working with spectral data might readily accept the trade-off for the practical benefits of smaller files and faster processing. Others handling particularly sensitive measurements might need to seek alternative methods of storage.

For now, the new technique remains primarily of interest to specialized fields like scientific visualization and high-end rendering. However, as industries from automotive design to medical imaging continue generating larger spectral datasets, compression techniques like this could help make those massive files more practical to work with.