Beyond RGB: A new image file format efficiently stores invisible light data

Comments

• AbouBenAdhem@lemmy.world ⁨5⁩ ⁨days⁩ ago

  Spectral JPEG XL utilizes a technique used with human-visible images, a math trick called a discrete cosine transform (DCT), to make these massive files smaller […] 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.

  This all sounds like standard jpeg compression. Is it just jpeg with extra channels?

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  • Prok@lemmy.world ⁨5⁩ ⁨days⁩ ago

    Yeah, it compresses better too though, and jpeg XL can be configured to compress lossless, which I imagine would also work here

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    • dohpaz42@lemmy.world ⁨5⁩ ⁨days⁩ ago

      Lossless JPEG would be amazing.

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  • wischi@programming.dev ⁨4⁩ ⁨days⁩ ago

    It’s not just like jpeg with extra channels. It’s technically far superior, supports loss less compression, and the way the decompression works would make thumbnails obsolete. It can even recompress already existing jpeg even smaller without additional generation less. It’s hard to describe what a major step this format would be without getting very technical. A lot of operating systems and software already support is but the Google chrome team is practically preventing adoption because of company politics.

    issues.chromium.org/issues/40168998

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    • uis@lemm.ee ⁨4⁩ ⁨days⁩ ago

      Both og JPEG and JXL support lossless compression.

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  • zerofk@lemm.ee ⁨5⁩ ⁨days⁩ ago

    Kind of, but JPEG converts image data to its own internal 3 came channel colour space before applying DCT. It is not compressing the R, G and B channels of most images. So a multichannel compression is not just compressing each channel separately.

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    • AbouBenAdhem@lemmy.world ⁨5⁩ ⁨days⁩ ago

      Yeah, jpeg converts to lab (or something similar, I think). But the dimensions are the same: one channel for lightness, and then a number of channels one less than the total number of sampled frequencies to capture the rest of the color space.

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• zerofk@lemm.ee ⁨5⁩ ⁨days⁩ ago

  “And while Spectral JPEG XL dramatically reduces file sizes, its lossy approach may pose drawbacks for some scientific applications.”

  This is the part that confuses me. First of all, many applications that need spectral data need it to be as accurate as possible. Lossy compression in that might not be acceptable.

  More interestingly (and I’ll read the actual paper for this): which data will be more compressed? Simply put, JPEG achieves its best compression by keeping the brightness but discarding colour. Which dimension in which spectral space do the researchers think can be more compressed than others? In this case there is no human visual system to base the decision on.

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  • rice@lemmy.org ⁨5⁩ ⁨days⁩ ago

    jpeg xl does support lossless and their 69 page paper does mention this so I am unsure why they are putting the lossy aspect of this as the comparison to their “lossless ZIP COMPRESSION of OpenEXR”

    page 51 has more detail on compression stuff. The openEXR does also support lossy. Anyway I think page 51-52 would answer it for someone that knows more about openEXR which I sure don’t

    Their comparison images do clearly show data being lost as well so they aren’t even using visually lossless of jpeg xl they are actually just going full lossy. Must be some use case somewhere?

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  • hera@feddit.uk ⁨5⁩ ⁨days⁩ ago

    I literally can’t think of a scientific use case where lossy compression would be acceptable?

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    • ayyy@sh.itjust.works ⁨4⁩ ⁨days⁩ ago

      Perhaps training an image classifier?

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• pelya@lemmy.world ⁨4⁩ ⁨days⁩ ago

  What, pickle.dump your enormous Numpy array not good enough for you anymore? Not even fancy zlib.compress(pickle.dumps(enormousNumpyArray)) will satisfy you? Are you a scientist or a spectral data photographer?

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  • KingRandomGuy@lemmy.world ⁨4⁩ ⁨days⁩ ago

    I guess part of the reason is to have a standardized method for multi and hyper spectral images, especially for storing things like metadata. Simply storing a numpy array may not be ideal if you don’t keep metadata on what is being stored and in what order (i.e. axis order, what channel corresponds to each frequency band, etc.). Plus it seems like they extend lossy compression to this modality which could be useful for some circumstances (though for scientific use you’d probably want lossless).

    If compression isn’t the concern, certainly other formats could work to store metadata in a standardized way. FITS, the image format used in astronomy, comes to mind.

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    • pelya@lemmy.world ⁨4⁩ ⁨days⁩ ago

      Saving arbitrary metadata is the exact use case for pickle module, you just put it together with your numpy array into a tuple. jpeg format has support for storing metadata, but they are an afterthought like .mp3 tags, half of applications do not support them.

      I can imagine multichannel jpeg to be used in photo editing software, so you can effortlessly create false-color plots of your infrared data, maybe even apply a beauty filter to your Eagle Nebula microwave scans.

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• rdri@lemmy.world ⁨4⁩ ⁨days⁩ ago

  Last I checked, JPEG XL takes a lot of time and resources to encode (create) an image, if you actually want it to be far more optimized than JPEG.

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