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In JPEG* The title should reflect that. Obviously this is misleading otherwise, there are a million ways of representing grayscale in data. Sure, you can arg

In JPEG* The title should reflect that. Obviously this is misleading otherwise, there are a million ways of representing grayscale in data. Sure, you can argue just picking a color channel and making it the grayscale source is not gonna work as expected, but what if we're talking about the data off a pan sensor vs an RGB array? What about a pan sensor created by taking an RGB sensor and removing the filter so it's producing an image with 3x the resolution but in pan? This is a complicated question.

generalizations 2y ago
Yup. "Do boolean matrices take up less space than fp32 matrices?"
- financltravsty 2y ago
  Depends on compression ;)
  - generalizations 2y ago
    Heh. Takes a scifi-level compression algorithm to make 32 bits comparable to 1 bit...or you'd need to get really lucky with your sparse matrix ;)
    - kbelder 2y ago
      But the other way around, it's all too common that a 1 bit array is stored equivalently to a 32 bit array.
      - generalizations 2y ago
        Yes, indeed. If a larger data type is used to represent the information, it will most certainly take up more space.
kadoban 2y ago
> In JPEG* This should end up being true for any presentation image format (the one you're going to actually show people, not necessarily the one you might do editing work on) optimized for human vision as well. It's more a property of our eyes than a quirk of JPEG. So I don't find it misleading personally.
- AnarchismIsCool 2y ago
  I'd argue compressed file formats make up a lot less of the broader problem space than most people realize. If you're doing anything high performance (where the difference matters) but not immediately consumer facing you're spending a lot of time working with lossless/uncompressed imagery in ram/disk. Stuff like MIPI->CUDA for classification, this question can go a number of different ways very quickly. Keep in mind, at least in my experience, if you're even remotely thinking about doing anything with grayscale and this question comes up you're probably doing something...interesting (read: multi band remote sensing shenanigans).
GuB-42 2y ago
This would be the case for any lossy format with decent compression. We are much less sensitive to color than we are to brightness so we can compress color much more than we can compress brightness for the same perceived quality. Not only that but color and brightness are often correlated, and advanced compression techniques use that property, see "chroma from luma" in AV1. Without compression, indeed, it can be 3x, or even 4x (for alignment).
dTal 2y ago
> What about a pan sensor created by taking an RGB sensor and removing the filter so it's producing an image with 3x the resolution but in pan? This got me thinking about how subtle and slippery the concept of "resolution" is. The relationship is not strictly 3x; an ideally demosaiced Bayer filtered image still contains some information at the original capture resolution, because R, G, and B are correlated in nontrivial ways. Nyquist is a nice and simple result for the special case where nothing can be predicted about the original function, measurements are assumed to be perfect, and the criterion for reconstruction is "exact" - but in the general case of "recovering information, more is better" even a simple interpolation procedure with tame priors like "luminance exists" might perform so much better than the naive approach that we'd be silly not to use it. Even the Nyquist frequency itself is less fundamental than it first appears; a less well known result is that a function can also be perfectly recovered if sampled at half the Nyquist frequency, if the slope is captured as well as the value. In other words you can losslessly convert a 2MP image into a 1M value+slope data matrix and back. What resolution is it? Depends on your point of view.
- krapht 2y ago
  The same idea is used when doing complex IQ sampling of radio frequency data. I don't know if it's as obscure as you say, it's just not commonly considered when dealing with plain cameras. It's a bigger deal when working in the RF domain, or SAR imagery...

generalizations 2y ago

Yup. "Do boolean matrices take up less space than fp32 matrices?"

financltravsty 2y ago
Depends on compression ;)
- generalizations 2y ago
  Heh. Takes a scifi-level compression algorithm to make 32 bits comparable to 1 bit...or you'd need to get really lucky with your sparse matrix ;)
  - kbelder 2y ago
    But the other way around, it's all too common that a 1 bit array is stored equivalently to a 32 bit array.
    - generalizations 2y ago
      Yes, indeed. If a larger data type is used to represent the information, it will most certainly take up more space.

kadoban 2y ago

> In JPEG* This should end up being true for any presentation image format (the one you're going to actually show people, not necessarily the one you might do editing work on) optimized for human vision as well. It's more a property of our eyes than a quirk of JPEG. So I don't find it misleading personally.

AnarchismIsCool 2y ago
I'd argue compressed file formats make up a lot less of the broader problem space than most people realize. If you're doing anything high performance (where the difference matters) but not immediately consumer facing you're spending a lot of time working with lossless/uncompressed imagery in ram/disk. Stuff like MIPI->CUDA for classification, this question can go a number of different ways very quickly. Keep in mind, at least in my experience, if you're even remotely thinking about doing anything with grayscale and this question comes up you're probably doing something...interesting (read: multi band remote sensing shenanigans).

GuB-42 2y ago

This would be the case for any lossy format with decent compression. We are much less sensitive to color than we are to brightness so we can compress color much more than we can compress brightness for the same perceived quality. Not only that but color and brightness are often correlated, and advanced compression techniques use that property, see "chroma from luma" in AV1. Without compression, indeed, it can be 3x, or even 4x (for alignment).

dTal 2y ago

> What about a pan sensor created by taking an RGB sensor and removing the filter so it's producing an image with 3x the resolution but in pan? This got me thinking about how subtle and slippery the concept of "resolution" is. The relationship is not strictly 3x; an ideally demosaiced Bayer filtered image still contains some information at the original capture resolution, because R, G, and B are correlated in nontrivial ways. Nyquist is a nice and simple result for the special case where nothing can be predicted about the original function, measurements are assumed to be perfect, and the criterion for reconstruction is "exact" - but in the general case of "recovering information, more is better" even a simple interpolation procedure with tame priors like "luminance exists" might perform so much better than the naive approach that we'd be silly not to use it. Even the Nyquist frequency itself is less fundamental than it first appears; a less well known result is that a function can also be perfectly recovered if sampled at half the Nyquist frequency, if the slope is captured as well as the value. In other words you can losslessly convert a 2MP image into a 1M value+slope data matrix and back. What resolution is it? Depends on your point of view.

krapht 2y ago
The same idea is used when doing complex IQ sampling of radio frequency data. I don't know if it's as obscure as you say, it's just not commonly considered when dealing with plain cameras. It's a bigger deal when working in the RF domain, or SAR imagery...