CODEWITHSUNDEEP
image-processing
Cyclone
Cyclone

Reputation: 2133

good ways to preserve image information when reducing bit depth

I have some (millions) of 16-bit losslessly compressed TIFFs (about 2MB each) and after exhausting TB of disk space I think it's time I archive the older TIFFs as 8-bit JPEGs. Each individual image is a grayscale image, though there may be as many as 5 such images representing the same imaging area at different wavelengths. Now I want to preserve as much information as possible in this process, including the ability to restore the images to their approximate original values. I know there are ways to get further savings through spatial correlations across multiple channels, but the number of channels can vary, and it would be nice to be able to load channels independently.

The images themselves suggest some possible strategies to use since close to ~60% of the area in each image is dark 'background'. So one way to preserve more of the useful image range is just to threshold away anything below this 'background' before scaling and reducing the bit depth. This strategy is, of course, pretty subjective, and I'm looking for any other suggestions for strategies that are demonstrably superior and/or more general. Maybe something like trying to preserve the most image entropy?

Thanks.

Upvotes: 2

Views: 239

Answers (1)

mpenkov
mpenkov

Reputation: 21904

Your 2MB TIFFs are already losslessly compressed, so you would be hard-pressed to find a method that allows you to "restore the images" to their original value ranges without some loss of intensity detail.

So here are some questions to narrow down your problem a bit:

  • What are the image dimensions and number of channels? It's a bit difficult to guess from the filesize and bit depth alone, because as you've mentioned you're using lossless compression. A sample image would be good.
  • What sort of images are they? E.g. are they B/W blueprints, X-ray/MRI images, color photographs. You mention that around 60% of the images is "background" -- could you tell us more about the image content?
  • What are they used for? Is it just for a human viewer, or are they training images for some computer algorithm?
  • What kind of coding efficiency are you expecting? E.g. for the current 2MB filesize, how small do you want your compressed files to be?

Based on that information, people may be able to suggest something. For example, if your images are just color photographs that people will look at, 4:2:0 chroma subsampling will give you a 50% reduction in space without any visually detectable quality loss. You may even be able to keep your 16-bit image depth, if the reduction is sufficient.

Finally, note that you've compared two fundamentally different things in your question:

  1. "top ~40% of the pixels" -- here it sounds like you're talking about contiguous parts of the intensity spectrum (e.g. intensities from 0.6 to 1.0) -- essentially the probability density function of the image.
  2. "close to ~60% of the area in each image" -- here you're talking about the distribution of pixels in the spatial domain.

In general, these two things are unrelated and comparing them is meaningless. There may be an exception for specific image content -- please put up a representative image to make it obvious what you're dealing with.

If you edit your question, I'll have a look and reply if I think of something.

Upvotes: 2

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