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  • #71
    Originally posted by coder View Post
    I'd look at the data (i.e. PSNR or other measurements) and then decide whether the distinctions are significant. Also, I'm aware there are more sophisticated image quality metrics - I only use PSNR as an example.
    Actually PSNR seems like a decent metric to start with... but maybe I am underestimating how significant those decoding changes would be. In my head, I'm picturing rounding errors and different noise patterns at most.

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    • #72
      Originally posted by brucethemoose View Post
      In my head, I'm picturing rounding errors and different noise patterns at most.
      That's why a good informal test is to subtract the images, bias & normalize the result, and then look at it to see if the noise has clear patterns to it or any resemblance to the original image.

      If it were just rounding errors, then it should be pretty random. If there's real information loss, that's where you'd expect to see structure.

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