6 ms·

What they did was super impressive, and is a fascinating way to figure out the optimal global compression settings. However, I got my hopes up that perceptual i

What they did was super impressive, and is a fascinating way to figure out the optimal global compression settings. However, I got my hopes up that perceptual image compression might be about computer vision. At scale, analyzing every image would obviously be prohibitively expensive, but it would be amazing to analyze the detail/edges or color usage (e.g. how much red) in a photo, and tune the q & chroma values accordingly. That, or compress with multiple settings and analyze the resulting images for noise introduced by artifacts.

hammock 11y ago
Love the approach. Then they showed their results, and it was only 606 "tests" (showings) of 250 images? On average, just a little more than one same and one different pair-showing for each image. Doesn't strike me as a huge sample.
- 336f5 11y ago
  "Doesn't strike me as a huge sample" Based on what criteria, exactly? 606 comparisons is more than enough to rule out large differences, especially considering that the testers were heavily primed to look for even the tiniest difference and making forced-choices about difference or no difference. Less than 1% difference suggests no real difference.
  - hammock 11y ago
    If it were 606 comparisons of one image I would agree. As it is, it's (on average) just ~2 comparisons of each image
    - 336f5 11y ago
      That doesn't matter if they're doing paired comparisons, as they are. 2 comparisons of a few hundred images yields far more reliable results than a few hundred comparisons of 2 images, because there are measurements both within and between images. An analogous situation: if someone runs a blood pressure clinical trial, whose results will you believe more - a trial which measures one person's blood pressure on and off a drug several hundred times over a year or two, or a trial which measures several hundred peoples' blood pressure at the beginning and end of the trial? Obviously the latter, because we know that there are big differences between people which must be measured if we want to make reliable predictions about the effect of the drug in the rest of the population, while additional blood pressure measurements of a person only reduces variability a little bit and helps only a little (because most of the sampling error was removed by the first pair of measurements, and further measurements leave the bulk of variance unaffected).

hammock 11y ago

Love the approach. Then they showed their results, and it was only 606 "tests" (showings) of 250 images? On average, just a little more than one same and one different pair-showing for each image. Doesn't strike me as a huge sample.

336f5 11y ago
"Doesn't strike me as a huge sample" Based on what criteria, exactly? 606 comparisons is more than enough to rule out large differences, especially considering that the testers were heavily primed to look for even the tiniest difference and making forced-choices about difference or no difference. Less than 1% difference suggests no real difference.
- hammock 11y ago
  If it were 606 comparisons of one image I would agree. As it is, it's (on average) just ~2 comparisons of each image
  - 336f5 11y ago
    That doesn't matter if they're doing paired comparisons, as they are. 2 comparisons of a few hundred images yields far more reliable results than a few hundred comparisons of 2 images, because there are measurements both within and between images. An analogous situation: if someone runs a blood pressure clinical trial, whose results will you believe more - a trial which measures one person's blood pressure on and off a drug several hundred times over a year or two, or a trial which measures several hundred peoples' blood pressure at the beginning and end of the trial? Obviously the latter, because we know that there are big differences between people which must be measured if we want to make reliable predictions about the effect of the drug in the rest of the population, while additional blood pressure measurements of a person only reduces variability a little bit and helps only a little (because most of the sampling error was removed by the first pair of measurements, and further measurements leave the bulk of variance unaffected).