3 ms·

Isn't lossy compression pretty much the same thing as extracting some amount of useful information?

• Retric 6y ago
  No, because lossy compression has the goal of preserving elements for replay. At the extreme you could turn a movie into subtitles, but not a costume from the movie alone. The costume might contain more information from the movie, but you can meaningfully recreate the movie from a single costume.
  • jhgb 6y ago
    If I derive Kepler's laws from positions of planets, I have preserved elements of their motion for replay as well.
    • Retric 6y ago
      The laws alone don’t have enough information to replay their motion without each planets location, a foci, the orbital period, and an associated timestamp or equivalent information. So, include that data and it’s lossy compression, exclude it and it’s not.
• teruakohatu 6y ago
  According to Wikipedia [data] compression "is the process of encoding information using fewer bits than the original representation" [1] while lossy compression is "the class of data encoding methods that uses inexact approximations and partial data discarding to represent the content" [2] There is a difference between feature extraction and compression. The former selects the most unique and important elements from a set of data, while the latter attempts, in the case of lossy compression, to find a smaller approximation of the original data set. For example imagine a data set of paintings. Feature extraction might simply identify what color paints were used in each painting. That alone might allow classification of paintings with respect to the painter, style or period. But this would not be an approximation of the original picture, unless you consider resizing a Jackson Pollock into an 8x1 pixel image to be compression. [1] https://en.wikipedia.org/wiki/Data_compression https://en.wikipedia.org/wiki/Data_compression [2] https://en.wikipedia.org/wiki/Lossy_compression https://en.wikipedia.org/wiki/Lossy_compression
  • miltondts 6y ago
    "The former selects the most unique and important elements from a set of data, while the latter attempts, in the case of lossy compression, to find a smaller approximation of the original data set." The compressor finds a smaller approximation of the data by finding the most redundant data, and as a byproduct it also finds the most unique. At least from the description you gave there is no difference between the two. "For example imagine a data set of paintings. Feature extraction might simply identify what color paints were used in each painting. That alone might allow classification of paintings with respect to the painter, style or period. But this would not be an approximation of the original picture, unless you consider resizing a Jackson Pollock into an 8x1 pixel image to be compression." You can and we do use compressors to do exactly that. Take a painting (or a set) as reference and use it to compress other paintings and the greater the compression the more similar the styles are. For an example see: https://www.sciencedirect.com/science/article/abs/pii/S0031320321000510 https://www.sciencedirect.com/science/article/abs/pii/S00313... EDIT: In fact one step in a compressor is feature extraction (usually called modelling) and the other is coding.