11–20 of 51 posts
What I want to see is an acceptable looking JPG next to a WaveOne image of the same size. Or an acceptable looking WaveOne next to a JPG of the same size.
How small is good enough? How good is small enough?
So is this only good for ridiculously low target filesizes? Noone in their right mind is going to compress a "480x480 image to a file size of 2.3kB" What I want to see is an acceptable looking JPG next to a WaveOne image of the same size. Or an acceptable looking WaveOne next to a JPG of the same size. How small is good enough? How good is small enough?
Interesting that decoding is slower than encoding. Also curious about performance on CPU.
This approach may also be susceptible to "hallucinating" inaccurate detail; you can see a little bit of this on the upper-right of the girl's circled eyelid compared to the original Kodak image. See also: http://www.dkriesel.com/en/blog/2013/0802_xerox-workcentres_...
This is great news!
I'd actually like to see the plot, though. (Both for encoding and decoding.) It stands to reason that a neural network can optimize image compression, as it can encode high-level information like "this is a face". But encoding / decoding speed is the sticking point, so I feel successes there should be emphasized.
The necessity of having a GPU doesn't seem problematic nowadays; everything has one. Testing it with a mobile-grade GPU would be interesting.
> Lubomir holds a Ph.D. from UC Berkeley, 20 years of professional experience, 50+ issued patents and 5000+ citations.
I just hope this type of research isn't going to end in a patent encumbrance, like it did with JPEG and MPEG.
These techniques are right around the corner, no matter who invents the file formats.
So if their idea is to lock these general ideas down with more patents, I'd want them to stop their research and let people with more open intentions research this further.
I wonder how this compares to FLIF. I also tried to compress images based on shape and structure but by approximating these using skeletons. I'm just a bit struggling with their performance comparison. The graphs they present are very pretty and promising but for the presented images we're quite left in the dark. They dump some images and theirs looks prettier and the authors give us some indication of quality but it…
Seems like a slightly unfair comparison. Training the compressor moves data from the images into the compressor, making the bit per pixel evaluation slightly more iffy.
As long as the decompressor needs just an image file and no other data, it's a fair game.
[1] https://static1.squarespace.com/static/57c8be4459cc68c3e3d7b...
[2] https://static1.squarespace.com/static/57c8be4459cc68c3e3d7b...
I wonder how this compares to FLIF. I also tried to compress images based on shape and structure but by approximating these using skeletons. I'm just a bit struggling with their performance comparison. The graphs they present are very pretty and promising but for the presented images we're quite left in the dark. They dump some images and theirs looks prettier and the authors give us some indication of quality but it…
