Messages in this thread | | | Date | Sun, 20 Dec 1998 19:54:14 -0500 | From | Raul Miller <> | Subject | Re: Article: IBM wants to "clean up the license" of Linux |
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Albert D. Cahalan <acahalan@cs.uml.edu> wrote: > Many kinds of perceptual compression algorithms for audio and video > are patented. They remove the data that humans don't notice, and get > far better compression.
This is an example of a patent I think the free software community could beat.
(1) Let's imagine that we had some sort of non-linear convolution which trimmed bits off the image (probably need a couple stages, so it's going to take a lot of work to design it). So, design a basic framework which has roughly the right kind of algorithmic properties, and characterize its parameter space.
(2) Build a big database of before/after compressed images, making sure that the "after" images are high quality. [Note that you can have more than one good "after" image.] Even better than a database would be "too slow but damned good" algorithm that we want to emulate, but I don't know that there is one.
(3) Run a search in the parameter space of your convolution framework for a decent transform. Tune it by hand if you can, but just throw massive parallelism at it (probably make it a genetic algorithm if you can come up with some kind of "this is good/this is bad" metric) to pick out a good compression scheme. [Example of how to parallelize: farm out image pairs to machines, then each machine a stream of parameters and have them pick the "best n".]
(4) Once you've gotten as far as you can with this system, start getting real people to look at before/after sets and pick out what they think is best, and use this as a [Use porn images and you'll have lots of volunteers.]
Convolution is old hat, non-linear algorithms are old hat, searching and genetic algorithms are old hat (so are neural net training algorithms, which I didn't mention but which might be useful here), and polling people to find out their preferences is old hat. And at no time are we using a "perceptual model" to design the transform -- we're using real perception.
This is just a sketch of an idea, and there's some serious room for improvement, but the underlying concept should work for any lossy compression algorithm.
-- Raul
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