[FFmpeg-devel] GSoC 2018

Michael Niedermayer michael at niedermayer.cc
Sun Jan 14 03:32:35 EET 2018


On Fri, Jan 12, 2018 at 11:56:07AM -0200, Pedro Arthur wrote:
> 2018-01-12 0:06 GMT-02:00 Michael Niedermayer <michael at niedermayer.cc>:
> 
> > if pedro is up to date on this stuff, then maybe he wants to mentor this
> >
> > either way, links to relevant research, tests, literature are welcome
> >
> > I can mentor this.
> 
> One of the first NN based method was [1] which has a very simple network
> layout, only 3 convolution layers. More complex methods can be found in
> [2], [3], [4].

> The important question is where we are going to perfom only inference,
> using a pre-trained net or we will also train the net. The first is more
> easy to do but we don't exploit the content knowledge we have, the second
> is more powerful as it adapts to the content but requires training which
> may be  expensive, in this case it would be best to use some library to
> perform the training.

Iam sure our users would want to train the filter in some cases.
use cases for different types of content anime vs movies with actors for
example likely benefit from seperate training sets.

The training code could be seperate from the filter

Also another issue is the space requirements that result out of the training.
This was an issue with NNEDI previously IIRC


> 
> There are also method which does not use NN like A+ [5] and ANR.

How do these perform in relation to the latest NN based solutions ?

Also i think its a great project, you should definitly mentor this if it
interrests you


> 
> [1] - https://arxiv.org/abs/1501.00092
> [2] - https://arxiv.org/abs/1609.05158
> [3] - https://arxiv.org/abs/1603.08155
> [4] - https://arxiv.org/abs/1609.04802
> [5] -
> http://www.vision.ee.ethz.ch/~timofter/publications/Timofte-ACCV-2014.pdf
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-- 
Michael     GnuPG fingerprint: 9FF2128B147EF6730BADF133611EC787040B0FAB

Awnsering whenever a program halts or runs forever is
On a turing machine, in general impossible (turings halting problem).
On any real computer, always possible as a real computer has a finite number
of states N, and will either halt in less than N cycles or never halt.
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