[FFmpeg-devel] GSoC 2018

[Pedro Arthur]

2018-01-13 23:32 GMT-02:00 Michael Niedermayer <michael at niedermayer.cc>:

> 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 ?
>
Comparing psnr the first NN method (SRCNN) achieves the same quality but
evaluation is faster than A+, or better quality at same speed.

Newer NN methods ([3], [4]) uses "perceptual loss" functions which degrades
the psnr but the images are much more sharp and appear to have better
quality than those that maximize psnr.


> 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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