Furthermore, those better results have the advantage of a much simpler model. This model has a fairly complicated architecture (a complex residual concatenation setup) and many more parameters (I would guess anywhere between 2x-10x as many, but I'd have to take a closer look), which means it's much slower to run and takes up more memory (disk and RAM).
I'd also say that in general the better model does things that are a lot more common sense: using the CIE Lab color space (perceptually uniform), omitting pooling, using a classification loss instead of regression (regression in generally performs poorly in deep learning), etc.
For comparability, I think it would be best if we could see outputs for the two models for the same, chosen in advance and not cherry picked, images.
What I imagine is full color input -> create B/W & color histogram (list of colors used) -> image viewer uses colorization algorithm to reapply colors.
I don't think a compression technique that would require that much processing power and have that little size reduction would be too useful.
Congrats, you wrote the first colorblind NN ever!
(Or you could just try to use a RNN directly and keep hidden state from frame to frame.)
The widely known Ben Hur (1959), the one with the chariot race, already is in colour. Did you mean "Ben-Hur: A Tale of the Christ" from 1925?
I'm interested to see if neural network parameters become the new "binary blob". While in theory you could always retrain the network yourself, actually doing so takes a lot of work fiddling with the network's hyperparameters and requires significant computing resources.
[1] http://www.robots.ox.ac.uk/~vgg/research/very_deep/
[2] "On a system equipped with four NVIDIA Titan Black GPUs, training a single net took 2–3 weeks depending on the architecture." - arXiv:1409.1556
In biometrics, there's been similar cases of software like face detectors and face recognition working very well on people from China and not very well for other people, because all the researchers who trained those models only had available large public databases from Chinese universities. The model hadn't seen any other ethnicity so its performance on "non-Chinese" folks wasn't surprising.
http://www.slashfilm.com/orangeblue-contrast-in-movie-poster...
I'd love to combine this technology with this: http://matplotlib.org/style_changes.html
You would probably have some cool results as you could generate examples of what they would look like to color blind people, and a corrected set so color blind people could see them.
Would be a cool, and I am assuming simpler problem then the one you have already managed to solve.
Good show, great work.
"But you didn't say what colour it was, so I made it a red truck."
Generally it's interesting to see nn thinking out missing details. I'd like to see images with an element deleted and a nn filling in black spots to see what level of shape recognition could do.
There is no way, from the greyscale, to know that the sky should be orange.
I've found this: [1], but the results seem somewhat disappointing. One of the problems is that the quality measures are (in my case) subjective (the results should look convincing but need not be "perfect", whatever that may mean).
[1] http://engineering.flipboard.com/2015/05/scaling-convnets/
Open source does not just mean you can see the source. From Wikipedia[0]:
"Open-source software is computer software with its source code made available with a license in which the copyright holder provides the rights to study, change, and distribute the software to anyone and for any purpose."
It's still interesting and cool to see! Just not what I thought it was when I clicked on the link.
Commonly JPEG separates the image into 1 channel of luma and 2 channels of chroma. It then downsamples the chroma to half the luma resolution, meaning that you have twice of much raw luma data as you have chroma.
It then goes on to do a whole load of fun discrete cosine transform, quantization and huffman encoding, but to a first approximation, I'd expect them to compress roughly similarly on average.
Since you've got twice as much luma data as chroma data, dropping the chroma will only save you ~30%.
Since the predictions from the neural network are not always accurate, you could then encode the error in the compressed file, and restore it on decompression. This will generally be close to 0, so compression should be pretty good.
At the end of the day, 30% better compression would be great... if the processing overheads aren't too great - I think that's the key factor.
(Most of this is from memory based on some hacking I did to be able to transcode videos to play on a cheap Chinese iPod Nano clone, which used an undocumented variant of MJPEG. The default quantization tables for luma and chroma are different from each other. The iPod Nano clone was using the standard quantization tables but the other way round (so using the luma table for chroma and the chroma table for luma). I can only imagine this was a bug in their code, as it was bound to reduce their compression ratio/image fidelity.)
In fact, looking closely at that definition, isn't practically nothing open source?
"Open-source software is computer software with its source code made available with a license in which the copyright holder provides the rights to study, change, and distribute the software to anyone and for any purpose."
I.e. public domain? Any other license lives precisely to limit those rights of distribution, no?
Anyway, just pedantry. I see your point. Learn something new every day.
Re: Open source vs Free software, this is a matter for debate (the tendency being too associate Free software with the more copyleft/viral/gnu-ish side of things), but I would definitely say that both involve explicit licencing, as opposed to potentially implicit " all rights reserved " style regional copyright.
The L part clarified the type of freedom, as the FL clarifies that it's not just OSS.
For such things it seems you'd need to check every shot change.
Even today, directors rarely seem to want to film in actual color. They'll tint everything sepia, or that hideous blue-orange scheme that is so popular these days.
http://priceonomics.com/why-every-movie-looks-sort-of-orange...
In any case, any such colorizing system would be designed to accept a bit of guidance here and there from the artist. This is much like when an OCR'd document needs a bit of touch-up.
And even if it wasn't perfect, many BW movies would be made much more watchable, like the 1927 Wings, which is crying out to be colorized (and have a soundtrack added).
Aren't we talking about a B&W film? In that case the colour (or its lack of) wouldn't communicate any information.
Now that's a horse of a different color[0]!
http://www.telegraph.co.uk/film/movie-news/paint-drying-bbfc...
It would have been funny to splice in a couple frames of porn throughout, Fight Club-style.